Chapter 22 ANOVA
2.0
22.1 Introduction
ANOVA stands for “Analysis of Variance”. In this chapter, we will study the most basic form of ANOVA, called “one-way ANOVA”. We’ve already considered the one-sample and two-sample t tests for means. ANOVA is what you do when you want to compare means for three or more groups.
22.1.1 Install new packages
If you are using R and RStudio on your own machine instead of accessing RStudio Workbench through a browser, you’ll need to type the following command at the Console:
install.packages("quantreg")22.1.2 Download the R notebook file
Check the upper-right corner in RStudio to make sure you’re in your intro_stats project. Then click on the following link to download this chapter as an R notebook file (.Rmd).
Once the file is downloaded, move it to your project folder in RStudio and open it there.
22.2 Load packages
We load the standard tidyverse, janitor, and infer packages. The quantreg package contains the uis data (which must be explicitly loaded using the data command) and the palmerpenguins package for the penguins data.
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.0 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.test## Loading required package: SparseM
##
## Attaching package: 'SparseM'
##
## The following object is masked from 'package:base':
##
## backsolve22.3 Research question
The uis data set from the quantreg package contains data from the UIS Drug Treatment Study. Is a history of IV drug use associated with depression?
Exercise 1
The help file for the uis data is particularly uninformative. The source, like so many we see in R packages, is a statistics textbook. If you happen to have access to a copy of the textbook, it’s pretty easy to look it up and see what the authors say about it. But it’s not likely you have such access.
See if you can find out more about where the data came from. This is tricky and you’re going have to dig deep.
Hint #1: Your first hits will be from the University of Illinois-Springfield. That is not the correct source.
Hint #2: You may have more success finding sources that quote from the textbook and mention more detail about the data as it’s explained in the textbook. In fact, you might even stumble across actual pages from the textbook with the direct explanation, but that is much harder. You should not try to find and download PDF files of the book itself. Not only is that illegal, but it might also come along with nasty computer viruses.
Please write up your answer here.
22.4 Data preparation and exploration
Let’s look at the UIS data:
## ID AGE BECK HC IV NDT RACE TREAT SITE LEN.T TIME CENSOR Y
## 1 1 39 9.000 4 3 1 0 1 0 123 188 1 5.236442
## 2 2 33 34.000 4 2 8 0 1 0 25 26 1 3.258097
## 3 3 33 10.000 2 3 3 0 1 0 7 207 1 5.332719
## 4 4 32 20.000 4 3 1 0 0 0 66 144 1 4.969813
## 5 5 24 5.000 2 1 5 1 1 0 173 551 0 6.311735
## 6 6 30 32.550 3 3 1 0 1 0 16 32 1 3.465736
## 7 7 39 19.000 4 3 34 0 1 0 179 459 1 6.129050
## 8 8 27 10.000 4 3 2 0 1 0 21 22 1 3.091042
## 9 9 40 29.000 2 3 3 0 1 0 176 210 1 5.347108
## 10 10 36 25.000 2 3 7 0 1 0 124 184 1 5.214936
## 11 12 38 18.900 2 3 8 0 1 0 176 212 1 5.356586
## 12 13 29 16.000 3 1 1 0 1 0 79 87 1 4.465908
## 13 14 32 36.000 3 3 2 1 1 0 182 598 0 6.393591
## 14 15 41 19.000 1 3 8 0 1 0 174 260 1 5.560682
## 15 16 31 18.000 1 3 1 0 1 0 181 210 1 5.347108
## 16 17 27 12.000 2 3 3 0 1 0 61 84 1 4.430817
## 17 18 28 34.000 1 3 6 0 1 0 177 196 1 5.278115
## 18 19 28 23.000 4 2 1 0 1 0 19 19 1 2.944439
## 19 20 36 26.000 3 1 15 1 1 0 27 441 1 6.089045
## 20 21 32 18.900 2 3 5 0 1 0 175 449 1 6.107023
## 21 22 33 15.000 3 1 1 0 0 0 12 659 0 6.490724
## 22 23 28 25.200 1 3 8 0 0 0 21 21 1 3.044522
## 23 24 29 6.632 4 2 0 0 0 0 48 53 1 3.970292
## 24 25 35 2.100 2 3 9 0 0 0 90 225 1 5.416100
## 25 26 45 26.000 1 3 6 0 0 0 91 161 1 5.081404
## 26 27 35 39.789 4 3 5 0 0 0 87 87 1 4.465908
## 27 28 24 20.000 3 1 3 0 0 0 88 89 1 4.488636
## 28 29 36 16.000 1 3 7 0 0 0 9 44 1 3.784190
## 29 31 39 22.000 1 3 9 0 0 0 94 523 0 6.259581
## 30 32 36 9.947 4 2 10 0 0 0 91 226 1 5.420535
## 31 33 37 9.450 4 3 1 0 0 0 90 259 1 5.556828
## 32 34 30 39.000 2 3 1 0 0 0 89 289 1 5.666427
## 33 35 44 41.000 1 3 5 0 0 0 89 103 1 4.634729
## 34 36 28 31.000 3 1 6 1 0 0 100 624 0 6.436150
## 35 37 25 20.000 3 1 3 1 0 0 67 68 1 4.219508
## 36 38 30 8.000 2 3 7 0 1 0 25 57 1 4.043051
## 37 39 24 9.000 4 1 1 0 0 0 12 65 1 4.174387
## 38 40 27 20.000 3 1 1 0 0 0 79 79 1 4.369448
## 39 41 30 8.000 3 1 2 1 0 0 79 559 0 6.326149
## 40 42 34 8.000 2 3 0 0 1 0 78 79 1 4.369448
## 41 43 33 23.000 4 2 2 0 1 0 84 87 1 4.465908
## 42 44 34 18.000 3 3 6 0 1 0 91 91 1 4.510860
## 43 45 36 13.000 2 3 1 0 1 0 162 297 1 5.693732
## 44 46 27 23.000 1 3 0 0 1 0 45 45 1 3.806662
## 45 47 35 9.000 4 3 1 1 1 0 61 246 1 5.505332
## 46 48 24 14.000 1 3 0 0 1 0 19 37 1 3.610918
## 47 49 28 23.000 4 1 2 1 1 0 37 37 1 3.610918
## 48 50 46 10.000 1 3 8 0 1 0 51 538 0 6.287859
## 49 51 26 11.000 3 3 1 0 1 0 60 541 0 6.293419
## 50 52 42 16.000 1 3 25 0 1 0 177 184 1 5.214936
## 51 53 30 0.000 3 1 0 0 1 0 43 122 1 4.804021
## 52 55 30 12.000 4 1 3 1 1 0 21 156 1 5.049856
## 53 56 27 21.000 2 3 2 0 0 0 88 121 1 4.795791
## 54 57 38 0.000 1 3 6 0 0 0 96 231 1 5.442418
## 55 58 48 8.000 4 3 10 0 0 0 111 111 1 4.709530
## 56 59 36 25.000 1 3 10 0 0 0 38 38 1 3.637586
## 57 60 28 6.300 3 1 7 0 0 0 15 15 1 2.708050
## 58 61 31 20.000 4 2 5 0 0 0 50 54 1 3.988984
## 59 62 28 4.000 2 3 5 0 0 0 61 127 1 4.844187
## 60 63 28 20.000 3 1 1 0 0 0 31 105 1 4.653960
## 61 64 26 17.000 2 1 2 1 0 0 11 11 1 2.397895
## 62 65 34 3.000 4 3 6 0 0 0 90 153 1 5.030438
## 63 66 26 29.000 2 3 5 0 0 0 11 11 1 2.397895
## 64 68 31 26.000 1 3 5 0 0 0 46 46 1 3.828641
## 65 69 41 12.000 1 3 0 1 0 0 38 655 0 6.484635
## 66 70 30 24.000 4 3 0 0 0 0 90 166 1 5.111988
## 67 72 39 15.750 4 3 5 0 0 0 88 95 1 4.553877
## 68 74 33 9.000 2 3 12 0 0 0 91 151 1 5.017280
## 69 75 33 18.000 4 2 6 0 0 0 85 220 1 5.393628
## 70 76 29 20.000 4 1 0 1 0 0 90 227 1 5.424950
## 71 77 36 17.000 1 3 5 0 0 0 52 343 1 5.837730
## 72 78 26 3.000 4 3 3 0 0 0 88 119 1 4.779123
## 73 79 37 27.000 1 3 13 0 0 0 43 43 1 3.761200
## 74 81 29 31.500 1 3 8 0 0 0 37 47 1 3.850148
## 75 83 30 19.000 3 1 0 1 0 0 87 805 0 6.690842
## 76 84 35 15.000 3 2 2 0 0 0 20 321 1 5.771441
## 77 85 33 22.000 3 1 1 0 0 0 9 167 1 5.117994
## 78 87 36 16.000 2 3 1 0 0 0 85 491 1 6.196444
## 79 88 28 17.000 1 3 2 0 0 0 18 35 1 3.555348
## 80 89 31 32.550 1 3 12 1 0 0 71 123 1 4.812184
## 81 90 23 24.000 1 3 2 0 0 0 88 597 0 6.391917
## 82 91 33 22.000 3 2 1 0 0 0 67 762 0 6.635947
## 83 93 37 18.000 2 3 4 0 0 0 30 31 1 3.433987
## 84 94 25 17.850 3 1 1 0 1 0 68 228 1 5.429346
## 85 95 56 5.000 2 2 9 1 1 0 182 553 0 6.315358
## 86 96 23 39.000 1 3 1 0 1 0 182 190 1 5.247024
## 87 97 26 21.000 3 1 1 0 1 0 146 307 1 5.726848
## 88 98 26 11.000 1 3 1 0 1 0 40 73 1 4.290459
## 89 99 23 14.000 3 1 1 0 1 0 177 208 1 5.337538
## 90 100 28 31.000 4 2 2 1 1 0 181 267 1 5.587249
## 91 102 30 14.000 1 3 15 0 1 0 168 169 1 5.129899
## 92 104 25 6.000 2 3 5 0 1 0 90 655 0 6.484635
## 93 105 33 16.000 1 3 5 0 1 0 61 70 1 4.248495
## 94 106 22 6.000 3 1 3 1 1 0 63 398 1 5.986452
## 95 108 25 20.000 4 2 8 1 1 0 121 122 1 4.804021
## 96 111 38 9.000 3 1 1 1 0 0 89 96 1 4.564348
## 97 112 35 11.000 2 1 3 0 1 0 51 1172 0 7.066467
## 98 113 35 15.000 3 1 1 0 0 0 88 734 0 6.598509
## 99 114 25 13.000 3 3 1 0 0 0 25 26 1 3.258097
## 100 115 33 31.000 3 1 3 1 0 0 83 84 1 4.430817
## 101 116 30 5.000 3 1 2 1 0 0 89 171 1 5.141664
## 102 117 45 10.000 2 3 1 0 0 0 24 159 1 5.068904
## 103 119 42 23.000 2 3 20 0 0 0 7 7 1 1.945910
## 104 120 29 16.000 4 1 1 1 0 0 85 763 0 6.637258
## 105 121 24 37.800 3 1 0 0 0 0 89 104 1 4.644391
## 106 122 33 10.000 2 3 4 0 0 0 91 162 1 5.087596
## 107 123 32 9.000 3 1 0 0 0 0 89 90 1 4.499810
## 108 124 26 15.000 3 1 0 0 0 0 82 373 1 5.921578
## 109 125 28 2.000 1 3 3 0 0 0 84 115 1 4.744932
## 110 127 37 34.000 2 3 1 0 0 0 30 30 1 3.401197
## 111 128 23 11.000 4 1 6 0 0 0 7 8 1 2.079442
## 112 129 40 31.000 2 3 3 1 0 0 84 168 1 5.123964
## 113 130 36 36.750 3 3 0 0 0 0 70 70 1 4.248495
## 114 131 23 26.000 3 2 2 0 0 0 76 130 1 4.867534
## 115 132 35 5.000 4 1 1 1 0 0 89 285 1 5.652489
## 116 133 25 19.000 2 3 1 0 1 0 178 569 0 6.343880
## 117 134 35 21.000 2 3 6 0 1 0 87 87 1 4.465908
## 118 135 46 1.000 4 2 0 0 1 0 175 310 1 5.736572
## 119 136 32 6.000 4 1 3 0 1 0 87 87 1 4.465908
## 120 137 35 23.000 3 1 16 1 1 0 110 544 0 6.298949
## 121 138 34 38.000 3 3 1 0 1 0 21 156 1 5.049856
## 122 139 43 24.000 3 1 3 0 1 0 139 658 0 6.489205
## 123 140 39 3.000 4 3 15 0 1 0 181 273 1 5.609472
## 124 141 27 16.800 4 3 2 1 1 0 33 168 1 5.123964
## 125 142 38 35.000 1 3 1 0 1 0 39 83 1 4.418841
## 126 143 37 11.000 2 3 7 0 1 0 4 4 1 1.386294
## 127 144 44 2.000 1 3 4 1 1 0 184 708 0 6.562444
## 128 145 25 16.000 4 1 1 1 1 0 123 137 1 4.919981
## 129 146 34 15.000 3 1 1 0 1 0 176 259 1 5.556828
## 130 147 34 11.000 3 3 2 1 1 0 174 560 0 6.327937
## 131 148 38 11.000 1 3 1 1 1 0 181 586 0 6.373320
## 132 149 24 22.000 2 3 2 1 1 0 113 190 1 5.247024
## 133 151 42 18.000 2 3 3 0 1 0 164 544 0 6.298949
## 134 153 34 29.000 4 3 1 1 0 0 84 494 1 6.202536
## 135 154 45 27.000 1 3 8 0 0 0 80 541 0 6.293419
## 136 155 40 16.000 2 3 4 0 0 0 91 94 1 4.543295
## 137 156 27 9.000 4 1 3 1 0 0 97 567 0 6.340359
## 138 157 24 0.000 4 1 3 0 0 0 51 55 1 4.007333
## 139 158 27 15.000 1 3 3 0 0 0 91 93 1 4.532599
## 140 159 34 24.000 3 1 4 0 0 0 90 276 1 5.620401
## 141 160 36 3.000 2 3 6 0 0 0 46 46 1 3.828641
## 142 162 31 9.000 3 1 1 0 0 0 76 250 1 5.521461
## 143 163 40 5.000 2 3 2 0 0 0 75 106 1 4.663439
## 144 164 40 13.000 1 3 4 1 0 0 91 552 0 6.313548
## 145 165 37 29.000 2 3 5 0 0 0 90 90 1 4.499810
## 146 166 25 11.000 4 3 6 0 0 0 3 203 1 5.313206
## 147 167 41 22.000 2 3 3 1 1 0 8 67 1 4.204693
## 148 168 22 9.000 4 1 1 0 1 0 33 559 1 6.326149
## 149 169 31 18.000 2 3 8 1 1 0 31 106 1 4.663439
## 150 170 29 40.000 1 1 1 1 1 0 174 374 1 5.924256
## 151 171 27 25.000 3 1 2 0 1 0 34 630 0 6.445720
## 152 172 22 26.000 4 2 3 0 1 0 60 61 1 4.110874
## 153 174 37 11.000 1 2 5 1 1 0 78 547 0 6.304449
## 154 175 36 6.000 3 1 2 1 1 0 182 568 0 6.342121
## 155 176 24 20.000 3 1 1 0 1 0 182 490 1 6.194405
## 156 177 28 9.000 4 1 0 1 1 0 78 222 1 5.402677
## 157 178 24 6.000 4 1 1 0 1 0 55 56 1 4.025352
## 158 179 28 0.000 3 1 2 0 1 0 223 282 1 5.641907
## 159 180 24 5.000 3 1 20 1 1 0 25 35 1 3.555348
## 160 181 24 15.000 4 1 0 0 1 0 63 603 0 6.401917
## 161 183 29 14.700 3 1 1 0 1 0 133 148 1 4.997212
## 162 184 37 3.000 1 3 5 1 1 0 154 354 1 5.869297
## 163 185 26 31.000 1 1 2 0 1 0 70 164 1 5.099866
## 164 186 29 14.000 3 2 1 0 1 0 66 94 1 4.543295
## 165 187 29 28.000 2 3 4 0 1 0 40 65 1 4.174387
## 166 188 33 18.000 4 1 1 0 1 0 75 567 0 6.340359
## 167 189 29 12.000 4 2 2 0 1 0 187 634 0 6.452049
## 168 190 32 5.000 1 1 2 1 1 0 183 633 0 6.450470
## 169 192 33 11.000 4 1 8 1 1 0 182 477 1 6.167516
## 170 193 26 21.000 4 2 2 0 1 0 192 436 1 6.077642
## 171 195 24 23.000 2 3 4 1 1 0 162 362 1 5.891644
## 172 196 46 32.000 2 3 2 0 1 0 193 552 0 6.313548
## 173 197 23 26.000 4 1 2 0 1 0 111 144 1 4.969813
## 174 198 40 19.950 4 3 8 0 1 0 182 242 1 5.488938
## 175 199 48 17.000 3 1 4 0 1 0 180 564 0 6.335054
## 176 200 33 16.000 3 1 0 0 1 0 93 299 1 5.700444
## 177 201 21 26.250 4 1 7 0 1 0 167 167 1 5.117994
## 178 202 38 29.000 3 1 2 0 1 0 196 380 1 5.940171
## 179 203 28 23.000 4 2 4 0 1 0 106 120 1 4.787492
## 180 205 39 9.000 1 3 6 0 1 0 158 218 1 5.384495
## 181 206 37 26.000 1 2 1 1 0 0 91 115 1 4.744932
## 182 207 32 22.000 3 1 4 1 0 0 89 224 1 5.411646
## 183 208 39 23.000 3 2 2 1 0 0 89 132 1 4.882802
## 184 209 28 0.000 1 3 10 0 0 0 88 148 1 4.997212
## 185 210 26 30.000 3 1 0 1 0 0 95 593 0 6.385194
## 186 211 31 21.000 1 3 0 0 0 0 5 26 1 3.258097
## 187 213 34 19.000 4 3 8 0 0 0 32 32 1 3.465736
## 188 214 26 28.000 4 2 2 1 0 0 92 292 1 5.676754
## 189 215 29 8.000 4 1 3 0 0 0 66 89 1 4.488636
## 190 217 25 11.000 3 1 8 0 0 0 90 364 1 5.897154
## 191 218 34 15.000 3 2 3 1 0 0 93 142 1 4.955827
## 192 219 32 8.000 3 1 2 0 0 0 89 188 1 5.236442
## 193 221 38 14.000 4 2 0 0 0 0 91 92 1 4.521789
## 194 222 32 7.000 1 3 8 0 0 0 56 56 1 4.025352
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## 213 242 34 0.000 3 1 7 0 1 0 173 560 0 6.327937
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## 228 257 43 19.000 3 2 2 1 1 0 174 175 1 5.164786
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## 278 310 37 29.000 1 3 20 0 0 0 23 23 1 3.135494
## 279 311 37 22.000 2 3 20 0 0 0 26 26 1 3.258097
## 280 312 40 12.000 4 2 9 0 0 0 84 98 1 4.584967
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## 282 314 40 15.000 1 1 2 0 0 0 86 555 0 6.318968
## 283 315 40 3.000 1 3 4 1 0 0 90 290 1 5.669881
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## 285 317 41 18.000 2 3 7 0 0 0 76 274 1 5.613128
## 286 321 23 2.000 4 1 1 0 1 0 18 119 1 4.779123
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## 486 534 31 13.000 4 1 0 1 1 1 116 130 1 4.867534
## 487 536 39 7.000 3 3 3 1 0 1 88 159 1 5.068904
## 488 538 33 14.000 3 1 1 0 0 1 33 33 1 3.496508
## 489 539 27 10.000 3 3 2 0 1 1 70 72 1 4.276666
## 490 540 37 7.000 4 1 2 1 1 1 68 161 1 5.081404
## 491 541 35 16.000 4 2 25 0 0 1 191 191 1 5.252273
## 492 542 25 11.000 3 1 5 0 0 1 35 181 1 5.198497
## 493 543 27 11.000 3 1 1 1 1 1 32 546 0 6.302619
## 494 544 34 15.000 4 1 0 0 0 1 28 540 0 6.291569
## 495 545 30 15.000 3 1 3 0 0 1 15 76 1 4.330733
## 496 546 35 17.000 1 3 7 0 0 1 7 7 1 1.945910
## 497 547 34 23.000 4 1 0 0 0 1 43 44 1 3.784190
## 498 548 25 23.000 3 2 5 0 0 1 89 103 1 4.634729
## 499 549 34 18.000 3 1 1 0 0 1 38 79 1 4.369448
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## 501 551 24 20.000 4 1 2 0 0 1 76 90 1 4.499810
## 502 552 40 36.000 4 1 3 0 0 1 195 542 0 6.295266
## 503 553 33 9.000 3 1 1 1 0 1 184 384 1 5.950643
## 504 554 38 14.000 4 2 1 1 1 1 254 255 1 5.541264
## 505 555 32 1.000 3 1 0 0 1 1 371 431 1 6.066108
## 506 556 33 3.000 4 1 1 0 0 1 196 587 0 6.375025
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## 514 564 40 14.700 2 3 4 0 1 1 34 34 1 3.526361
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## 516 567 30 11.000 3 2 7 0 0 1 133 133 1 4.890349
## 517 568 36 0.000 3 2 3 0 0 1 69 226 1 5.420535
## 518 569 38 17.000 2 3 6 0 1 1 366 401 1 5.993961
## 519 570 31 20.000 1 3 6 1 1 1 14 14 1 2.639057
## 520 571 27 22.000 2 2 2 0 0 1 184 548 0 6.306275
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## 522 573 35 23.000 3 1 5 1 0 1 183 540 0 6.291569
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## 528 579 32 10.000 1 3 6 0 1 1 360 360 1 5.886104
## 529 580 29 15.750 4 1 2 0 0 1 79 139 1 4.934474
## 530 581 40 2.000 2 2 5 0 1 1 201 215 1 5.370638
## 531 582 27 9.000 4 2 0 0 1 1 129 129 1 4.859812
## 532 583 26 2.000 3 1 1 0 1 1 365 396 1 5.981414
## 533 584 34 15.000 3 1 4 1 1 1 159 547 0 6.304449
## 534 585 49 4.000 4 2 2 0 0 1 177 547 0 6.304449
## 535 586 21 25.000 1 3 1 0 1 1 71 71 1 4.262680
## 536 587 39 23.000 3 3 2 0 1 1 108 168 1 5.123964
## 537 588 33 15.000 4 2 4 0 1 1 198 228 1 5.429346
## 538 589 32 3.000 3 1 1 0 1 1 372 551 0 6.311735
## 539 590 35 9.000 4 2 6 0 0 1 25 654 0 6.483107
## 540 591 31 20.000 4 1 0 1 1 1 48 51 1 3.931826
## 541 592 28 5.000 4 1 3 0 0 1 191 548 0 6.306275
## 542 593 27 29.000 3 2 5 0 1 1 171 231 1 5.442418
## 543 594 29 21.000 2 1 1 1 1 1 145 280 1 5.634790
## 544 595 30 1.000 2 1 20 0 0 1 183 184 1 5.214936
## 545 596 27 18.000 4 1 3 1 0 1 72 86 1 4.454347
## 546 598 40 15.000 4 2 1 0 1 1 44 46 1 3.828641
## 547 599 37 20.000 3 1 2 1 1 1 140 200 1 5.298317
## 548 600 33 10.000 4 1 0 0 0 1 184 244 1 5.497168
## 549 601 28 20.000 4 1 2 0 0 1 94 182 1 5.204007
## 550 602 40 15.000 4 2 8 0 1 1 296 296 1 5.690359
## 551 603 48 20.000 4 1 0 1 0 1 23 24 1 3.178054
## 552 604 38 25.000 3 1 1 0 0 1 128 142 1 4.955827
## 553 605 35 13.000 4 1 0 0 0 1 106 120 1 4.787492
## 554 606 37 13.000 4 2 0 0 0 1 46 47 1 3.850148
## 555 607 25 15.000 3 1 0 1 1 1 150 519 1 6.251904
## 556 608 26 8.000 4 1 2 0 1 1 48 248 1 5.513429
## 557 609 30 9.000 3 3 3 0 0 1 29 31 1 3.433987
## 558 610 28 16.000 4 2 2 0 0 1 179 567 0 6.340359
## 559 611 23 11.000 2 3 4 0 0 1 170 353 1 5.866468
## 560 612 36 31.000 4 1 1 0 1 1 365 458 1 6.126869
## 561 613 36 13.000 4 2 4 0 1 1 400 554 0 6.317165
## 562 614 24 5.000 4 1 0 1 0 1 56 116 1 4.753590
## 563 615 33 9.000 3 2 5 0 0 1 24 74 1 4.304065
## 564 616 38 15.000 4 2 6 0 0 1 10 10 1 2.302585
## 565 617 41 20.000 3 3 21 0 1 1 354 355 1 5.872118
## 566 618 31 21.000 3 1 0 1 1 1 232 232 1 5.446737
## 567 619 31 23.000 4 2 11 0 1 1 54 68 1 4.219508
## 568 620 37 5.000 4 1 0 1 1 1 48 48 1 3.871201
## 569 621 37 17.000 4 2 4 1 0 1 57 60 1 4.094345
## 570 622 33 13.000 4 1 0 0 0 1 46 50 1 3.912023
## 571 624 53 9.000 4 2 6 0 0 1 39 126 1 4.836282
## 572 625 37 20.000 2 3 4 0 0 1 17 18 1 2.890372
## 573 626 28 10.000 4 2 3 0 1 1 21 35 1 3.555348
## 574 627 35 17.000 1 3 2 0 0 1 184 379 1 5.937536
## 575 628 46 31.500 1 3 15 1 1 1 9 377 1 5.932245
## ND1 ND2 LNDT FRAC IV3
## 1 5.0000000 -8.04718956 0.6931472 0.68333333 1
## 2 1.1111111 -0.11706724 2.1972246 0.13888889 0
## 3 2.5000000 -2.29072683 1.3862944 0.03888889 1
## 4 5.0000000 -8.04718956 0.6931472 0.73333333 1
## 5 1.6666667 -0.85137604 1.7917595 0.96111111 0
## 6 5.0000000 -8.04718956 0.6931472 0.08888889 1
## 7 0.2857143 0.35793228 3.5553481 0.99444444 1
## 8 3.3333333 -4.01324268 1.0986123 0.11666667 1
## 9 2.5000000 -2.29072683 1.3862944 0.97777778 1
## 10 1.2500000 -0.27892944 2.0794415 0.68888889 1
## 11 1.1111111 -0.11706724 2.1972246 0.97777778 1
## 12 5.0000000 -8.04718956 0.6931472 0.43888889 0
## 13 3.3333333 -4.01324268 1.0986123 1.01111111 1
## 14 1.1111111 -0.11706724 2.1972246 0.96666667 1
## 15 5.0000000 -8.04718956 0.6931472 1.00555556 1
## 16 2.5000000 -2.29072683 1.3862944 0.33888889 1
## 17 1.4285714 -0.50953563 1.9459101 0.98333333 1
## 18 5.0000000 -8.04718956 0.6931472 0.10555556 0
## 19 0.6250000 0.29375227 2.7725887 0.15000000 0
## 20 1.6666667 -0.85137604 1.7917595 0.97222222 1
## 21 5.0000000 -8.04718956 0.6931472 0.13333333 0
## 22 1.1111111 -0.11706724 2.1972246 0.23333333 1
## 23 10.0000000 -23.02585093 0.0000000 0.53333333 0
## 24 1.0000000 0.00000000 2.3025851 1.00000000 1
## 25 1.4285714 -0.50953563 1.9459101 1.01111111 1
## 26 1.6666667 -0.85137604 1.7917595 0.96666667 1
## 27 2.5000000 -2.29072683 1.3862944 0.97777778 0
## 28 1.2500000 -0.27892944 2.0794415 0.10000000 1
## 29 1.0000000 0.00000000 2.3025851 1.04444444 1
## 30 0.9090909 0.08664562 2.3978953 1.01111111 0
## 31 5.0000000 -8.04718956 0.6931472 1.00000000 1
## 32 5.0000000 -8.04718956 0.6931472 0.98888889 1
## 33 1.6666667 -0.85137604 1.7917595 0.98888889 1
## 34 1.4285714 -0.50953563 1.9459101 1.11111111 0
## 35 2.5000000 -2.29072683 1.3862944 0.74444444 0
## 36 1.2500000 -0.27892944 2.0794415 0.13888889 1
## 37 5.0000000 -8.04718956 0.6931472 0.13333333 0
## 38 5.0000000 -8.04718956 0.6931472 0.87777778 0
## 39 3.3333333 -4.01324268 1.0986123 0.87777778 0
## 40 10.0000000 -23.02585093 0.0000000 0.43333333 1
## 41 3.3333333 -4.01324268 1.0986123 0.46666667 0
## 42 1.4285714 -0.50953563 1.9459101 0.50555556 1
## 43 5.0000000 -8.04718956 0.6931472 0.90000000 1
## 44 10.0000000 -23.02585093 0.0000000 0.25000000 1
## 45 5.0000000 -8.04718956 0.6931472 0.33888889 1
## 46 10.0000000 -23.02585093 0.0000000 0.10555556 1
## 47 3.3333333 -4.01324268 1.0986123 0.20555556 0
## 48 1.1111111 -0.11706724 2.1972246 0.28333333 1
## 49 5.0000000 -8.04718956 0.6931472 0.33333333 1
## 50 0.3846154 0.36750440 3.2580965 0.98333333 1
## 51 10.0000000 -23.02585093 0.0000000 0.23888889 0
## 52 2.5000000 -2.29072683 1.3862944 0.11666667 0
## 53 3.3333333 -4.01324268 1.0986123 0.97777778 1
## 54 1.4285714 -0.50953563 1.9459101 1.06666667 1
## 55 0.9090909 0.08664562 2.3978953 1.23333333 1
## 56 0.9090909 0.08664562 2.3978953 0.42222222 1
## 57 1.2500000 -0.27892944 2.0794415 0.16666667 0
## 58 1.6666667 -0.85137604 1.7917595 0.55555556 0
## 59 1.6666667 -0.85137604 1.7917595 0.67777778 1
## 60 5.0000000 -8.04718956 0.6931472 0.34444444 0
## 61 3.3333333 -4.01324268 1.0986123 0.12222222 0
## 62 1.4285714 -0.50953563 1.9459101 1.00000000 1
## 63 1.6666667 -0.85137604 1.7917595 0.12222222 1
## 64 1.6666667 -0.85137604 1.7917595 0.51111111 1
## 65 10.0000000 -23.02585093 0.0000000 0.42222222 1
## 66 10.0000000 -23.02585093 0.0000000 1.00000000 1
## 67 1.6666667 -0.85137604 1.7917595 0.97777778 1
## 68 0.7692308 0.20181866 2.5649494 1.01111111 1
## 69 1.4285714 -0.50953563 1.9459101 0.94444444 0
## 70 10.0000000 -23.02585093 0.0000000 1.00000000 0
## 71 1.6666667 -0.85137604 1.7917595 0.57777778 1
## 72 2.5000000 -2.29072683 1.3862944 0.97777778 1
## 73 0.7142857 0.24033731 2.6390573 0.47777778 1
## 74 1.1111111 -0.11706724 2.1972246 0.41111111 1
## 75 10.0000000 -23.02585093 0.0000000 0.96666667 0
## 76 3.3333333 -4.01324268 1.0986123 0.22222222 0
## 77 5.0000000 -8.04718956 0.6931472 0.10000000 0
## 78 5.0000000 -8.04718956 0.6931472 0.94444444 1
## 79 3.3333333 -4.01324268 1.0986123 0.20000000 1
## 80 0.7692308 0.20181866 2.5649494 0.78888889 1
## 81 3.3333333 -4.01324268 1.0986123 0.97777778 1
## 82 5.0000000 -8.04718956 0.6931472 0.74444444 0
## 83 2.0000000 -1.38629436 1.6094379 0.33333333 1
## 84 5.0000000 -8.04718956 0.6931472 0.37777778 0
## 85 1.0000000 0.00000000 2.3025851 1.01111111 0
## 86 5.0000000 -8.04718956 0.6931472 1.01111111 1
## 87 5.0000000 -8.04718956 0.6931472 0.81111111 0
## 88 5.0000000 -8.04718956 0.6931472 0.22222222 1
## 89 5.0000000 -8.04718956 0.6931472 0.98333333 0
## 90 3.3333333 -4.01324268 1.0986123 1.00555556 0
## 91 0.6250000 0.29375227 2.7725887 0.93333333 1
## 92 1.6666667 -0.85137604 1.7917595 0.50000000 1
## 93 1.6666667 -0.85137604 1.7917595 0.33888889 1
## 94 2.5000000 -2.29072683 1.3862944 0.35000000 0
## 95 1.1111111 -0.11706724 2.1972246 0.67222222 0
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## 574 3.3333333 -4.01324268 1.0986123 2.04444444 1
## 575 0.6250000 0.29375227 2.7725887 0.05000000 1## Rows: 575
## Columns: 18
## $ ID <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, …
## $ AGE <dbl> 39, 33, 33, 32, 24, 30, 39, 27, 40, 36, 38, 29, 32, 41, 31, 27,…
## $ BECK <dbl> 9.000, 34.000, 10.000, 20.000, 5.000, 32.550, 19.000, 10.000, 2…
## $ HC <dbl> 4, 4, 2, 4, 2, 3, 4, 4, 2, 2, 2, 3, 3, 1, 1, 2, 1, 4, 3, 2, 3, …
## $ IV <dbl> 3, 2, 3, 3, 1, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 2, 1, 3, 1, …
## $ NDT <dbl> 1, 8, 3, 1, 5, 1, 34, 2, 3, 7, 8, 1, 2, 8, 1, 3, 6, 1, 15, 5, 1…
## $ RACE <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ TREAT <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, …
## $ SITE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LEN.T <dbl> 123, 25, 7, 66, 173, 16, 179, 21, 176, 124, 176, 79, 182, 174, …
## $ TIME <dbl> 188, 26, 207, 144, 551, 32, 459, 22, 210, 184, 212, 87, 598, 26…
## $ CENSOR <dbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, …
## $ Y <dbl> 5.236442, 3.258097, 5.332719, 4.969813, 6.311735, 3.465736, 6.1…
## $ ND1 <dbl> 5.0000000, 1.1111111, 2.5000000, 5.0000000, 1.6666667, 5.000000…
## $ ND2 <dbl> -8.0471896, -0.1170672, -2.2907268, -8.0471896, -0.8513760, -8.…
## $ LNDT <dbl> 0.6931472, 2.1972246, 1.3862944, 0.6931472, 1.7917595, 0.693147…
## $ FRAC <dbl> 0.68333333, 0.13888889, 0.03888889, 0.73333333, 0.96111111, 0.0…
## $ IV3 <dbl> 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, …To talk about the ANOVA procedure, we’ll use the BECK and IV variables. We need to convert IV to a factor variable first (using the help file for guidance). We’ll add it to a new tibble called uis2.
uis2 <- uis %>%
mutate(IV_fct = factor(IV, levels = c(1, 2, 3),
labels = c("Never", "Previous", "Recent")))
uis2## ID AGE BECK HC IV NDT RACE TREAT SITE LEN.T TIME CENSOR Y
## 1 1 39 9.000 4 3 1 0 1 0 123 188 1 5.236442
## 2 2 33 34.000 4 2 8 0 1 0 25 26 1 3.258097
## 3 3 33 10.000 2 3 3 0 1 0 7 207 1 5.332719
## 4 4 32 20.000 4 3 1 0 0 0 66 144 1 4.969813
## 5 5 24 5.000 2 1 5 1 1 0 173 551 0 6.311735
## 6 6 30 32.550 3 3 1 0 1 0 16 32 1 3.465736
## 7 7 39 19.000 4 3 34 0 1 0 179 459 1 6.129050
## 8 8 27 10.000 4 3 2 0 1 0 21 22 1 3.091042
## 9 9 40 29.000 2 3 3 0 1 0 176 210 1 5.347108
## 10 10 36 25.000 2 3 7 0 1 0 124 184 1 5.214936
## 11 12 38 18.900 2 3 8 0 1 0 176 212 1 5.356586
## 12 13 29 16.000 3 1 1 0 1 0 79 87 1 4.465908
## 13 14 32 36.000 3 3 2 1 1 0 182 598 0 6.393591
## 14 15 41 19.000 1 3 8 0 1 0 174 260 1 5.560682
## 15 16 31 18.000 1 3 1 0 1 0 181 210 1 5.347108
## 16 17 27 12.000 2 3 3 0 1 0 61 84 1 4.430817
## 17 18 28 34.000 1 3 6 0 1 0 177 196 1 5.278115
## 18 19 28 23.000 4 2 1 0 1 0 19 19 1 2.944439
## 19 20 36 26.000 3 1 15 1 1 0 27 441 1 6.089045
## 20 21 32 18.900 2 3 5 0 1 0 175 449 1 6.107023
## 21 22 33 15.000 3 1 1 0 0 0 12 659 0 6.490724
## 22 23 28 25.200 1 3 8 0 0 0 21 21 1 3.044522
## 23 24 29 6.632 4 2 0 0 0 0 48 53 1 3.970292
## 24 25 35 2.100 2 3 9 0 0 0 90 225 1 5.416100
## 25 26 45 26.000 1 3 6 0 0 0 91 161 1 5.081404
## 26 27 35 39.789 4 3 5 0 0 0 87 87 1 4.465908
## 27 28 24 20.000 3 1 3 0 0 0 88 89 1 4.488636
## 28 29 36 16.000 1 3 7 0 0 0 9 44 1 3.784190
## 29 31 39 22.000 1 3 9 0 0 0 94 523 0 6.259581
## 30 32 36 9.947 4 2 10 0 0 0 91 226 1 5.420535
## 31 33 37 9.450 4 3 1 0 0 0 90 259 1 5.556828
## 32 34 30 39.000 2 3 1 0 0 0 89 289 1 5.666427
## 33 35 44 41.000 1 3 5 0 0 0 89 103 1 4.634729
## 34 36 28 31.000 3 1 6 1 0 0 100 624 0 6.436150
## 35 37 25 20.000 3 1 3 1 0 0 67 68 1 4.219508
## 36 38 30 8.000 2 3 7 0 1 0 25 57 1 4.043051
## 37 39 24 9.000 4 1 1 0 0 0 12 65 1 4.174387
## 38 40 27 20.000 3 1 1 0 0 0 79 79 1 4.369448
## 39 41 30 8.000 3 1 2 1 0 0 79 559 0 6.326149
## 40 42 34 8.000 2 3 0 0 1 0 78 79 1 4.369448
## 41 43 33 23.000 4 2 2 0 1 0 84 87 1 4.465908
## 42 44 34 18.000 3 3 6 0 1 0 91 91 1 4.510860
## 43 45 36 13.000 2 3 1 0 1 0 162 297 1 5.693732
## 44 46 27 23.000 1 3 0 0 1 0 45 45 1 3.806662
## 45 47 35 9.000 4 3 1 1 1 0 61 246 1 5.505332
## 46 48 24 14.000 1 3 0 0 1 0 19 37 1 3.610918
## 47 49 28 23.000 4 1 2 1 1 0 37 37 1 3.610918
## 48 50 46 10.000 1 3 8 0 1 0 51 538 0 6.287859
## 49 51 26 11.000 3 3 1 0 1 0 60 541 0 6.293419
## 50 52 42 16.000 1 3 25 0 1 0 177 184 1 5.214936
## 51 53 30 0.000 3 1 0 0 1 0 43 122 1 4.804021
## 52 55 30 12.000 4 1 3 1 1 0 21 156 1 5.049856
## 53 56 27 21.000 2 3 2 0 0 0 88 121 1 4.795791
## 54 57 38 0.000 1 3 6 0 0 0 96 231 1 5.442418
## 55 58 48 8.000 4 3 10 0 0 0 111 111 1 4.709530
## 56 59 36 25.000 1 3 10 0 0 0 38 38 1 3.637586
## 57 60 28 6.300 3 1 7 0 0 0 15 15 1 2.708050
## 58 61 31 20.000 4 2 5 0 0 0 50 54 1 3.988984
## 59 62 28 4.000 2 3 5 0 0 0 61 127 1 4.844187
## 60 63 28 20.000 3 1 1 0 0 0 31 105 1 4.653960
## 61 64 26 17.000 2 1 2 1 0 0 11 11 1 2.397895
## 62 65 34 3.000 4 3 6 0 0 0 90 153 1 5.030438
## 63 66 26 29.000 2 3 5 0 0 0 11 11 1 2.397895
## 64 68 31 26.000 1 3 5 0 0 0 46 46 1 3.828641
## 65 69 41 12.000 1 3 0 1 0 0 38 655 0 6.484635
## 66 70 30 24.000 4 3 0 0 0 0 90 166 1 5.111988
## 67 72 39 15.750 4 3 5 0 0 0 88 95 1 4.553877
## 68 74 33 9.000 2 3 12 0 0 0 91 151 1 5.017280
## 69 75 33 18.000 4 2 6 0 0 0 85 220 1 5.393628
## 70 76 29 20.000 4 1 0 1 0 0 90 227 1 5.424950
## 71 77 36 17.000 1 3 5 0 0 0 52 343 1 5.837730
## 72 78 26 3.000 4 3 3 0 0 0 88 119 1 4.779123
## 73 79 37 27.000 1 3 13 0 0 0 43 43 1 3.761200
## 74 81 29 31.500 1 3 8 0 0 0 37 47 1 3.850148
## 75 83 30 19.000 3 1 0 1 0 0 87 805 0 6.690842
## 76 84 35 15.000 3 2 2 0 0 0 20 321 1 5.771441
## 77 85 33 22.000 3 1 1 0 0 0 9 167 1 5.117994
## 78 87 36 16.000 2 3 1 0 0 0 85 491 1 6.196444
## 79 88 28 17.000 1 3 2 0 0 0 18 35 1 3.555348
## 80 89 31 32.550 1 3 12 1 0 0 71 123 1 4.812184
## 81 90 23 24.000 1 3 2 0 0 0 88 597 0 6.391917
## 82 91 33 22.000 3 2 1 0 0 0 67 762 0 6.635947
## 83 93 37 18.000 2 3 4 0 0 0 30 31 1 3.433987
## 84 94 25 17.850 3 1 1 0 1 0 68 228 1 5.429346
## 85 95 56 5.000 2 2 9 1 1 0 182 553 0 6.315358
## 86 96 23 39.000 1 3 1 0 1 0 182 190 1 5.247024
## 87 97 26 21.000 3 1 1 0 1 0 146 307 1 5.726848
## 88 98 26 11.000 1 3 1 0 1 0 40 73 1 4.290459
## 89 99 23 14.000 3 1 1 0 1 0 177 208 1 5.337538
## 90 100 28 31.000 4 2 2 1 1 0 181 267 1 5.587249
## 91 102 30 14.000 1 3 15 0 1 0 168 169 1 5.129899
## 92 104 25 6.000 2 3 5 0 1 0 90 655 0 6.484635
## 93 105 33 16.000 1 3 5 0 1 0 61 70 1 4.248495
## 94 106 22 6.000 3 1 3 1 1 0 63 398 1 5.986452
## 95 108 25 20.000 4 2 8 1 1 0 121 122 1 4.804021
## 96 111 38 9.000 3 1 1 1 0 0 89 96 1 4.564348
## 97 112 35 11.000 2 1 3 0 1 0 51 1172 0 7.066467
## 98 113 35 15.000 3 1 1 0 0 0 88 734 0 6.598509
## 99 114 25 13.000 3 3 1 0 0 0 25 26 1 3.258097
## 100 115 33 31.000 3 1 3 1 0 0 83 84 1 4.430817
## 101 116 30 5.000 3 1 2 1 0 0 89 171 1 5.141664
## 102 117 45 10.000 2 3 1 0 0 0 24 159 1 5.068904
## 103 119 42 23.000 2 3 20 0 0 0 7 7 1 1.945910
## 104 120 29 16.000 4 1 1 1 0 0 85 763 0 6.637258
## 105 121 24 37.800 3 1 0 0 0 0 89 104 1 4.644391
## 106 122 33 10.000 2 3 4 0 0 0 91 162 1 5.087596
## 107 123 32 9.000 3 1 0 0 0 0 89 90 1 4.499810
## 108 124 26 15.000 3 1 0 0 0 0 82 373 1 5.921578
## 109 125 28 2.000 1 3 3 0 0 0 84 115 1 4.744932
## 110 127 37 34.000 2 3 1 0 0 0 30 30 1 3.401197
## 111 128 23 11.000 4 1 6 0 0 0 7 8 1 2.079442
## 112 129 40 31.000 2 3 3 1 0 0 84 168 1 5.123964
## 113 130 36 36.750 3 3 0 0 0 0 70 70 1 4.248495
## 114 131 23 26.000 3 2 2 0 0 0 76 130 1 4.867534
## 115 132 35 5.000 4 1 1 1 0 0 89 285 1 5.652489
## 116 133 25 19.000 2 3 1 0 1 0 178 569 0 6.343880
## 117 134 35 21.000 2 3 6 0 1 0 87 87 1 4.465908
## 118 135 46 1.000 4 2 0 0 1 0 175 310 1 5.736572
## 119 136 32 6.000 4 1 3 0 1 0 87 87 1 4.465908
## 120 137 35 23.000 3 1 16 1 1 0 110 544 0 6.298949
## 121 138 34 38.000 3 3 1 0 1 0 21 156 1 5.049856
## 122 139 43 24.000 3 1 3 0 1 0 139 658 0 6.489205
## 123 140 39 3.000 4 3 15 0 1 0 181 273 1 5.609472
## 124 141 27 16.800 4 3 2 1 1 0 33 168 1 5.123964
## 125 142 38 35.000 1 3 1 0 1 0 39 83 1 4.418841
## 126 143 37 11.000 2 3 7 0 1 0 4 4 1 1.386294
## 127 144 44 2.000 1 3 4 1 1 0 184 708 0 6.562444
## 128 145 25 16.000 4 1 1 1 1 0 123 137 1 4.919981
## 129 146 34 15.000 3 1 1 0 1 0 176 259 1 5.556828
## 130 147 34 11.000 3 3 2 1 1 0 174 560 0 6.327937
## 131 148 38 11.000 1 3 1 1 1 0 181 586 0 6.373320
## 132 149 24 22.000 2 3 2 1 1 0 113 190 1 5.247024
## 133 151 42 18.000 2 3 3 0 1 0 164 544 0 6.298949
## 134 153 34 29.000 4 3 1 1 0 0 84 494 1 6.202536
## 135 154 45 27.000 1 3 8 0 0 0 80 541 0 6.293419
## 136 155 40 16.000 2 3 4 0 0 0 91 94 1 4.543295
## 137 156 27 9.000 4 1 3 1 0 0 97 567 0 6.340359
## 138 157 24 0.000 4 1 3 0 0 0 51 55 1 4.007333
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## 140 159 34 24.000 3 1 4 0 0 0 90 276 1 5.620401
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## 144 164 40 13.000 1 3 4 1 0 0 91 552 0 6.313548
## 145 165 37 29.000 2 3 5 0 0 0 90 90 1 4.499810
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## 507 557 28 40.000 3 1 2 1 0 1 198 198 1 5.288267
## 508 558 31 13.000 3 3 2 0 0 1 170 551 0 6.311735
## 509 559 31 39.000 2 3 4 0 1 1 50 110 1 4.700480
## 510 560 33 24.000 4 1 0 0 1 1 163 541 0 6.293419
## 511 561 24 26.000 3 1 11 0 0 1 182 242 1 5.488938
## 512 562 26 18.000 3 1 3 0 0 1 150 537 0 6.285998
## 513 563 31 19.000 2 3 7 0 1 1 34 56 1 4.025352
## 514 564 40 14.700 2 3 4 0 1 1 34 34 1 3.526361
## 515 566 34 2.000 3 1 3 0 1 1 366 549 0 6.308098
## 516 567 30 11.000 3 2 7 0 0 1 133 133 1 4.890349
## 517 568 36 0.000 3 2 3 0 0 1 69 226 1 5.420535
## 518 569 38 17.000 2 3 6 0 1 1 366 401 1 5.993961
## 519 570 31 20.000 1 3 6 1 1 1 14 14 1 2.639057
## 520 571 27 22.000 2 2 2 0 0 1 184 548 0 6.306275
## 521 572 32 21.000 1 3 15 0 1 1 89 224 1 5.411646
## 522 573 35 23.000 3 1 5 1 0 1 183 540 0 6.291569
## 523 574 44 29.000 2 3 13 0 0 1 177 237 1 5.468060
## 524 575 31 5.000 2 3 10 0 1 1 154 354 1 5.869297
## 525 576 28 23.000 3 2 20 0 0 1 123 123 1 4.812184
## 526 577 40 8.000 4 2 1 0 0 1 146 170 1 5.135798
## 527 578 25 12.000 3 1 10 1 1 1 203 203 1 5.313206
## 528 579 32 10.000 1 3 6 0 1 1 360 360 1 5.886104
## 529 580 29 15.750 4 1 2 0 0 1 79 139 1 4.934474
## 530 581 40 2.000 2 2 5 0 1 1 201 215 1 5.370638
## 531 582 27 9.000 4 2 0 0 1 1 129 129 1 4.859812
## 532 583 26 2.000 3 1 1 0 1 1 365 396 1 5.981414
## 533 584 34 15.000 3 1 4 1 1 1 159 547 0 6.304449
## 534 585 49 4.000 4 2 2 0 0 1 177 547 0 6.304449
## 535 586 21 25.000 1 3 1 0 1 1 71 71 1 4.262680
## 536 587 39 23.000 3 3 2 0 1 1 108 168 1 5.123964
## 537 588 33 15.000 4 2 4 0 1 1 198 228 1 5.429346
## 538 589 32 3.000 3 1 1 0 1 1 372 551 0 6.311735
## 539 590 35 9.000 4 2 6 0 0 1 25 654 0 6.483107
## 540 591 31 20.000 4 1 0 1 1 1 48 51 1 3.931826
## 541 592 28 5.000 4 1 3 0 0 1 191 548 0 6.306275
## 542 593 27 29.000 3 2 5 0 1 1 171 231 1 5.442418
## 543 594 29 21.000 2 1 1 1 1 1 145 280 1 5.634790
## 544 595 30 1.000 2 1 20 0 0 1 183 184 1 5.214936
## 545 596 27 18.000 4 1 3 1 0 1 72 86 1 4.454347
## 546 598 40 15.000 4 2 1 0 1 1 44 46 1 3.828641
## 547 599 37 20.000 3 1 2 1 1 1 140 200 1 5.298317
## 548 600 33 10.000 4 1 0 0 0 1 184 244 1 5.497168
## 549 601 28 20.000 4 1 2 0 0 1 94 182 1 5.204007
## 550 602 40 15.000 4 2 8 0 1 1 296 296 1 5.690359
## 551 603 48 20.000 4 1 0 1 0 1 23 24 1 3.178054
## 552 604 38 25.000 3 1 1 0 0 1 128 142 1 4.955827
## 553 605 35 13.000 4 1 0 0 0 1 106 120 1 4.787492
## 554 606 37 13.000 4 2 0 0 0 1 46 47 1 3.850148
## 555 607 25 15.000 3 1 0 1 1 1 150 519 1 6.251904
## 556 608 26 8.000 4 1 2 0 1 1 48 248 1 5.513429
## 557 609 30 9.000 3 3 3 0 0 1 29 31 1 3.433987
## 558 610 28 16.000 4 2 2 0 0 1 179 567 0 6.340359
## 559 611 23 11.000 2 3 4 0 0 1 170 353 1 5.866468
## 560 612 36 31.000 4 1 1 0 1 1 365 458 1 6.126869
## 561 613 36 13.000 4 2 4 0 1 1 400 554 0 6.317165
## 562 614 24 5.000 4 1 0 1 0 1 56 116 1 4.753590
## 563 615 33 9.000 3 2 5 0 0 1 24 74 1 4.304065
## 564 616 38 15.000 4 2 6 0 0 1 10 10 1 2.302585
## 565 617 41 20.000 3 3 21 0 1 1 354 355 1 5.872118
## 566 618 31 21.000 3 1 0 1 1 1 232 232 1 5.446737
## 567 619 31 23.000 4 2 11 0 1 1 54 68 1 4.219508
## 568 620 37 5.000 4 1 0 1 1 1 48 48 1 3.871201
## 569 621 37 17.000 4 2 4 1 0 1 57 60 1 4.094345
## 570 622 33 13.000 4 1 0 0 0 1 46 50 1 3.912023
## 571 624 53 9.000 4 2 6 0 0 1 39 126 1 4.836282
## 572 625 37 20.000 2 3 4 0 0 1 17 18 1 2.890372
## 573 626 28 10.000 4 2 3 0 1 1 21 35 1 3.555348
## 574 627 35 17.000 1 3 2 0 0 1 184 379 1 5.937536
## 575 628 46 31.500 1 3 15 1 1 1 9 377 1 5.932245
## ND1 ND2 LNDT FRAC IV3 IV_fct
## 1 5.0000000 -8.04718956 0.6931472 0.68333333 1 Recent
## 2 1.1111111 -0.11706724 2.1972246 0.13888889 0 Previous
## 3 2.5000000 -2.29072683 1.3862944 0.03888889 1 Recent
## 4 5.0000000 -8.04718956 0.6931472 0.73333333 1 Recent
## 5 1.6666667 -0.85137604 1.7917595 0.96111111 0 Never
## 6 5.0000000 -8.04718956 0.6931472 0.08888889 1 Recent
## 7 0.2857143 0.35793228 3.5553481 0.99444444 1 Recent
## 8 3.3333333 -4.01324268 1.0986123 0.11666667 1 Recent
## 9 2.5000000 -2.29072683 1.3862944 0.97777778 1 Recent
## 10 1.2500000 -0.27892944 2.0794415 0.68888889 1 Recent
## 11 1.1111111 -0.11706724 2.1972246 0.97777778 1 Recent
## 12 5.0000000 -8.04718956 0.6931472 0.43888889 0 Never
## 13 3.3333333 -4.01324268 1.0986123 1.01111111 1 Recent
## 14 1.1111111 -0.11706724 2.1972246 0.96666667 1 Recent
## 15 5.0000000 -8.04718956 0.6931472 1.00555556 1 Recent
## 16 2.5000000 -2.29072683 1.3862944 0.33888889 1 Recent
## 17 1.4285714 -0.50953563 1.9459101 0.98333333 1 Recent
## 18 5.0000000 -8.04718956 0.6931472 0.10555556 0 Previous
## 19 0.6250000 0.29375227 2.7725887 0.15000000 0 Never
## 20 1.6666667 -0.85137604 1.7917595 0.97222222 1 Recent
## 21 5.0000000 -8.04718956 0.6931472 0.13333333 0 Never
## 22 1.1111111 -0.11706724 2.1972246 0.23333333 1 Recent
## 23 10.0000000 -23.02585093 0.0000000 0.53333333 0 Previous
## 24 1.0000000 0.00000000 2.3025851 1.00000000 1 Recent
## 25 1.4285714 -0.50953563 1.9459101 1.01111111 1 Recent
## 26 1.6666667 -0.85137604 1.7917595 0.96666667 1 Recent
## 27 2.5000000 -2.29072683 1.3862944 0.97777778 0 Never
## 28 1.2500000 -0.27892944 2.0794415 0.10000000 1 Recent
## 29 1.0000000 0.00000000 2.3025851 1.04444444 1 Recent
## 30 0.9090909 0.08664562 2.3978953 1.01111111 0 Previous
## 31 5.0000000 -8.04718956 0.6931472 1.00000000 1 Recent
## 32 5.0000000 -8.04718956 0.6931472 0.98888889 1 Recent
## 33 1.6666667 -0.85137604 1.7917595 0.98888889 1 Recent
## 34 1.4285714 -0.50953563 1.9459101 1.11111111 0 Never
## 35 2.5000000 -2.29072683 1.3862944 0.74444444 0 Never
## 36 1.2500000 -0.27892944 2.0794415 0.13888889 1 Recent
## 37 5.0000000 -8.04718956 0.6931472 0.13333333 0 Never
## 38 5.0000000 -8.04718956 0.6931472 0.87777778 0 Never
## 39 3.3333333 -4.01324268 1.0986123 0.87777778 0 Never
## 40 10.0000000 -23.02585093 0.0000000 0.43333333 1 Recent
## 41 3.3333333 -4.01324268 1.0986123 0.46666667 0 Previous
## 42 1.4285714 -0.50953563 1.9459101 0.50555556 1 Recent
## 43 5.0000000 -8.04718956 0.6931472 0.90000000 1 Recent
## 44 10.0000000 -23.02585093 0.0000000 0.25000000 1 Recent
## 45 5.0000000 -8.04718956 0.6931472 0.33888889 1 Recent
## 46 10.0000000 -23.02585093 0.0000000 0.10555556 1 Recent
## 47 3.3333333 -4.01324268 1.0986123 0.20555556 0 Never
## 48 1.1111111 -0.11706724 2.1972246 0.28333333 1 Recent
## 49 5.0000000 -8.04718956 0.6931472 0.33333333 1 Recent
## 50 0.3846154 0.36750440 3.2580965 0.98333333 1 Recent
## 51 10.0000000 -23.02585093 0.0000000 0.23888889 0 Never
## 52 2.5000000 -2.29072683 1.3862944 0.11666667 0 Never
## 53 3.3333333 -4.01324268 1.0986123 0.97777778 1 Recent
## 54 1.4285714 -0.50953563 1.9459101 1.06666667 1 Recent
## 55 0.9090909 0.08664562 2.3978953 1.23333333 1 Recent
## 56 0.9090909 0.08664562 2.3978953 0.42222222 1 Recent
## 57 1.2500000 -0.27892944 2.0794415 0.16666667 0 Never
## 58 1.6666667 -0.85137604 1.7917595 0.55555556 0 Previous
## 59 1.6666667 -0.85137604 1.7917595 0.67777778 1 Recent
## 60 5.0000000 -8.04718956 0.6931472 0.34444444 0 Never
## 61 3.3333333 -4.01324268 1.0986123 0.12222222 0 Never
## 62 1.4285714 -0.50953563 1.9459101 1.00000000 1 Recent
## 63 1.6666667 -0.85137604 1.7917595 0.12222222 1 Recent
## 64 1.6666667 -0.85137604 1.7917595 0.51111111 1 Recent
## 65 10.0000000 -23.02585093 0.0000000 0.42222222 1 Recent
## 66 10.0000000 -23.02585093 0.0000000 1.00000000 1 Recent
## 67 1.6666667 -0.85137604 1.7917595 0.97777778 1 Recent
## 68 0.7692308 0.20181866 2.5649494 1.01111111 1 Recent
## 69 1.4285714 -0.50953563 1.9459101 0.94444444 0 Previous
## 70 10.0000000 -23.02585093 0.0000000 1.00000000 0 Never
## 71 1.6666667 -0.85137604 1.7917595 0.57777778 1 Recent
## 72 2.5000000 -2.29072683 1.3862944 0.97777778 1 Recent
## 73 0.7142857 0.24033731 2.6390573 0.47777778 1 Recent
## 74 1.1111111 -0.11706724 2.1972246 0.41111111 1 Recent
## 75 10.0000000 -23.02585093 0.0000000 0.96666667 0 Never
## 76 3.3333333 -4.01324268 1.0986123 0.22222222 0 Previous
## 77 5.0000000 -8.04718956 0.6931472 0.10000000 0 Never
## 78 5.0000000 -8.04718956 0.6931472 0.94444444 1 Recent
## 79 3.3333333 -4.01324268 1.0986123 0.20000000 1 Recent
## 80 0.7692308 0.20181866 2.5649494 0.78888889 1 Recent
## 81 3.3333333 -4.01324268 1.0986123 0.97777778 1 Recent
## 82 5.0000000 -8.04718956 0.6931472 0.74444444 0 Previous
## 83 2.0000000 -1.38629436 1.6094379 0.33333333 1 Recent
## 84 5.0000000 -8.04718956 0.6931472 0.37777778 0 Never
## 85 1.0000000 0.00000000 2.3025851 1.01111111 0 Previous
## 86 5.0000000 -8.04718956 0.6931472 1.01111111 1 Recent
## 87 5.0000000 -8.04718956 0.6931472 0.81111111 0 Never
## 88 5.0000000 -8.04718956 0.6931472 0.22222222 1 Recent
## 89 5.0000000 -8.04718956 0.6931472 0.98333333 0 Never
## 90 3.3333333 -4.01324268 1.0986123 1.00555556 0 Previous
## 91 0.6250000 0.29375227 2.7725887 0.93333333 1 Recent
## 92 1.6666667 -0.85137604 1.7917595 0.50000000 1 Recent
## 93 1.6666667 -0.85137604 1.7917595 0.33888889 1 Recent
## 94 2.5000000 -2.29072683 1.3862944 0.35000000 0 Never
## 95 1.1111111 -0.11706724 2.1972246 0.67222222 0 Previous
## 96 5.0000000 -8.04718956 0.6931472 0.98888889 0 Never
## 97 2.5000000 -2.29072683 1.3862944 0.28333333 0 Never
## 98 5.0000000 -8.04718956 0.6931472 0.97777778 0 Never
## 99 5.0000000 -8.04718956 0.6931472 0.27777778 1 Recent
## 100 2.5000000 -2.29072683 1.3862944 0.92222222 0 Never
## 101 3.3333333 -4.01324268 1.0986123 0.98888889 0 Never
## 102 5.0000000 -8.04718956 0.6931472 0.26666667 1 Recent
## 103 0.4761905 0.35330350 3.0445224 0.07777778 1 Recent
## 104 5.0000000 -8.04718956 0.6931472 0.94444444 0 Never
## 105 10.0000000 -23.02585093 0.0000000 0.98888889 0 Never
## 106 2.0000000 -1.38629436 1.6094379 1.01111111 1 Recent
## 107 10.0000000 -23.02585093 0.0000000 0.98888889 0 Never
## 108 10.0000000 -23.02585093 0.0000000 0.91111111 0 Never
## 109 2.5000000 -2.29072683 1.3862944 0.93333333 1 Recent
## 110 5.0000000 -8.04718956 0.6931472 0.33333333 1 Recent
## 111 1.4285714 -0.50953563 1.9459101 0.07777778 0 Never
## 112 2.5000000 -2.29072683 1.3862944 0.93333333 1 Recent
## 113 10.0000000 -23.02585093 0.0000000 0.77777778 1 Recent
## 114 3.3333333 -4.01324268 1.0986123 0.84444444 0 Previous
## 115 5.0000000 -8.04718956 0.6931472 0.98888889 0 Never
## 116 5.0000000 -8.04718956 0.6931472 0.98888889 1 Recent
## 117 1.4285714 -0.50953563 1.9459101 0.48333333 1 Recent
## 118 10.0000000 -23.02585093 0.0000000 0.97222222 0 Previous
## 119 2.5000000 -2.29072683 1.3862944 0.48333333 0 Never
## 120 0.5882353 0.31213427 2.8332133 0.61111111 0 Never
## 121 5.0000000 -8.04718956 0.6931472 0.11666667 1 Recent
## 122 2.5000000 -2.29072683 1.3862944 0.77222222 0 Never
## 123 0.6250000 0.29375227 2.7725887 1.00555556 1 Recent
## 124 3.3333333 -4.01324268 1.0986123 0.18333333 1 Recent
## 125 5.0000000 -8.04718956 0.6931472 0.21666667 1 Recent
## 126 1.2500000 -0.27892944 2.0794415 0.02222222 1 Recent
## 127 2.0000000 -1.38629436 1.6094379 1.02222222 1 Recent
## 128 5.0000000 -8.04718956 0.6931472 0.68333333 0 Never
## 129 5.0000000 -8.04718956 0.6931472 0.97777778 0 Never
## 130 3.3333333 -4.01324268 1.0986123 0.96666667 1 Recent
## 131 5.0000000 -8.04718956 0.6931472 1.00555556 1 Recent
## 132 3.3333333 -4.01324268 1.0986123 0.62777778 1 Recent
## 133 2.5000000 -2.29072683 1.3862944 0.91111111 1 Recent
## 134 5.0000000 -8.04718956 0.6931472 0.93333333 1 Recent
## 135 1.1111111 -0.11706724 2.1972246 0.88888889 1 Recent
## 136 2.0000000 -1.38629436 1.6094379 1.01111111 1 Recent
## 137 2.5000000 -2.29072683 1.3862944 1.07777778 0 Never
## 138 2.5000000 -2.29072683 1.3862944 0.56666667 0 Never
## 139 2.5000000 -2.29072683 1.3862944 1.01111111 1 Recent
## 140 2.0000000 -1.38629436 1.6094379 1.00000000 0 Never
## 141 1.4285714 -0.50953563 1.9459101 0.51111111 1 Recent
## 142 5.0000000 -8.04718956 0.6931472 0.84444444 0 Never
## 143 3.3333333 -4.01324268 1.0986123 0.83333333 1 Recent
## 144 2.0000000 -1.38629436 1.6094379 1.01111111 1 Recent
## 145 1.6666667 -0.85137604 1.7917595 1.00000000 1 Recent
## 146 1.4285714 -0.50953563 1.9459101 0.03333333 1 Recent
## 147 2.5000000 -2.29072683 1.3862944 0.04444444 1 Recent
## 148 5.0000000 -8.04718956 0.6931472 0.18333333 0 Never
## 149 1.1111111 -0.11706724 2.1972246 0.17222222 1 Recent
## 150 5.0000000 -8.04718956 0.6931472 0.96666667 0 Never
## 151 3.3333333 -4.01324268 1.0986123 0.18888889 0 Never
## 152 2.5000000 -2.29072683 1.3862944 0.33333333 0 Previous
## 153 1.6666667 -0.85137604 1.7917595 0.43333333 0 Previous
## 154 3.3333333 -4.01324268 1.0986123 1.01111111 0 Never
## 155 5.0000000 -8.04718956 0.6931472 1.01111111 0 Never
## 156 10.0000000 -23.02585093 0.0000000 0.43333333 0 Never
## 157 5.0000000 -8.04718956 0.6931472 0.30555556 0 Never
## 158 3.3333333 -4.01324268 1.0986123 1.23888889 0 Never
## 159 0.4761905 0.35330350 3.0445224 0.13888889 0 Never
## 160 10.0000000 -23.02585093 0.0000000 0.35000000 0 Never
## 161 5.0000000 -8.04718956 0.6931472 0.73888889 0 Never
## 162 1.6666667 -0.85137604 1.7917595 0.85555556 1 Recent
## 163 3.3333333 -4.01324268 1.0986123 0.38888889 0 Never
## 164 5.0000000 -8.04718956 0.6931472 0.36666667 0 Previous
## 165 2.0000000 -1.38629436 1.6094379 0.22222222 1 Recent
## 166 5.0000000 -8.04718956 0.6931472 0.41666667 0 Never
## 167 3.3333333 -4.01324268 1.0986123 1.03888889 0 Previous
## 168 3.3333333 -4.01324268 1.0986123 1.01666667 0 Never
## 169 1.1111111 -0.11706724 2.1972246 1.01111111 0 Never
## 170 3.3333333 -4.01324268 1.0986123 1.06666667 0 Previous
## 171 2.0000000 -1.38629436 1.6094379 0.90000000 1 Recent
## 172 3.3333333 -4.01324268 1.0986123 1.07222222 1 Recent
## 173 3.3333333 -4.01324268 1.0986123 0.61666667 0 Never
## 174 1.1111111 -0.11706724 2.1972246 1.01111111 1 Recent
## 175 2.0000000 -1.38629436 1.6094379 1.00000000 0 Never
## 176 10.0000000 -23.02585093 0.0000000 0.51666667 0 Never
## 177 1.2500000 -0.27892944 2.0794415 0.92777778 0 Never
## 178 3.3333333 -4.01324268 1.0986123 1.08888889 0 Never
## 179 2.0000000 -1.38629436 1.6094379 0.58888889 0 Previous
## 180 1.4285714 -0.50953563 1.9459101 0.87777778 1 Recent
## 181 5.0000000 -8.04718956 0.6931472 1.01111111 0 Previous
## 182 2.0000000 -1.38629436 1.6094379 0.98888889 0 Never
## 183 3.3333333 -4.01324268 1.0986123 0.98888889 0 Previous
## 184 0.9090909 0.08664562 2.3978953 0.97777778 1 Recent
## 185 10.0000000 -23.02585093 0.0000000 1.05555556 0 Never
## 186 10.0000000 -23.02585093 0.0000000 0.05555556 1 Recent
## 187 1.1111111 -0.11706724 2.1972246 0.35555556 1 Recent
## 188 3.3333333 -4.01324268 1.0986123 1.02222222 0 Previous
## 189 2.5000000 -2.29072683 1.3862944 0.73333333 0 Never
## 190 1.1111111 -0.11706724 2.1972246 1.00000000 0 Never
## 191 2.5000000 -2.29072683 1.3862944 1.03333333 0 Previous
## 192 3.3333333 -4.01324268 1.0986123 0.98888889 0 Never
## 193 10.0000000 -23.02585093 0.0000000 1.01111111 0 Previous
## 194 1.1111111 -0.11706724 2.1972246 0.62222222 1 Recent
## 195 1.2500000 -0.27892944 2.0794415 1.00000000 1 Recent
## 196 2.5000000 -2.29072683 1.3862944 0.81111111 0 Never
## 197 1.6666667 -0.85137604 1.7917595 0.94444444 0 Never
## 198 2.5000000 -2.29072683 1.3862944 0.25555556 1 Recent
## 199 2.5000000 -2.29072683 1.3862944 0.94444444 1 Recent
## 200 1.6666667 -0.85137604 1.7917595 1.00000000 1 Recent
## 201 2.0000000 -1.38629436 1.6094379 0.58888889 0 Never
## 202 3.3333333 -4.01324268 1.0986123 1.06666667 1 Recent
## 203 3.3333333 -4.01324268 1.0986123 0.92222222 0 Previous
## 204 1.1111111 -0.11706724 2.1972246 0.60000000 1 Recent
## 205 1.0000000 0.00000000 2.3025851 0.87777778 0 Previous
## 206 2.5000000 -2.29072683 1.3862944 0.90000000 1 Recent
## 207 2.0000000 -1.38629436 1.6094379 0.20000000 0 Never
## 208 2.0000000 -1.38629436 1.6094379 1.02222222 1 Recent
## 209 0.9090909 0.08664562 2.3978953 0.21666667 0 Previous
## 210 10.0000000 -23.02585093 0.0000000 0.98333333 0 Never
## 211 3.3333333 -4.01324268 1.0986123 0.67777778 1 Recent
## 212 2.0000000 -1.38629436 1.6094379 0.98888889 0 Never
## 213 1.2500000 -0.27892944 2.0794415 0.96111111 0 Never
## 214 0.3125000 0.36348463 3.4657359 0.29444444 1 Recent
## 215 1.6666667 -0.85137604 1.7917595 0.52222222 1 Recent
## 216 3.3333333 -4.01324268 1.0986123 0.90555556 0 Never
## 217 1.4285714 -0.50953563 1.9459101 0.88888889 0 Previous
## 218 1.4285714 -0.50953563 1.9459101 0.33888889 0 Previous
## 219 0.7692308 0.20181866 2.5649494 0.22777778 0 Never
## 220 1.6666667 -0.85137604 1.7917595 0.29444444 1 Recent
## 221 2.0000000 -1.38629436 1.6094379 0.29444444 1 Recent
## 222 0.8333333 0.15193463 2.4849066 0.07222222 0 Previous
## 223 2.5000000 -2.29072683 1.3862944 1.01666667 0 Never
## 224 2.0000000 -1.38629436 1.6094379 1.01111111 1 Recent
## 225 2.5000000 -2.29072683 1.3862944 1.01666667 1 Recent
## 226 1.0000000 0.00000000 2.3025851 0.35000000 1 Recent
## 227 1.2500000 -0.27892944 2.0794415 0.61666667 1 Recent
## 228 3.3333333 -4.01324268 1.0986123 0.96666667 0 Previous
## 229 2.5000000 -2.29072683 1.3862944 0.96111111 1 Recent
## 230 3.3333333 -4.01324268 1.0986123 0.66111111 0 Never
## 231 2.5000000 -2.29072683 1.3862944 1.00000000 0 Never
## 232 3.3333333 -4.01324268 1.0986123 0.54444444 0 Never
## 233 2.0000000 -1.38629436 1.6094379 0.27777778 0 Never
## 234 5.0000000 -8.04718956 0.6931472 0.98888889 0 Previous
## 235 0.5882353 0.31213427 2.8332133 0.55555556 1 Recent
## 236 2.0000000 -1.38629436 1.6094379 0.51666667 0 Previous
## 237 2.5000000 -2.29072683 1.3862944 0.91666667 1 Recent
## 238 10.0000000 -23.02585093 0.0000000 0.51666667 0 Never
## 239 1.6666667 -0.85137604 1.7917595 0.48888889 1 Recent
## 240 1.6666667 -0.85137604 1.7917595 0.85555556 0 Never
## 241 10.0000000 -23.02585093 0.0000000 1.01111111 0 Never
## 242 3.3333333 -4.01324268 1.0986123 1.05555556 0 Never
## 243 5.0000000 -8.04718956 0.6931472 0.91111111 1 Recent
## 244 1.6666667 -0.85137604 1.7917595 0.84444444 0 Previous
## 245 2.5000000 -2.29072683 1.3862944 0.05555556 0 Previous
## 246 1.2500000 -0.27892944 2.0794415 0.76666667 1 Recent
## 247 0.7692308 0.20181866 2.5649494 1.00000000 0 Never
## 248 1.1111111 -0.11706724 2.1972246 0.21111111 0 Never
## 249 2.0000000 -1.38629436 1.6094379 0.66666667 0 Never
## 250 10.0000000 -23.02585093 0.0000000 0.76666667 0 Never
## 251 1.1111111 -0.11706724 2.1972246 0.94444444 0 Previous
## 252 2.5000000 -2.29072683 1.3862944 1.02222222 1 Recent
## 253 2.5000000 -2.29072683 1.3862944 0.61111111 0 Never
## 254 1.0000000 0.00000000 2.3025851 0.22222222 1 Recent
## 255 3.3333333 -4.01324268 1.0986123 0.96666667 1 Recent
## 256 10.0000000 -23.02585093 0.0000000 1.01111111 0 Never
## 257 10.0000000 -23.02585093 0.0000000 0.10000000 0 Never
## 258 1.4285714 -0.50953563 1.9459101 0.24444444 1 Recent
## 259 5.0000000 -8.04718956 0.6931472 0.96666667 0 Never
## 260 0.9090909 0.08664562 2.3978953 0.95555556 1 Recent
## 261 1.2500000 -0.27892944 2.0794415 0.94444444 1 Recent
## 262 10.0000000 -23.02585093 0.0000000 0.92222222 0 Never
## 263 1.4285714 -0.50953563 1.9459101 0.92222222 0 Never
## 264 1.4285714 -0.50953563 1.9459101 1.02222222 0 Previous
## 265 2.5000000 -2.29072683 1.3862944 0.94444444 0 Previous
## 266 1.4285714 -0.50953563 1.9459101 0.40000000 0 Never
## 267 3.3333333 -4.01324268 1.0986123 0.96666667 1 Recent
## 268 10.0000000 -23.02585093 0.0000000 0.31111111 0 Never
## 269 1.6666667 -0.85137604 1.7917595 0.52222222 1 Recent
## 270 10.0000000 -23.02585093 0.0000000 0.41111111 1 Recent
## 271 5.0000000 -8.04718956 0.6931472 1.03333333 1 Recent
## 272 3.3333333 -4.01324268 1.0986123 0.98888889 0 Previous
## 273 2.5000000 -2.29072683 1.3862944 0.46666667 1 Recent
## 274 10.0000000 -23.02585093 0.0000000 0.07222222 0 Previous
## 275 0.7692308 0.20181866 2.5649494 0.47222222 1 Recent
## 276 2.5000000 -2.29072683 1.3862944 0.05000000 0 Previous
## 277 2.0000000 -1.38629436 1.6094379 0.90000000 0 Previous
## 278 0.4761905 0.35330350 3.0445224 0.25555556 1 Recent
## 279 0.4761905 0.35330350 3.0445224 0.28888889 1 Recent
## 280 1.0000000 0.00000000 2.3025851 0.93333333 0 Previous
## 281 1.6666667 -0.85137604 1.7917595 0.25555556 1 Recent
## 282 3.3333333 -4.01324268 1.0986123 0.95555556 0 Never
## 283 2.0000000 -1.38629436 1.6094379 1.00000000 1 Recent
## 284 1.1111111 -0.11706724 2.1972246 0.81111111 1 Recent
## 285 1.2500000 -0.27892944 2.0794415 0.84444444 1 Recent
## 286 5.0000000 -8.04718956 0.6931472 0.10000000 0 Never
## 287 2.5000000 -2.29072683 1.3862944 0.52222222 0 Never
## 288 3.3333333 -4.01324268 1.0986123 0.42222222 0 Never
## 289 2.5000000 -2.29072683 1.3862944 0.22222222 0 Never
## 290 2.5000000 -2.29072683 1.3862944 0.97777778 0 Never
## 291 10.0000000 -23.02585093 0.0000000 0.57777778 0 Previous
## 292 0.6250000 0.29375227 2.7725887 0.02777778 1 Recent
## 293 1.4285714 -0.50953563 1.9459101 0.99444444 0 Never
## 294 1.1111111 -0.11706724 2.1972246 0.19444444 1 Recent
## 295 5.0000000 -8.04718956 0.6931472 0.13333333 0 Previous
## 296 0.2777778 0.35581496 3.5835189 0.45555556 1 Recent
## 297 2.5000000 -2.29072683 1.3862944 0.15555556 0 Never
## 298 10.0000000 -23.02585093 0.0000000 0.45000000 0 Never
## 299 3.3333333 -4.01324268 1.0986123 0.02222222 0 Never
## 300 2.0000000 -1.38629436 1.6094379 0.53888889 1 Recent
## 301 10.0000000 -23.02585093 0.0000000 0.43333333 0 Never
## 302 2.5000000 -2.29072683 1.3862944 1.00555556 0 Never
## 303 5.0000000 -8.04718956 0.6931472 0.16111111 0 Previous
## 304 5.0000000 -8.04718956 0.6931472 0.77222222 0 Never
## 305 3.3333333 -4.01324268 1.0986123 0.84444444 1 Recent
## 306 1.6666667 -0.85137604 1.7917595 0.50000000 0 Previous
## 307 2.0000000 -1.38629436 1.6094379 0.34444444 1 Recent
## 308 0.7142857 0.24033731 2.6390573 0.61111111 0 Previous
## 309 0.6666667 0.27031007 2.7080502 0.08333333 0 Never
## 310 1.6666667 -0.85137604 1.7917595 0.37777778 0 Never
## 311 10.0000000 -23.02585093 0.0000000 0.10555556 0 Never
## 312 1.4285714 -0.50953563 1.9459101 0.25555556 1 Recent
## 313 0.3225806 0.36496842 3.4339872 1.02222222 1 Recent
## 314 1.1111111 -0.11706724 2.1972246 1.04444444 1 Recent
## 315 1.4285714 -0.50953563 1.9459101 0.34444444 1 Recent
## 316 2.5000000 -2.29072683 1.3862944 0.31111111 0 Previous
## 317 0.9090909 0.08664562 2.3978953 0.64444444 1 Recent
## 318 1.4285714 -0.50953563 1.9459101 1.25555556 1 Recent
## 319 2.5000000 -2.29072683 1.3862944 0.77777778 0 Never
## 320 2.0000000 -1.38629436 1.6094379 1.00000000 1 Recent
## 321 1.4285714 -0.50953563 1.9459101 0.61111111 1 Recent
## 322 1.6666667 -0.85137604 1.7917595 0.98888889 1 Recent
## 323 1.2500000 -0.27892944 2.0794415 0.78888889 1 Recent
## 324 1.6666667 -0.85137604 1.7917595 0.93333333 0 Never
## 325 2.0000000 -1.38629436 1.6094379 0.86666667 0 Never
## 326 2.0000000 -1.38629436 1.6094379 0.66666667 0 Never
## 327 1.4285714 -0.50953563 1.9459101 0.91111111 1 Recent
## 328 1.6666667 -0.85137604 1.7917595 0.90000000 0 Previous
## 329 2.5000000 -2.29072683 1.3862944 0.19444444 1 Recent
## 330 0.4347826 0.36213440 3.1354942 0.08888889 1 Recent
## 331 5.0000000 -8.04718956 0.6931472 0.03888889 0 Previous
## 332 2.0000000 -1.38629436 1.6094379 0.16666667 0 Never
## 333 2.5000000 -2.29072683 1.3862944 0.58888889 0 Never
## 334 0.9090909 0.08664562 2.3978953 0.96666667 0 Never
## 335 5.0000000 -8.04718956 0.6931472 0.80000000 0 Never
## 336 5.0000000 -8.04718956 0.6931472 0.13333333 0 Previous
## 337 2.5000000 -2.29072683 1.3862944 0.09444444 0 Previous
## 338 2.0000000 -1.38629436 1.6094379 0.53888889 0 Never
## 339 10.0000000 -23.02585093 0.0000000 0.14444444 1 Recent
## 340 3.3333333 -4.01324268 1.0986123 0.17222222 0 Never
## 341 2.5000000 -2.29072683 1.3862944 0.15555556 1 Recent
## 342 3.3333333 -4.01324268 1.0986123 0.83333333 1 Recent
## 343 1.4285714 -0.50953563 1.9459101 0.22222222 1 Recent
## 344 2.0000000 -1.38629436 1.6094379 1.15555556 0 Never
## 345 0.7692308 0.20181866 2.5649494 0.94444444 1 Recent
## 346 10.0000000 -23.02585093 0.0000000 1.22222222 0 Never
## 347 0.5882353 0.31213427 2.8332133 1.11111111 1 Recent
## 348 5.0000000 -8.04718956 0.6931472 0.81111111 1 Recent
## 349 1.4285714 -0.50953563 1.9459101 0.72222222 1 Recent
## 350 0.4761905 0.35330350 3.0445224 0.83333333 1 Recent
## 351 5.0000000 -8.04718956 0.6931472 0.92222222 0 Never
## 352 0.5555556 0.32654815 2.8903718 0.08333333 0 Previous
## 353 0.3225806 0.36496842 3.4339872 0.24444444 1 Recent
## 354 2.5000000 -2.29072683 1.3862944 0.03888889 0 Never
## 355 2.0000000 -1.38629436 1.6094379 0.11111111 1 Recent
## 356 0.7692308 0.20181866 2.5649494 0.97222222 1 Recent
## 357 2.5000000 -2.29072683 1.3862944 0.39444444 0 Never
## 358 3.3333333 -4.01324268 1.0986123 0.14444444 0 Never
## 359 2.5000000 -2.29072683 1.3862944 0.89444444 1 Recent
## 360 1.0000000 0.00000000 2.3025851 0.20000000 0 Never
## 361 1.1111111 -0.11706724 2.1972246 0.16666667 1 Recent
## 362 2.5000000 -2.29072683 1.3862944 0.99444444 0 Previous
## 363 3.3333333 -4.01324268 1.0986123 1.10555556 1 Recent
## 364 2.5000000 -2.29072683 1.3862944 1.01111111 1 Recent
## 365 2.5000000 -2.29072683 1.3862944 1.24444444 0 Never
## 366 1.4285714 -0.50953563 1.9459101 0.08888889 1 Recent
## 367 3.3333333 -4.01324268 1.0986123 0.20000000 0 Never
## 368 1.4285714 -0.50953563 1.9459101 0.22222222 0 Never
## 369 2.5000000 -2.29072683 1.3862944 0.97777778 1 Recent
## 370 1.4285714 -0.50953563 1.9459101 0.97777778 0 Never
## 371 0.4761905 0.35330350 3.0445224 0.84444444 1 Recent
## 372 5.0000000 -8.04718956 0.6931472 0.24444444 0 Never
## 373 2.0000000 -1.38629436 1.6094379 1.22222222 1 Recent
## 374 2.5000000 -2.29072683 1.3862944 0.94444444 1 Recent
## 375 2.5000000 -2.29072683 1.3862944 0.11111111 1 Recent
## 376 0.5555556 0.32654815 2.8903718 0.87222222 1 Recent
## 377 0.3703704 0.36787103 3.2958369 0.73888889 1 Recent
## 378 2.5000000 -2.29072683 1.3862944 0.46111111 0 Never
## 379 2.0000000 -1.38629436 1.6094379 0.84444444 0 Never
## 380 2.0000000 -1.38629436 1.6094379 0.93888889 0 Never
## 381 2.5000000 -2.29072683 1.3862944 0.49444444 0 Previous
## 382 1.6666667 -0.85137604 1.7917595 0.51111111 1 Recent
## 383 2.0000000 -1.38629436 1.6094379 0.11666667 0 Previous
## 384 0.3225806 0.36496842 3.4339872 0.17222222 1 Recent
## 385 5.0000000 -8.04718956 0.6931472 0.17222222 0 Never
## 386 0.5882353 0.31213427 2.8332133 0.73888889 1 Recent
## 387 2.0000000 -1.38629436 1.6094379 0.85000000 1 Recent
## 388 10.0000000 -23.02585093 0.0000000 1.00000000 0 Never
## 389 0.4761905 0.35330350 3.0445224 1.13333333 1 Recent
## 390 1.2500000 -0.27892944 2.0794415 0.94444444 1 Recent
## 391 5.0000000 -8.04718956 0.6931472 0.98888889 0 Never
## 392 0.7142857 0.24033731 2.6390573 0.31111111 1 Recent
## 393 5.0000000 -8.04718956 0.6931472 1.00000000 1 Recent
## 394 2.5000000 -2.29072683 1.3862944 0.93333333 0 Previous
## 395 2.5000000 -2.29072683 1.3862944 0.94444444 1 Recent
## 396 1.2500000 -0.27892944 2.0794415 0.40000000 1 Recent
## 397 0.9090909 0.08664562 2.3978953 0.82222222 1 Recent
## 398 10.0000000 -23.02585093 0.0000000 0.46666667 1 Recent
## 399 5.0000000 -8.04718956 0.6931472 1.00000000 1 Recent
## 400 10.0000000 -23.02585093 0.0000000 1.20000000 1 Recent
## 401 2.5000000 -2.29072683 1.3862944 0.54444444 1 Recent
## 402 5.0000000 -8.04718956 0.6931472 2.43333333 1 Recent
## 403 1.4285714 -0.50953563 1.9459101 1.20000000 1 Recent
## 404 10.0000000 -23.02585093 0.0000000 1.97777778 0 Never
## 405 3.3333333 -4.01324268 1.0986123 0.46666667 0 Previous
## 406 1.2500000 -0.27892944 2.0794415 2.02222222 1 Recent
## 407 1.6666667 -0.85137604 1.7917595 0.06666667 0 Never
## 408 2.5000000 -2.29072683 1.3862944 1.95000000 0 Previous
## 409 5.0000000 -8.04718956 0.6931472 0.06666667 0 Never
## 410 10.0000000 -23.02585093 0.0000000 0.03333333 1 Recent
## 411 5.0000000 -8.04718956 0.6931472 0.50555556 0 Never
## 412 5.0000000 -8.04718956 0.6931472 1.36111111 0 Never
## 413 5.0000000 -8.04718956 0.6931472 2.06666667 0 Previous
## 414 10.0000000 -23.02585093 0.0000000 1.21111111 0 Never
## 415 10.0000000 -23.02585093 0.0000000 0.25555556 0 Previous
## 416 1.4285714 -0.50953563 1.9459101 2.01666667 1 Recent
## 417 10.0000000 -23.02585093 0.0000000 0.73888889 0 Never
## 418 5.0000000 -8.04718956 0.6931472 0.03888889 1 Recent
## 419 10.0000000 -23.02585093 0.0000000 0.62222222 0 Previous
## 420 3.3333333 -4.01324268 1.0986123 0.23333333 0 Previous
## 421 5.0000000 -8.04718956 0.6931472 1.87777778 1 Recent
## 422 1.4285714 -0.50953563 1.9459101 0.31111111 1 Recent
## 423 0.4761905 0.35330350 3.0445224 0.52222222 1 Recent
## 424 1.6666667 -0.85137604 1.7917595 0.22222222 1 Recent
## 425 2.0000000 -1.38629436 1.6094379 1.95555556 1 Recent
## 426 10.0000000 -23.02585093 0.0000000 0.36666667 0 Previous
## 427 1.4285714 -0.50953563 1.9459101 0.30555556 0 Never
## 428 1.2500000 -0.27892944 2.0794415 1.91111111 0 Previous
## 429 10.0000000 -23.02585093 0.0000000 0.85000000 0 Never
## 430 1.1111111 -0.11706724 2.1972246 2.04444444 0 Previous
## 431 10.0000000 -23.02585093 0.0000000 2.03333333 0 Never
## 432 2.5000000 -2.29072683 1.3862944 0.24444444 0 Previous
## 433 10.0000000 -23.02585093 0.0000000 2.03333333 0 Never
## 434 10.0000000 -23.02585093 0.0000000 1.55555556 0 Never
## 435 3.3333333 -4.01324268 1.0986123 0.21111111 0 Never
## 436 0.7692308 0.20181866 2.5649494 2.04444444 1 Recent
## 437 10.0000000 -23.02585093 0.0000000 0.55555556 0 Never
## 438 3.3333333 -4.01324268 1.0986123 1.46666667 0 Never
## 439 5.0000000 -8.04718956 0.6931472 1.42222222 1 Recent
## 440 5.0000000 -8.04718956 0.6931472 0.59444444 0 Previous
## 441 10.0000000 -23.02585093 0.0000000 2.04444444 0 Never
## 442 2.0000000 -1.38629436 1.6094379 1.21666667 1 Recent
## 443 1.6666667 -0.85137604 1.7917595 2.07777778 0 Previous
## 444 5.0000000 -8.04718956 0.6931472 0.51111111 0 Never
## 445 10.0000000 -23.02585093 0.0000000 0.25000000 0 Never
## 446 10.0000000 -23.02585093 0.0000000 2.03333333 0 Never
## 447 3.3333333 -4.01324268 1.0986123 2.04444444 1 Recent
## 448 5.0000000 -8.04718956 0.6931472 0.86666667 0 Never
## 449 10.0000000 -23.02585093 0.0000000 2.04444444 0 Never
## 450 3.3333333 -4.01324268 1.0986123 2.07777778 0 Previous
## 451 3.3333333 -4.01324268 1.0986123 1.12222222 1 Recent
## 452 10.0000000 -23.02585093 0.0000000 1.56666667 0 Never
## 453 5.0000000 -8.04718956 0.6931472 0.26666667 0 Never
## 454 0.2439024 0.34414316 3.7135721 0.40000000 0 Previous
## 455 10.0000000 -23.02585093 0.0000000 0.31111111 0 Never
## 456 1.1111111 -0.11706724 2.1972246 2.03888889 0 Previous
## 457 3.3333333 -4.01324268 1.0986123 0.38888889 0 Never
## 458 1.4285714 -0.50953563 1.9459101 0.32222222 0 Previous
## 459 2.0000000 -1.38629436 1.6094379 2.03333333 0 Never
## 460 1.4285714 -0.50953563 1.9459101 0.05555556 1 Recent
## 461 3.3333333 -4.01324268 1.0986123 2.37777778 1 Recent
## 462 1.1111111 -0.11706724 2.1972246 2.18888889 0 Previous
## 463 10.0000000 -23.02585093 0.0000000 0.98888889 0 Never
## 464 5.0000000 -8.04718956 0.6931472 0.62222222 0 Never
## 465 5.0000000 -8.04718956 0.6931472 0.10000000 0 Never
## 466 10.0000000 -23.02585093 0.0000000 2.06666667 0 Never
## 467 5.0000000 -8.04718956 0.6931472 1.68333333 0 Previous
## 468 2.5000000 -2.29072683 1.3862944 0.17777778 0 Never
## 469 2.0000000 -1.38629436 1.6094379 0.04444444 0 Previous
## 470 5.0000000 -8.04718956 0.6931472 0.35000000 0 Never
## 471 0.4761905 0.35330350 3.0445224 1.20000000 0 Previous
## 472 5.0000000 -8.04718956 0.6931472 2.03333333 0 Previous
## 473 5.0000000 -8.04718956 0.6931472 0.83888889 0 Never
## 474 10.0000000 -23.02585093 0.0000000 0.07777778 0 Never
## 475 3.3333333 -4.01324268 1.0986123 0.42222222 0 Never
## 476 3.3333333 -4.01324268 1.0986123 0.97777778 0 Never
## 477 1.2500000 -0.27892944 2.0794415 0.51666667 1 Recent
## 478 2.5000000 -2.29072683 1.3862944 2.22222222 1 Recent
## 479 10.0000000 -23.02585093 0.0000000 1.97777778 0 Previous
## 480 10.0000000 -23.02585093 0.0000000 0.43333333 0 Never
## 481 0.9090909 0.08664562 2.3978953 0.66111111 0 Previous
## 482 3.3333333 -4.01324268 1.0986123 1.71111111 0 Previous
## 483 3.3333333 -4.01324268 1.0986123 0.90555556 1 Recent
## 484 1.2500000 -0.27892944 2.0794415 0.65555556 0 Previous
## 485 10.0000000 -23.02585093 0.0000000 0.42222222 0 Never
## 486 10.0000000 -23.02585093 0.0000000 0.64444444 0 Never
## 487 2.5000000 -2.29072683 1.3862944 0.97777778 1 Recent
## 488 5.0000000 -8.04718956 0.6931472 0.36666667 0 Never
## 489 3.3333333 -4.01324268 1.0986123 0.38888889 1 Recent
## 490 3.3333333 -4.01324268 1.0986123 0.37777778 0 Never
## 491 0.3846154 0.36750440 3.2580965 2.12222222 0 Previous
## 492 1.6666667 -0.85137604 1.7917595 0.38888889 0 Never
## 493 5.0000000 -8.04718956 0.6931472 0.17777778 0 Never
## 494 10.0000000 -23.02585093 0.0000000 0.31111111 0 Never
## 495 2.5000000 -2.29072683 1.3862944 0.16666667 0 Never
## 496 1.2500000 -0.27892944 2.0794415 0.07777778 1 Recent
## 497 10.0000000 -23.02585093 0.0000000 0.47777778 0 Never
## 498 1.6666667 -0.85137604 1.7917595 0.98888889 0 Previous
## 499 5.0000000 -8.04718956 0.6931472 0.42222222 0 Never
## 500 2.5000000 -2.29072683 1.3862944 2.26666667 1 Recent
## 501 3.3333333 -4.01324268 1.0986123 0.84444444 0 Never
## 502 2.5000000 -2.29072683 1.3862944 2.16666667 0 Never
## 503 5.0000000 -8.04718956 0.6931472 2.04444444 0 Never
## 504 5.0000000 -8.04718956 0.6931472 1.41111111 0 Previous
## 505 10.0000000 -23.02585093 0.0000000 2.06111111 0 Never
## 506 5.0000000 -8.04718956 0.6931472 2.17777778 0 Never
## 507 3.3333333 -4.01324268 1.0986123 2.20000000 0 Never
## 508 3.3333333 -4.01324268 1.0986123 1.88888889 1 Recent
## 509 2.0000000 -1.38629436 1.6094379 0.27777778 1 Recent
## 510 10.0000000 -23.02585093 0.0000000 0.90555556 0 Never
## 511 0.8333333 0.15193463 2.4849066 2.02222222 0 Never
## 512 2.5000000 -2.29072683 1.3862944 1.66666667 0 Never
## 513 1.2500000 -0.27892944 2.0794415 0.18888889 1 Recent
## 514 2.0000000 -1.38629436 1.6094379 0.18888889 1 Recent
## 515 2.5000000 -2.29072683 1.3862944 2.03333333 0 Never
## 516 1.2500000 -0.27892944 2.0794415 1.47777778 0 Previous
## 517 2.5000000 -2.29072683 1.3862944 0.76666667 0 Previous
## 518 1.4285714 -0.50953563 1.9459101 2.03333333 1 Recent
## 519 1.4285714 -0.50953563 1.9459101 0.07777778 1 Recent
## 520 3.3333333 -4.01324268 1.0986123 2.04444444 0 Previous
## 521 0.6250000 0.29375227 2.7725887 0.49444444 1 Recent
## 522 1.6666667 -0.85137604 1.7917595 2.03333333 0 Never
## 523 0.7142857 0.24033731 2.6390573 1.96666667 1 Recent
## 524 0.9090909 0.08664562 2.3978953 0.85555556 1 Recent
## 525 0.4761905 0.35330350 3.0445224 1.36666667 0 Previous
## 526 5.0000000 -8.04718956 0.6931472 1.62222222 0 Previous
## 527 0.9090909 0.08664562 2.3978953 1.12777778 0 Never
## 528 1.4285714 -0.50953563 1.9459101 2.00000000 1 Recent
## 529 3.3333333 -4.01324268 1.0986123 0.87777778 0 Never
## 530 1.6666667 -0.85137604 1.7917595 1.11666667 0 Previous
## 531 10.0000000 -23.02585093 0.0000000 0.71666667 0 Previous
## 532 5.0000000 -8.04718956 0.6931472 2.02777778 0 Never
## 533 2.0000000 -1.38629436 1.6094379 0.88333333 0 Never
## 534 3.3333333 -4.01324268 1.0986123 1.96666667 0 Previous
## 535 5.0000000 -8.04718956 0.6931472 0.39444444 1 Recent
## 536 3.3333333 -4.01324268 1.0986123 0.60000000 1 Recent
## 537 2.0000000 -1.38629436 1.6094379 1.10000000 0 Previous
## 538 5.0000000 -8.04718956 0.6931472 2.06666667 0 Never
## 539 1.4285714 -0.50953563 1.9459101 0.27777778 0 Previous
## 540 10.0000000 -23.02585093 0.0000000 0.26666667 0 Never
## 541 2.5000000 -2.29072683 1.3862944 2.12222222 0 Never
## 542 1.6666667 -0.85137604 1.7917595 0.95000000 0 Previous
## 543 5.0000000 -8.04718956 0.6931472 0.80555556 0 Never
## 544 0.4761905 0.35330350 3.0445224 2.03333333 0 Never
## 545 2.5000000 -2.29072683 1.3862944 0.80000000 0 Never
## 546 5.0000000 -8.04718956 0.6931472 0.24444444 0 Previous
## 547 3.3333333 -4.01324268 1.0986123 0.77777778 0 Never
## 548 10.0000000 -23.02585093 0.0000000 2.04444444 0 Never
## 549 3.3333333 -4.01324268 1.0986123 1.04444444 0 Never
## 550 1.1111111 -0.11706724 2.1972246 1.64444444 0 Previous
## 551 10.0000000 -23.02585093 0.0000000 0.25555556 0 Never
## 552 5.0000000 -8.04718956 0.6931472 1.42222222 0 Never
## 553 10.0000000 -23.02585093 0.0000000 1.17777778 0 Never
## 554 10.0000000 -23.02585093 0.0000000 0.51111111 0 Previous
## 555 10.0000000 -23.02585093 0.0000000 0.83333333 0 Never
## 556 3.3333333 -4.01324268 1.0986123 0.26666667 0 Never
## 557 2.5000000 -2.29072683 1.3862944 0.32222222 1 Recent
## 558 3.3333333 -4.01324268 1.0986123 1.98888889 0 Previous
## 559 2.0000000 -1.38629436 1.6094379 1.88888889 1 Recent
## 560 5.0000000 -8.04718956 0.6931472 2.02777778 0 Never
## 561 2.0000000 -1.38629436 1.6094379 2.22222222 0 Previous
## 562 10.0000000 -23.02585093 0.0000000 0.62222222 0 Never
## 563 1.6666667 -0.85137604 1.7917595 0.26666667 0 Previous
## 564 1.4285714 -0.50953563 1.9459101 0.11111111 0 Previous
## 565 0.4545455 0.35838971 3.0910425 1.96666667 1 Recent
## 566 10.0000000 -23.02585093 0.0000000 1.28888889 0 Never
## 567 0.8333333 0.15193463 2.4849066 0.30000000 0 Previous
## 568 10.0000000 -23.02585093 0.0000000 0.26666667 0 Never
## 569 2.0000000 -1.38629436 1.6094379 0.63333333 0 Previous
## 570 10.0000000 -23.02585093 0.0000000 0.51111111 0 Never
## 571 1.4285714 -0.50953563 1.9459101 0.43333333 0 Previous
## 572 2.0000000 -1.38629436 1.6094379 0.18888889 1 Recent
## 573 2.5000000 -2.29072683 1.3862944 0.11666667 0 Previous
## 574 3.3333333 -4.01324268 1.0986123 2.04444444 1 Recent
## 575 0.6250000 0.29375227 2.7725887 0.05000000 1 Recent## Rows: 575
## Columns: 19
## $ ID <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, …
## $ AGE <dbl> 39, 33, 33, 32, 24, 30, 39, 27, 40, 36, 38, 29, 32, 41, 31, 27,…
## $ BECK <dbl> 9.000, 34.000, 10.000, 20.000, 5.000, 32.550, 19.000, 10.000, 2…
## $ HC <dbl> 4, 4, 2, 4, 2, 3, 4, 4, 2, 2, 2, 3, 3, 1, 1, 2, 1, 4, 3, 2, 3, …
## $ IV <dbl> 3, 2, 3, 3, 1, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 2, 1, 3, 1, …
## $ NDT <dbl> 1, 8, 3, 1, 5, 1, 34, 2, 3, 7, 8, 1, 2, 8, 1, 3, 6, 1, 15, 5, 1…
## $ RACE <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ TREAT <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, …
## $ SITE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LEN.T <dbl> 123, 25, 7, 66, 173, 16, 179, 21, 176, 124, 176, 79, 182, 174, …
## $ TIME <dbl> 188, 26, 207, 144, 551, 32, 459, 22, 210, 184, 212, 87, 598, 26…
## $ CENSOR <dbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, …
## $ Y <dbl> 5.236442, 3.258097, 5.332719, 4.969813, 6.311735, 3.465736, 6.1…
## $ ND1 <dbl> 5.0000000, 1.1111111, 2.5000000, 5.0000000, 1.6666667, 5.000000…
## $ ND2 <dbl> -8.0471896, -0.1170672, -2.2907268, -8.0471896, -0.8513760, -8.…
## $ LNDT <dbl> 0.6931472, 2.1972246, 1.3862944, 0.6931472, 1.7917595, 0.693147…
## $ FRAC <dbl> 0.68333333, 0.13888889, 0.03888889, 0.73333333, 0.96111111, 0.0…
## $ IV3 <dbl> 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, …
## $ IV_fct <fct> Recent, Previous, Recent, Recent, Never, Recent, Recent, Recent…Let’s look at the three groups in our data defined by the IV variable. These are people who have never used IV drugs, those who have previously used IV drugs, and those who have recently used IV drugs. The following table shows how many people are in each group.
## IV_fct n percent
## Never 223 0.3878261
## Previous 109 0.1895652
## Recent 243 0.4226087
## Total 575 1.0000000We’re interested in depression as measured by the Beck Depression Inventory.
Exercise 2
Search the internet for the Beck Depression Inventory. (This search is much easier than for Exercise 1.) Write a short paragraph about it and how it purports to measure depression.
Please write up your answer here.
A useful graph is a side-by-side boxplot.
This boxplot shows that the distribution of depression scores is similar across the groups. There are some small differences, but it’s not clear if these differences are statistically significant.
We can get the overall mean of all Beck scores, sometimes called the “grand mean”.
## mean(BECK)
## 1 17.36743If we use group_by, we can separate this out by IV group:
## # A tibble: 3 × 2
## IV_fct `mean(BECK)`
## <fct> <dbl>
## 1 Never 15.9
## 2 Previous 16.6
## 3 Recent 19.0Exericse 3
We have to be careful about the term “grand mean”. In some contexts, the term “grand mean” refers to the mean of all scores in the response variable (17.36743 above). In other cases, the term refers to the mean of the three group means (the mean of 15.94996, 16.64201, and 18.99363).
First calculate the mean of the three group means above. (You can use R to do this if you want, or you can just use a calculator.) Explain mathematically why the overall mean 17.36743 is not the same as the mean of the three group means. What would have to be true of the sample for the overall mean to agree with the mean of the three group means? (Hint: think about the size of each of the three groups.)
Please write up your answer here.
22.5 The F distribution
To keep the exposition simple here, we’ll assume that the term “grand mean” refers to the overall mean of the response variable, 17.36743.
When assessing the differences among groups, there are two numbers that are important.
The first is called the “mean square between groups” (MSG). It measures how far away each group mean is away from the overall grand mean for the whole sample. For example, for those who never used IV drugs, their mean Beck score was 15.95. This is 1.42 points below the grand mean of 17.37. On the other hand, recent IV drug users had a mean Beck score of nearly 19. This is 1.63 points above the grand mean. MSG is calculated by taking these differences for each group, squaring them to make them positive, weighting them by the sizes of each group (larger groups should obviously count for more), and dividing by the “group degrees of freedom” \(df_{G} = k - 1\) where \(k\) is the number of groups. The idea is that MSG is a kind of “average variability” among the groups. In other words, how far away are the groups from the grand mean (and therefore, from each other)?
The second number of interest is the “mean square error” (MSE). It is a measure of variability within groups. In other words, it measures how far away data points are from their own group means. Even under the assumption of a null hypothesis that says all the groups should be the same, we still expect some variability. Its calculation also involves dividing by some degrees of freedom, but now it is \(df_{E} = n - k\).
All that is somewhat technical and complicated. We’ll leave it to the computer. The key insight comes from considering the ratio of \(MSG\) and \(MSE\). We will call this quantity F:
\[ F = \frac{MSG}{MSE}. \]
What can be said about this magical F? Under the assumption of the null hypothesis, we expect some variability among the groups, and we expect some variability within each group as well, but these two sources of variability should be about the same. In other words, \(MSG\) should be roughly equal to \(MSE\). Therefore, F ought to be close to 1.
We can simulate this using the infer package. Suppose that there were no difference in the mean BECK scores among the three groups. We can accomplish this by shuffling the IV labels, an idea we’ve seen several times before in this book. Permuting the IV values breaks any association that might have existed in the original data.
set.seed(420)
BECK_IV_test_sim <- uis2 %>%
specify(response = BECK, explanatory = IV_fct) %>%
hypothesize(null = "independence") %>%
generate(reps = 1000, type = "permute") %>%
calculate(stat = "F")
BECK_IV_test_sim## Response: BECK (numeric)
## Explanatory: IV_fct (factor)
## Null Hypothesis: independence
## # A tibble: 1,000 × 2
## replicate stat
## <int> <dbl>
## 1 1 0.616
## 2 2 2.36
## 3 3 1.38
## 4 4 2.64
## 5 5 0.333
## 6 6 0.732
## 7 7 1.33
## 8 8 0.261
## 9 9 1.31
## 10 10 0.616
## # … with 990 more rows
As explained earlier, the F scores are clustered around 1. They can never be smaller than zero. (The bar at zero is centered on zero, but no F score can be less than zero.) There are occasional F scores much larger than 1, but just by chance.
It’s not particularly interesting if F is less than one. That just means that the variability between groups is small and the variability of the data within each group is large. That doesn’t allow us to conclude that there is a difference among groups. However, if F is really large, that means that there is much more variability between the groups than there is within each group. Therefore, the groups are far apart and there is evidence of a difference among groups.
\(MSG\) and \(MSE\) are measures of variability, and that’s why this is called “Analysis of Variance”.
The F distribution is the correct sampling distribution model. Like a t model, there are infinitely many different F models because degrees of freedom are involved. But unlike a t model, the F model has two numbers called degrees of freedom, \(df_{G}\) and \(df_{E}\). Both of these numbers affect the precise shape of the F distribution.
For example, here is picture of a few different F models.
# Don't worry about the syntax here.
# You won't need to know how to do this on your own.
ggplot(data.frame(x = c(0, 5)), aes(x)) +
stat_function(fun = df, args = list(df1 = 2, df2 = 5),
aes(color = "2, 5")) +
stat_function(fun = df, args = list(df1 = 2, df2 = 50),
aes(color = "2, 50" )) +
stat_function(fun = df, args = list(df1 = 10, df2 = 50),
aes(color = "10, 50")) +
scale_color_manual(name = expression(paste(df[G], ", ", df[E])),
values = c("2, 5" = "red",
"2, 50" = "blue",
"10, 50" = "green"),
breaks = c("2, 5", "2, 50", "10, 50"))
Here is the theoretical F distribution for our data:
BECK_IV_test <- uis2 %>%
specify(response = BECK, explanatory = IV_fct) %>%
hypothesize(null = "independence") %>%
assume(distribution = "F")
BECK_IV_test## An F distribution with 2 and 572 degrees of freedom.Exercise 4
Explain why there are 2 and 572 degrees of freedom. Which one is \(df_{G}\) and which one is \(df_{E}\)?
Please write up your answer here.
Here are the simulated values again, but with the theoretical F distribution superimposed for comparison.
## Warning: Check to make sure the conditions have been met for the theoretical
## method. {infer} currently does not check these for you.
Other than the very left edge, the theoretical curve is a good fit to the simulated F scores.
22.6 Assumptions
What conditions can we check to justify the use of an F model for our sampling distribution? In addition to the typical “Random” and “10%” conditions that ensure independence, we also need to check the “Nearly normal” condition for each group, just like for the t tests. A new assumption is the “Constant variance” assumption, which says that each group should have the same variance in the population. This is impossible to check, although we can use our sample as a rough guide. If each group has about the same spread, that is some evidence that such an assumption might hold in the population as well. Also, ANOVA is pretty robust to this assumption, especially when the groups are close to the same size. Even when the group sizes are unequal (sometimes called “unbalanced”), some say the variances can be off by up to a factor of 3 and ANOVA will still work pretty well. So what we’re looking for here are gross violations, not minor ones.
Let’s go through the rubric with commentary.
22.7 Exploratory data analysis
22.7.1 Use data documentation (help files, code books, Google, etc.) to determine as much as possible about the data provenance and structure.
You should have researched this extensively in a previous exercise.
## ID AGE BECK HC IV NDT RACE TREAT SITE LEN.T TIME CENSOR Y
## 1 1 39 9.000 4 3 1 0 1 0 123 188 1 5.236442
## 2 2 33 34.000 4 2 8 0 1 0 25 26 1 3.258097
## 3 3 33 10.000 2 3 3 0 1 0 7 207 1 5.332719
## 4 4 32 20.000 4 3 1 0 0 0 66 144 1 4.969813
## 5 5 24 5.000 2 1 5 1 1 0 173 551 0 6.311735
## 6 6 30 32.550 3 3 1 0 1 0 16 32 1 3.465736
## 7 7 39 19.000 4 3 34 0 1 0 179 459 1 6.129050
## 8 8 27 10.000 4 3 2 0 1 0 21 22 1 3.091042
## 9 9 40 29.000 2 3 3 0 1 0 176 210 1 5.347108
## 10 10 36 25.000 2 3 7 0 1 0 124 184 1 5.214936
## 11 12 38 18.900 2 3 8 0 1 0 176 212 1 5.356586
## 12 13 29 16.000 3 1 1 0 1 0 79 87 1 4.465908
## 13 14 32 36.000 3 3 2 1 1 0 182 598 0 6.393591
## 14 15 41 19.000 1 3 8 0 1 0 174 260 1 5.560682
## 15 16 31 18.000 1 3 1 0 1 0 181 210 1 5.347108
## 16 17 27 12.000 2 3 3 0 1 0 61 84 1 4.430817
## 17 18 28 34.000 1 3 6 0 1 0 177 196 1 5.278115
## 18 19 28 23.000 4 2 1 0 1 0 19 19 1 2.944439
## 19 20 36 26.000 3 1 15 1 1 0 27 441 1 6.089045
## 20 21 32 18.900 2 3 5 0 1 0 175 449 1 6.107023
## 21 22 33 15.000 3 1 1 0 0 0 12 659 0 6.490724
## 22 23 28 25.200 1 3 8 0 0 0 21 21 1 3.044522
## 23 24 29 6.632 4 2 0 0 0 0 48 53 1 3.970292
## 24 25 35 2.100 2 3 9 0 0 0 90 225 1 5.416100
## 25 26 45 26.000 1 3 6 0 0 0 91 161 1 5.081404
## 26 27 35 39.789 4 3 5 0 0 0 87 87 1 4.465908
## 27 28 24 20.000 3 1 3 0 0 0 88 89 1 4.488636
## 28 29 36 16.000 1 3 7 0 0 0 9 44 1 3.784190
## 29 31 39 22.000 1 3 9 0 0 0 94 523 0 6.259581
## 30 32 36 9.947 4 2 10 0 0 0 91 226 1 5.420535
## 31 33 37 9.450 4 3 1 0 0 0 90 259 1 5.556828
## 32 34 30 39.000 2 3 1 0 0 0 89 289 1 5.666427
## 33 35 44 41.000 1 3 5 0 0 0 89 103 1 4.634729
## 34 36 28 31.000 3 1 6 1 0 0 100 624 0 6.436150
## 35 37 25 20.000 3 1 3 1 0 0 67 68 1 4.219508
## 36 38 30 8.000 2 3 7 0 1 0 25 57 1 4.043051
## 37 39 24 9.000 4 1 1 0 0 0 12 65 1 4.174387
## 38 40 27 20.000 3 1 1 0 0 0 79 79 1 4.369448
## 39 41 30 8.000 3 1 2 1 0 0 79 559 0 6.326149
## 40 42 34 8.000 2 3 0 0 1 0 78 79 1 4.369448
## 41 43 33 23.000 4 2 2 0 1 0 84 87 1 4.465908
## 42 44 34 18.000 3 3 6 0 1 0 91 91 1 4.510860
## 43 45 36 13.000 2 3 1 0 1 0 162 297 1 5.693732
## 44 46 27 23.000 1 3 0 0 1 0 45 45 1 3.806662
## 45 47 35 9.000 4 3 1 1 1 0 61 246 1 5.505332
## 46 48 24 14.000 1 3 0 0 1 0 19 37 1 3.610918
## 47 49 28 23.000 4 1 2 1 1 0 37 37 1 3.610918
## 48 50 46 10.000 1 3 8 0 1 0 51 538 0 6.287859
## 49 51 26 11.000 3 3 1 0 1 0 60 541 0 6.293419
## 50 52 42 16.000 1 3 25 0 1 0 177 184 1 5.214936
## 51 53 30 0.000 3 1 0 0 1 0 43 122 1 4.804021
## 52 55 30 12.000 4 1 3 1 1 0 21 156 1 5.049856
## 53 56 27 21.000 2 3 2 0 0 0 88 121 1 4.795791
## 54 57 38 0.000 1 3 6 0 0 0 96 231 1 5.442418
## 55 58 48 8.000 4 3 10 0 0 0 111 111 1 4.709530
## 56 59 36 25.000 1 3 10 0 0 0 38 38 1 3.637586
## 57 60 28 6.300 3 1 7 0 0 0 15 15 1 2.708050
## 58 61 31 20.000 4 2 5 0 0 0 50 54 1 3.988984
## 59 62 28 4.000 2 3 5 0 0 0 61 127 1 4.844187
## 60 63 28 20.000 3 1 1 0 0 0 31 105 1 4.653960
## 61 64 26 17.000 2 1 2 1 0 0 11 11 1 2.397895
## 62 65 34 3.000 4 3 6 0 0 0 90 153 1 5.030438
## 63 66 26 29.000 2 3 5 0 0 0 11 11 1 2.397895
## 64 68 31 26.000 1 3 5 0 0 0 46 46 1 3.828641
## 65 69 41 12.000 1 3 0 1 0 0 38 655 0 6.484635
## 66 70 30 24.000 4 3 0 0 0 0 90 166 1 5.111988
## 67 72 39 15.750 4 3 5 0 0 0 88 95 1 4.553877
## 68 74 33 9.000 2 3 12 0 0 0 91 151 1 5.017280
## 69 75 33 18.000 4 2 6 0 0 0 85 220 1 5.393628
## 70 76 29 20.000 4 1 0 1 0 0 90 227 1 5.424950
## 71 77 36 17.000 1 3 5 0 0 0 52 343 1 5.837730
## 72 78 26 3.000 4 3 3 0 0 0 88 119 1 4.779123
## 73 79 37 27.000 1 3 13 0 0 0 43 43 1 3.761200
## 74 81 29 31.500 1 3 8 0 0 0 37 47 1 3.850148
## 75 83 30 19.000 3 1 0 1 0 0 87 805 0 6.690842
## 76 84 35 15.000 3 2 2 0 0 0 20 321 1 5.771441
## 77 85 33 22.000 3 1 1 0 0 0 9 167 1 5.117994
## 78 87 36 16.000 2 3 1 0 0 0 85 491 1 6.196444
## 79 88 28 17.000 1 3 2 0 0 0 18 35 1 3.555348
## 80 89 31 32.550 1 3 12 1 0 0 71 123 1 4.812184
## 81 90 23 24.000 1 3 2 0 0 0 88 597 0 6.391917
## 82 91 33 22.000 3 2 1 0 0 0 67 762 0 6.635947
## 83 93 37 18.000 2 3 4 0 0 0 30 31 1 3.433987
## 84 94 25 17.850 3 1 1 0 1 0 68 228 1 5.429346
## 85 95 56 5.000 2 2 9 1 1 0 182 553 0 6.315358
## 86 96 23 39.000 1 3 1 0 1 0 182 190 1 5.247024
## 87 97 26 21.000 3 1 1 0 1 0 146 307 1 5.726848
## 88 98 26 11.000 1 3 1 0 1 0 40 73 1 4.290459
## 89 99 23 14.000 3 1 1 0 1 0 177 208 1 5.337538
## 90 100 28 31.000 4 2 2 1 1 0 181 267 1 5.587249
## 91 102 30 14.000 1 3 15 0 1 0 168 169 1 5.129899
## 92 104 25 6.000 2 3 5 0 1 0 90 655 0 6.484635
## 93 105 33 16.000 1 3 5 0 1 0 61 70 1 4.248495
## 94 106 22 6.000 3 1 3 1 1 0 63 398 1 5.986452
## 95 108 25 20.000 4 2 8 1 1 0 121 122 1 4.804021
## 96 111 38 9.000 3 1 1 1 0 0 89 96 1 4.564348
## 97 112 35 11.000 2 1 3 0 1 0 51 1172 0 7.066467
## 98 113 35 15.000 3 1 1 0 0 0 88 734 0 6.598509
## 99 114 25 13.000 3 3 1 0 0 0 25 26 1 3.258097
## 100 115 33 31.000 3 1 3 1 0 0 83 84 1 4.430817
## 101 116 30 5.000 3 1 2 1 0 0 89 171 1 5.141664
## 102 117 45 10.000 2 3 1 0 0 0 24 159 1 5.068904
## 103 119 42 23.000 2 3 20 0 0 0 7 7 1 1.945910
## 104 120 29 16.000 4 1 1 1 0 0 85 763 0 6.637258
## 105 121 24 37.800 3 1 0 0 0 0 89 104 1 4.644391
## 106 122 33 10.000 2 3 4 0 0 0 91 162 1 5.087596
## 107 123 32 9.000 3 1 0 0 0 0 89 90 1 4.499810
## 108 124 26 15.000 3 1 0 0 0 0 82 373 1 5.921578
## 109 125 28 2.000 1 3 3 0 0 0 84 115 1 4.744932
## 110 127 37 34.000 2 3 1 0 0 0 30 30 1 3.401197
## 111 128 23 11.000 4 1 6 0 0 0 7 8 1 2.079442
## 112 129 40 31.000 2 3 3 1 0 0 84 168 1 5.123964
## 113 130 36 36.750 3 3 0 0 0 0 70 70 1 4.248495
## 114 131 23 26.000 3 2 2 0 0 0 76 130 1 4.867534
## 115 132 35 5.000 4 1 1 1 0 0 89 285 1 5.652489
## 116 133 25 19.000 2 3 1 0 1 0 178 569 0 6.343880
## 117 134 35 21.000 2 3 6 0 1 0 87 87 1 4.465908
## 118 135 46 1.000 4 2 0 0 1 0 175 310 1 5.736572
## 119 136 32 6.000 4 1 3 0 1 0 87 87 1 4.465908
## 120 137 35 23.000 3 1 16 1 1 0 110 544 0 6.298949
## 121 138 34 38.000 3 3 1 0 1 0 21 156 1 5.049856
## 122 139 43 24.000 3 1 3 0 1 0 139 658 0 6.489205
## 123 140 39 3.000 4 3 15 0 1 0 181 273 1 5.609472
## 124 141 27 16.800 4 3 2 1 1 0 33 168 1 5.123964
## 125 142 38 35.000 1 3 1 0 1 0 39 83 1 4.418841
## 126 143 37 11.000 2 3 7 0 1 0 4 4 1 1.386294
## 127 144 44 2.000 1 3 4 1 1 0 184 708 0 6.562444
## 128 145 25 16.000 4 1 1 1 1 0 123 137 1 4.919981
## 129 146 34 15.000 3 1 1 0 1 0 176 259 1 5.556828
## 130 147 34 11.000 3 3 2 1 1 0 174 560 0 6.327937
## 131 148 38 11.000 1 3 1 1 1 0 181 586 0 6.373320
## 132 149 24 22.000 2 3 2 1 1 0 113 190 1 5.247024
## 133 151 42 18.000 2 3 3 0 1 0 164 544 0 6.298949
## 134 153 34 29.000 4 3 1 1 0 0 84 494 1 6.202536
## 135 154 45 27.000 1 3 8 0 0 0 80 541 0 6.293419
## 136 155 40 16.000 2 3 4 0 0 0 91 94 1 4.543295
## 137 156 27 9.000 4 1 3 1 0 0 97 567 0 6.340359
## 138 157 24 0.000 4 1 3 0 0 0 51 55 1 4.007333
## 139 158 27 15.000 1 3 3 0 0 0 91 93 1 4.532599
## 140 159 34 24.000 3 1 4 0 0 0 90 276 1 5.620401
## 141 160 36 3.000 2 3 6 0 0 0 46 46 1 3.828641
## 142 162 31 9.000 3 1 1 0 0 0 76 250 1 5.521461
## 143 163 40 5.000 2 3 2 0 0 0 75 106 1 4.663439
## 144 164 40 13.000 1 3 4 1 0 0 91 552 0 6.313548
## 145 165 37 29.000 2 3 5 0 0 0 90 90 1 4.499810
## 146 166 25 11.000 4 3 6 0 0 0 3 203 1 5.313206
## 147 167 41 22.000 2 3 3 1 1 0 8 67 1 4.204693
## 148 168 22 9.000 4 1 1 0 1 0 33 559 1 6.326149
## 149 169 31 18.000 2 3 8 1 1 0 31 106 1 4.663439
## 150 170 29 40.000 1 1 1 1 1 0 174 374 1 5.924256
## 151 171 27 25.000 3 1 2 0 1 0 34 630 0 6.445720
## 152 172 22 26.000 4 2 3 0 1 0 60 61 1 4.110874
## 153 174 37 11.000 1 2 5 1 1 0 78 547 0 6.304449
## 154 175 36 6.000 3 1 2 1 1 0 182 568 0 6.342121
## 155 176 24 20.000 3 1 1 0 1 0 182 490 1 6.194405
## 156 177 28 9.000 4 1 0 1 1 0 78 222 1 5.402677
## 157 178 24 6.000 4 1 1 0 1 0 55 56 1 4.025352
## 158 179 28 0.000 3 1 2 0 1 0 223 282 1 5.641907
## 159 180 24 5.000 3 1 20 1 1 0 25 35 1 3.555348
## 160 181 24 15.000 4 1 0 0 1 0 63 603 0 6.401917
## 161 183 29 14.700 3 1 1 0 1 0 133 148 1 4.997212
## 162 184 37 3.000 1 3 5 1 1 0 154 354 1 5.869297
## 163 185 26 31.000 1 1 2 0 1 0 70 164 1 5.099866
## 164 186 29 14.000 3 2 1 0 1 0 66 94 1 4.543295
## 165 187 29 28.000 2 3 4 0 1 0 40 65 1 4.174387
## 166 188 33 18.000 4 1 1 0 1 0 75 567 0 6.340359
## 167 189 29 12.000 4 2 2 0 1 0 187 634 0 6.452049
## 168 190 32 5.000 1 1 2 1 1 0 183 633 0 6.450470
## 169 192 33 11.000 4 1 8 1 1 0 182 477 1 6.167516
## 170 193 26 21.000 4 2 2 0 1 0 192 436 1 6.077642
## 171 195 24 23.000 2 3 4 1 1 0 162 362 1 5.891644
## 172 196 46 32.000 2 3 2 0 1 0 193 552 0 6.313548
## 173 197 23 26.000 4 1 2 0 1 0 111 144 1 4.969813
## 174 198 40 19.950 4 3 8 0 1 0 182 242 1 5.488938
## 175 199 48 17.000 3 1 4 0 1 0 180 564 0 6.335054
## 176 200 33 16.000 3 1 0 0 1 0 93 299 1 5.700444
## 177 201 21 26.250 4 1 7 0 1 0 167 167 1 5.117994
## 178 202 38 29.000 3 1 2 0 1 0 196 380 1 5.940171
## 179 203 28 23.000 4 2 4 0 1 0 106 120 1 4.787492
## 180 205 39 9.000 1 3 6 0 1 0 158 218 1 5.384495
## 181 206 37 26.000 1 2 1 1 0 0 91 115 1 4.744932
## 182 207 32 22.000 3 1 4 1 0 0 89 224 1 5.411646
## 183 208 39 23.000 3 2 2 1 0 0 89 132 1 4.882802
## 184 209 28 0.000 1 3 10 0 0 0 88 148 1 4.997212
## 185 210 26 30.000 3 1 0 1 0 0 95 593 0 6.385194
## 186 211 31 21.000 1 3 0 0 0 0 5 26 1 3.258097
## 187 213 34 19.000 4 3 8 0 0 0 32 32 1 3.465736
## 188 214 26 28.000 4 2 2 1 0 0 92 292 1 5.676754
## 189 215 29 8.000 4 1 3 0 0 0 66 89 1 4.488636
## 190 217 25 11.000 3 1 8 0 0 0 90 364 1 5.897154
## 191 218 34 15.000 3 2 3 1 0 0 93 142 1 4.955827
## 192 219 32 8.000 3 1 2 0 0 0 89 188 1 5.236442
## 193 221 38 14.000 4 2 0 0 0 0 91 92 1 4.521789
## 194 222 32 7.000 1 3 8 0 0 0 56 56 1 4.025352
## 195 223 31 13.000 2 3 7 0 0 0 90 110 1 4.700480
## 196 224 40 10.000 3 1 3 0 0 0 73 555 0 6.318968
## 197 225 28 17.000 4 1 5 1 0 0 85 220 1 5.393628
## 198 226 40 18.000 1 3 3 0 0 0 23 23 1 3.135494
## 199 227 32 5.000 2 3 3 0 0 0 85 285 1 5.652489
## 200 228 29 20.000 3 3 5 0 0 0 90 90 1 4.499810
## 201 229 25 31.000 3 1 4 0 0 0 53 59 1 4.077537
## 202 230 32 15.000 2 3 2 0 0 0 96 156 1 5.049856
## 203 232 37 4.000 2 2 2 0 0 0 83 142 1 4.955827
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## 564 616 38 15.000 4 2 6 0 0 1 10 10 1 2.302585
## 565 617 41 20.000 3 3 21 0 1 1 354 355 1 5.872118
## 566 618 31 21.000 3 1 0 1 1 1 232 232 1 5.446737
## 567 619 31 23.000 4 2 11 0 1 1 54 68 1 4.219508
## 568 620 37 5.000 4 1 0 1 1 1 48 48 1 3.871201
## 569 621 37 17.000 4 2 4 1 0 1 57 60 1 4.094345
## 570 622 33 13.000 4 1 0 0 0 1 46 50 1 3.912023
## 571 624 53 9.000 4 2 6 0 0 1 39 126 1 4.836282
## 572 625 37 20.000 2 3 4 0 0 1 17 18 1 2.890372
## 573 626 28 10.000 4 2 3 0 1 1 21 35 1 3.555348
## 574 627 35 17.000 1 3 2 0 0 1 184 379 1 5.937536
## 575 628 46 31.500 1 3 15 1 1 1 9 377 1 5.932245
## ND1 ND2 LNDT FRAC IV3
## 1 5.0000000 -8.04718956 0.6931472 0.68333333 1
## 2 1.1111111 -0.11706724 2.1972246 0.13888889 0
## 3 2.5000000 -2.29072683 1.3862944 0.03888889 1
## 4 5.0000000 -8.04718956 0.6931472 0.73333333 1
## 5 1.6666667 -0.85137604 1.7917595 0.96111111 0
## 6 5.0000000 -8.04718956 0.6931472 0.08888889 1
## 7 0.2857143 0.35793228 3.5553481 0.99444444 1
## 8 3.3333333 -4.01324268 1.0986123 0.11666667 1
## 9 2.5000000 -2.29072683 1.3862944 0.97777778 1
## 10 1.2500000 -0.27892944 2.0794415 0.68888889 1
## 11 1.1111111 -0.11706724 2.1972246 0.97777778 1
## 12 5.0000000 -8.04718956 0.6931472 0.43888889 0
## 13 3.3333333 -4.01324268 1.0986123 1.01111111 1
## 14 1.1111111 -0.11706724 2.1972246 0.96666667 1
## 15 5.0000000 -8.04718956 0.6931472 1.00555556 1
## 16 2.5000000 -2.29072683 1.3862944 0.33888889 1
## 17 1.4285714 -0.50953563 1.9459101 0.98333333 1
## 18 5.0000000 -8.04718956 0.6931472 0.10555556 0
## 19 0.6250000 0.29375227 2.7725887 0.15000000 0
## 20 1.6666667 -0.85137604 1.7917595 0.97222222 1
## 21 5.0000000 -8.04718956 0.6931472 0.13333333 0
## 22 1.1111111 -0.11706724 2.1972246 0.23333333 1
## 23 10.0000000 -23.02585093 0.0000000 0.53333333 0
## 24 1.0000000 0.00000000 2.3025851 1.00000000 1
## 25 1.4285714 -0.50953563 1.9459101 1.01111111 1
## 26 1.6666667 -0.85137604 1.7917595 0.96666667 1
## 27 2.5000000 -2.29072683 1.3862944 0.97777778 0
## 28 1.2500000 -0.27892944 2.0794415 0.10000000 1
## 29 1.0000000 0.00000000 2.3025851 1.04444444 1
## 30 0.9090909 0.08664562 2.3978953 1.01111111 0
## 31 5.0000000 -8.04718956 0.6931472 1.00000000 1
## 32 5.0000000 -8.04718956 0.6931472 0.98888889 1
## 33 1.6666667 -0.85137604 1.7917595 0.98888889 1
## 34 1.4285714 -0.50953563 1.9459101 1.11111111 0
## 35 2.5000000 -2.29072683 1.3862944 0.74444444 0
## 36 1.2500000 -0.27892944 2.0794415 0.13888889 1
## 37 5.0000000 -8.04718956 0.6931472 0.13333333 0
## 38 5.0000000 -8.04718956 0.6931472 0.87777778 0
## 39 3.3333333 -4.01324268 1.0986123 0.87777778 0
## 40 10.0000000 -23.02585093 0.0000000 0.43333333 1
## 41 3.3333333 -4.01324268 1.0986123 0.46666667 0
## 42 1.4285714 -0.50953563 1.9459101 0.50555556 1
## 43 5.0000000 -8.04718956 0.6931472 0.90000000 1
## 44 10.0000000 -23.02585093 0.0000000 0.25000000 1
## 45 5.0000000 -8.04718956 0.6931472 0.33888889 1
## 46 10.0000000 -23.02585093 0.0000000 0.10555556 1
## 47 3.3333333 -4.01324268 1.0986123 0.20555556 0
## 48 1.1111111 -0.11706724 2.1972246 0.28333333 1
## 49 5.0000000 -8.04718956 0.6931472 0.33333333 1
## 50 0.3846154 0.36750440 3.2580965 0.98333333 1
## 51 10.0000000 -23.02585093 0.0000000 0.23888889 0
## 52 2.5000000 -2.29072683 1.3862944 0.11666667 0
## 53 3.3333333 -4.01324268 1.0986123 0.97777778 1
## 54 1.4285714 -0.50953563 1.9459101 1.06666667 1
## 55 0.9090909 0.08664562 2.3978953 1.23333333 1
## 56 0.9090909 0.08664562 2.3978953 0.42222222 1
## 57 1.2500000 -0.27892944 2.0794415 0.16666667 0
## 58 1.6666667 -0.85137604 1.7917595 0.55555556 0
## 59 1.6666667 -0.85137604 1.7917595 0.67777778 1
## 60 5.0000000 -8.04718956 0.6931472 0.34444444 0
## 61 3.3333333 -4.01324268 1.0986123 0.12222222 0
## 62 1.4285714 -0.50953563 1.9459101 1.00000000 1
## 63 1.6666667 -0.85137604 1.7917595 0.12222222 1
## 64 1.6666667 -0.85137604 1.7917595 0.51111111 1
## 65 10.0000000 -23.02585093 0.0000000 0.42222222 1
## 66 10.0000000 -23.02585093 0.0000000 1.00000000 1
## 67 1.6666667 -0.85137604 1.7917595 0.97777778 1
## 68 0.7692308 0.20181866 2.5649494 1.01111111 1
## 69 1.4285714 -0.50953563 1.9459101 0.94444444 0
## 70 10.0000000 -23.02585093 0.0000000 1.00000000 0
## 71 1.6666667 -0.85137604 1.7917595 0.57777778 1
## 72 2.5000000 -2.29072683 1.3862944 0.97777778 1
## 73 0.7142857 0.24033731 2.6390573 0.47777778 1
## 74 1.1111111 -0.11706724 2.1972246 0.41111111 1
## 75 10.0000000 -23.02585093 0.0000000 0.96666667 0
## 76 3.3333333 -4.01324268 1.0986123 0.22222222 0
## 77 5.0000000 -8.04718956 0.6931472 0.10000000 0
## 78 5.0000000 -8.04718956 0.6931472 0.94444444 1
## 79 3.3333333 -4.01324268 1.0986123 0.20000000 1
## 80 0.7692308 0.20181866 2.5649494 0.78888889 1
## 81 3.3333333 -4.01324268 1.0986123 0.97777778 1
## 82 5.0000000 -8.04718956 0.6931472 0.74444444 0
## 83 2.0000000 -1.38629436 1.6094379 0.33333333 1
## 84 5.0000000 -8.04718956 0.6931472 0.37777778 0
## 85 1.0000000 0.00000000 2.3025851 1.01111111 0
## 86 5.0000000 -8.04718956 0.6931472 1.01111111 1
## 87 5.0000000 -8.04718956 0.6931472 0.81111111 0
## 88 5.0000000 -8.04718956 0.6931472 0.22222222 1
## 89 5.0000000 -8.04718956 0.6931472 0.98333333 0
## 90 3.3333333 -4.01324268 1.0986123 1.00555556 0
## 91 0.6250000 0.29375227 2.7725887 0.93333333 1
## 92 1.6666667 -0.85137604 1.7917595 0.50000000 1
## 93 1.6666667 -0.85137604 1.7917595 0.33888889 1
## 94 2.5000000 -2.29072683 1.3862944 0.35000000 0
## 95 1.1111111 -0.11706724 2.1972246 0.67222222 0
## 96 5.0000000 -8.04718956 0.6931472 0.98888889 0
## 97 2.5000000 -2.29072683 1.3862944 0.28333333 0
## 98 5.0000000 -8.04718956 0.6931472 0.97777778 0
## 99 5.0000000 -8.04718956 0.6931472 0.27777778 1
## 100 2.5000000 -2.29072683 1.3862944 0.92222222 0
## 101 3.3333333 -4.01324268 1.0986123 0.98888889 0
## 102 5.0000000 -8.04718956 0.6931472 0.26666667 1
## 103 0.4761905 0.35330350 3.0445224 0.07777778 1
## 104 5.0000000 -8.04718956 0.6931472 0.94444444 0
## 105 10.0000000 -23.02585093 0.0000000 0.98888889 0
## 106 2.0000000 -1.38629436 1.6094379 1.01111111 1
## 107 10.0000000 -23.02585093 0.0000000 0.98888889 0
## 108 10.0000000 -23.02585093 0.0000000 0.91111111 0
## 109 2.5000000 -2.29072683 1.3862944 0.93333333 1
## 110 5.0000000 -8.04718956 0.6931472 0.33333333 1
## 111 1.4285714 -0.50953563 1.9459101 0.07777778 0
## 112 2.5000000 -2.29072683 1.3862944 0.93333333 1
## 113 10.0000000 -23.02585093 0.0000000 0.77777778 1
## 114 3.3333333 -4.01324268 1.0986123 0.84444444 0
## 115 5.0000000 -8.04718956 0.6931472 0.98888889 0
## 116 5.0000000 -8.04718956 0.6931472 0.98888889 1
## 117 1.4285714 -0.50953563 1.9459101 0.48333333 1
## 118 10.0000000 -23.02585093 0.0000000 0.97222222 0
## 119 2.5000000 -2.29072683 1.3862944 0.48333333 0
## 120 0.5882353 0.31213427 2.8332133 0.61111111 0
## 121 5.0000000 -8.04718956 0.6931472 0.11666667 1
## 122 2.5000000 -2.29072683 1.3862944 0.77222222 0
## 123 0.6250000 0.29375227 2.7725887 1.00555556 1
## 124 3.3333333 -4.01324268 1.0986123 0.18333333 1
## 125 5.0000000 -8.04718956 0.6931472 0.21666667 1
## 126 1.2500000 -0.27892944 2.0794415 0.02222222 1
## 127 2.0000000 -1.38629436 1.6094379 1.02222222 1
## 128 5.0000000 -8.04718956 0.6931472 0.68333333 0
## 129 5.0000000 -8.04718956 0.6931472 0.97777778 0
## 130 3.3333333 -4.01324268 1.0986123 0.96666667 1
## 131 5.0000000 -8.04718956 0.6931472 1.00555556 1
## 132 3.3333333 -4.01324268 1.0986123 0.62777778 1
## 133 2.5000000 -2.29072683 1.3862944 0.91111111 1
## 134 5.0000000 -8.04718956 0.6931472 0.93333333 1
## 135 1.1111111 -0.11706724 2.1972246 0.88888889 1
## 136 2.0000000 -1.38629436 1.6094379 1.01111111 1
## 137 2.5000000 -2.29072683 1.3862944 1.07777778 0
## 138 2.5000000 -2.29072683 1.3862944 0.56666667 0
## 139 2.5000000 -2.29072683 1.3862944 1.01111111 1
## 140 2.0000000 -1.38629436 1.6094379 1.00000000 0
## 141 1.4285714 -0.50953563 1.9459101 0.51111111 1
## 142 5.0000000 -8.04718956 0.6931472 0.84444444 0
## 143 3.3333333 -4.01324268 1.0986123 0.83333333 1
## 144 2.0000000 -1.38629436 1.6094379 1.01111111 1
## 145 1.6666667 -0.85137604 1.7917595 1.00000000 1
## 146 1.4285714 -0.50953563 1.9459101 0.03333333 1
## 147 2.5000000 -2.29072683 1.3862944 0.04444444 1
## 148 5.0000000 -8.04718956 0.6931472 0.18333333 0
## 149 1.1111111 -0.11706724 2.1972246 0.17222222 1
## 150 5.0000000 -8.04718956 0.6931472 0.96666667 0
## 151 3.3333333 -4.01324268 1.0986123 0.18888889 0
## 152 2.5000000 -2.29072683 1.3862944 0.33333333 0
## 153 1.6666667 -0.85137604 1.7917595 0.43333333 0
## 154 3.3333333 -4.01324268 1.0986123 1.01111111 0
## 155 5.0000000 -8.04718956 0.6931472 1.01111111 0
## 156 10.0000000 -23.02585093 0.0000000 0.43333333 0
## 157 5.0000000 -8.04718956 0.6931472 0.30555556 0
## 158 3.3333333 -4.01324268 1.0986123 1.23888889 0
## 159 0.4761905 0.35330350 3.0445224 0.13888889 0
## 160 10.0000000 -23.02585093 0.0000000 0.35000000 0
## 161 5.0000000 -8.04718956 0.6931472 0.73888889 0
## 162 1.6666667 -0.85137604 1.7917595 0.85555556 1
## 163 3.3333333 -4.01324268 1.0986123 0.38888889 0
## 164 5.0000000 -8.04718956 0.6931472 0.36666667 0
## 165 2.0000000 -1.38629436 1.6094379 0.22222222 1
## 166 5.0000000 -8.04718956 0.6931472 0.41666667 0
## 167 3.3333333 -4.01324268 1.0986123 1.03888889 0
## 168 3.3333333 -4.01324268 1.0986123 1.01666667 0
## 169 1.1111111 -0.11706724 2.1972246 1.01111111 0
## 170 3.3333333 -4.01324268 1.0986123 1.06666667 0
## 171 2.0000000 -1.38629436 1.6094379 0.90000000 1
## 172 3.3333333 -4.01324268 1.0986123 1.07222222 1
## 173 3.3333333 -4.01324268 1.0986123 0.61666667 0
## 174 1.1111111 -0.11706724 2.1972246 1.01111111 1
## 175 2.0000000 -1.38629436 1.6094379 1.00000000 0
## 176 10.0000000 -23.02585093 0.0000000 0.51666667 0
## 177 1.2500000 -0.27892944 2.0794415 0.92777778 0
## 178 3.3333333 -4.01324268 1.0986123 1.08888889 0
## 179 2.0000000 -1.38629436 1.6094379 0.58888889 0
## 180 1.4285714 -0.50953563 1.9459101 0.87777778 1
## 181 5.0000000 -8.04718956 0.6931472 1.01111111 0
## 182 2.0000000 -1.38629436 1.6094379 0.98888889 0
## 183 3.3333333 -4.01324268 1.0986123 0.98888889 0
## 184 0.9090909 0.08664562 2.3978953 0.97777778 1
## 185 10.0000000 -23.02585093 0.0000000 1.05555556 0
## 186 10.0000000 -23.02585093 0.0000000 0.05555556 1
## 187 1.1111111 -0.11706724 2.1972246 0.35555556 1
## 188 3.3333333 -4.01324268 1.0986123 1.02222222 0
## 189 2.5000000 -2.29072683 1.3862944 0.73333333 0
## 190 1.1111111 -0.11706724 2.1972246 1.00000000 0
## 191 2.5000000 -2.29072683 1.3862944 1.03333333 0
## 192 3.3333333 -4.01324268 1.0986123 0.98888889 0
## 193 10.0000000 -23.02585093 0.0000000 1.01111111 0
## 194 1.1111111 -0.11706724 2.1972246 0.62222222 1
## 195 1.2500000 -0.27892944 2.0794415 1.00000000 1
## 196 2.5000000 -2.29072683 1.3862944 0.81111111 0
## 197 1.6666667 -0.85137604 1.7917595 0.94444444 0
## 198 2.5000000 -2.29072683 1.3862944 0.25555556 1
## 199 2.5000000 -2.29072683 1.3862944 0.94444444 1
## 200 1.6666667 -0.85137604 1.7917595 1.00000000 1
## 201 2.0000000 -1.38629436 1.6094379 0.58888889 0
## 202 3.3333333 -4.01324268 1.0986123 1.06666667 1
## 203 3.3333333 -4.01324268 1.0986123 0.92222222 0
## 204 1.1111111 -0.11706724 2.1972246 0.60000000 1
## 205 1.0000000 0.00000000 2.3025851 0.87777778 0
## 206 2.5000000 -2.29072683 1.3862944 0.90000000 1
## 207 2.0000000 -1.38629436 1.6094379 0.20000000 0
## 208 2.0000000 -1.38629436 1.6094379 1.02222222 1
## 209 0.9090909 0.08664562 2.3978953 0.21666667 0
## 210 10.0000000 -23.02585093 0.0000000 0.98333333 0
## 211 3.3333333 -4.01324268 1.0986123 0.67777778 1
## 212 2.0000000 -1.38629436 1.6094379 0.98888889 0
## 213 1.2500000 -0.27892944 2.0794415 0.96111111 0
## 214 0.3125000 0.36348463 3.4657359 0.29444444 1
## 215 1.6666667 -0.85137604 1.7917595 0.52222222 1
## 216 3.3333333 -4.01324268 1.0986123 0.90555556 0
## 217 1.4285714 -0.50953563 1.9459101 0.88888889 0
## 218 1.4285714 -0.50953563 1.9459101 0.33888889 0
## 219 0.7692308 0.20181866 2.5649494 0.22777778 0
## 220 1.6666667 -0.85137604 1.7917595 0.29444444 1
## 221 2.0000000 -1.38629436 1.6094379 0.29444444 1
## 222 0.8333333 0.15193463 2.4849066 0.07222222 0
## 223 2.5000000 -2.29072683 1.3862944 1.01666667 0
## 224 2.0000000 -1.38629436 1.6094379 1.01111111 1
## 225 2.5000000 -2.29072683 1.3862944 1.01666667 1
## 226 1.0000000 0.00000000 2.3025851 0.35000000 1
## 227 1.2500000 -0.27892944 2.0794415 0.61666667 1
## 228 3.3333333 -4.01324268 1.0986123 0.96666667 0
## 229 2.5000000 -2.29072683 1.3862944 0.96111111 1
## 230 3.3333333 -4.01324268 1.0986123 0.66111111 0
## 231 2.5000000 -2.29072683 1.3862944 1.00000000 0
## 232 3.3333333 -4.01324268 1.0986123 0.54444444 0
## 233 2.0000000 -1.38629436 1.6094379 0.27777778 0
## 234 5.0000000 -8.04718956 0.6931472 0.98888889 0
## 235 0.5882353 0.31213427 2.8332133 0.55555556 1
## 236 2.0000000 -1.38629436 1.6094379 0.51666667 0
## 237 2.5000000 -2.29072683 1.3862944 0.91666667 1
## 238 10.0000000 -23.02585093 0.0000000 0.51666667 0
## 239 1.6666667 -0.85137604 1.7917595 0.48888889 1
## 240 1.6666667 -0.85137604 1.7917595 0.85555556 0
## 241 10.0000000 -23.02585093 0.0000000 1.01111111 0
## 242 3.3333333 -4.01324268 1.0986123 1.05555556 0
## 243 5.0000000 -8.04718956 0.6931472 0.91111111 1
## 244 1.6666667 -0.85137604 1.7917595 0.84444444 0
## 245 2.5000000 -2.29072683 1.3862944 0.05555556 0
## 246 1.2500000 -0.27892944 2.0794415 0.76666667 1
## 247 0.7692308 0.20181866 2.5649494 1.00000000 0
## 248 1.1111111 -0.11706724 2.1972246 0.21111111 0
## 249 2.0000000 -1.38629436 1.6094379 0.66666667 0
## 250 10.0000000 -23.02585093 0.0000000 0.76666667 0
## 251 1.1111111 -0.11706724 2.1972246 0.94444444 0
## 252 2.5000000 -2.29072683 1.3862944 1.02222222 1
## 253 2.5000000 -2.29072683 1.3862944 0.61111111 0
## 254 1.0000000 0.00000000 2.3025851 0.22222222 1
## 255 3.3333333 -4.01324268 1.0986123 0.96666667 1
## 256 10.0000000 -23.02585093 0.0000000 1.01111111 0
## 257 10.0000000 -23.02585093 0.0000000 0.10000000 0
## 258 1.4285714 -0.50953563 1.9459101 0.24444444 1
## 259 5.0000000 -8.04718956 0.6931472 0.96666667 0
## 260 0.9090909 0.08664562 2.3978953 0.95555556 1
## 261 1.2500000 -0.27892944 2.0794415 0.94444444 1
## 262 10.0000000 -23.02585093 0.0000000 0.92222222 0
## 263 1.4285714 -0.50953563 1.9459101 0.92222222 0
## 264 1.4285714 -0.50953563 1.9459101 1.02222222 0
## 265 2.5000000 -2.29072683 1.3862944 0.94444444 0
## 266 1.4285714 -0.50953563 1.9459101 0.40000000 0
## 267 3.3333333 -4.01324268 1.0986123 0.96666667 1
## 268 10.0000000 -23.02585093 0.0000000 0.31111111 0
## 269 1.6666667 -0.85137604 1.7917595 0.52222222 1
## 270 10.0000000 -23.02585093 0.0000000 0.41111111 1
## 271 5.0000000 -8.04718956 0.6931472 1.03333333 1
## 272 3.3333333 -4.01324268 1.0986123 0.98888889 0
## 273 2.5000000 -2.29072683 1.3862944 0.46666667 1
## 274 10.0000000 -23.02585093 0.0000000 0.07222222 0
## 275 0.7692308 0.20181866 2.5649494 0.47222222 1
## 276 2.5000000 -2.29072683 1.3862944 0.05000000 0
## 277 2.0000000 -1.38629436 1.6094379 0.90000000 0
## 278 0.4761905 0.35330350 3.0445224 0.25555556 1
## 279 0.4761905 0.35330350 3.0445224 0.28888889 1
## 280 1.0000000 0.00000000 2.3025851 0.93333333 0
## 281 1.6666667 -0.85137604 1.7917595 0.25555556 1
## 282 3.3333333 -4.01324268 1.0986123 0.95555556 0
## 283 2.0000000 -1.38629436 1.6094379 1.00000000 1
## 284 1.1111111 -0.11706724 2.1972246 0.81111111 1
## 285 1.2500000 -0.27892944 2.0794415 0.84444444 1
## 286 5.0000000 -8.04718956 0.6931472 0.10000000 0
## 287 2.5000000 -2.29072683 1.3862944 0.52222222 0
## 288 3.3333333 -4.01324268 1.0986123 0.42222222 0
## 289 2.5000000 -2.29072683 1.3862944 0.22222222 0
## 290 2.5000000 -2.29072683 1.3862944 0.97777778 0
## 291 10.0000000 -23.02585093 0.0000000 0.57777778 0
## 292 0.6250000 0.29375227 2.7725887 0.02777778 1
## 293 1.4285714 -0.50953563 1.9459101 0.99444444 0
## 294 1.1111111 -0.11706724 2.1972246 0.19444444 1
## 295 5.0000000 -8.04718956 0.6931472 0.13333333 0
## 296 0.2777778 0.35581496 3.5835189 0.45555556 1
## 297 2.5000000 -2.29072683 1.3862944 0.15555556 0
## 298 10.0000000 -23.02585093 0.0000000 0.45000000 0
## 299 3.3333333 -4.01324268 1.0986123 0.02222222 0
## 300 2.0000000 -1.38629436 1.6094379 0.53888889 1
## 301 10.0000000 -23.02585093 0.0000000 0.43333333 0
## 302 2.5000000 -2.29072683 1.3862944 1.00555556 0
## 303 5.0000000 -8.04718956 0.6931472 0.16111111 0
## 304 5.0000000 -8.04718956 0.6931472 0.77222222 0
## 305 3.3333333 -4.01324268 1.0986123 0.84444444 1
## 306 1.6666667 -0.85137604 1.7917595 0.50000000 0
## 307 2.0000000 -1.38629436 1.6094379 0.34444444 1
## 308 0.7142857 0.24033731 2.6390573 0.61111111 0
## 309 0.6666667 0.27031007 2.7080502 0.08333333 0
## 310 1.6666667 -0.85137604 1.7917595 0.37777778 0
## 311 10.0000000 -23.02585093 0.0000000 0.10555556 0
## 312 1.4285714 -0.50953563 1.9459101 0.25555556 1
## 313 0.3225806 0.36496842 3.4339872 1.02222222 1
## 314 1.1111111 -0.11706724 2.1972246 1.04444444 1
## 315 1.4285714 -0.50953563 1.9459101 0.34444444 1
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## 385 5.0000000 -8.04718956 0.6931472 0.17222222 0
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## 389 0.4761905 0.35330350 3.0445224 1.13333333 1
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## 391 5.0000000 -8.04718956 0.6931472 0.98888889 0
## 392 0.7142857 0.24033731 2.6390573 0.31111111 1
## 393 5.0000000 -8.04718956 0.6931472 1.00000000 1
## 394 2.5000000 -2.29072683 1.3862944 0.93333333 0
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## 402 5.0000000 -8.04718956 0.6931472 2.43333333 1
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## 424 1.6666667 -0.85137604 1.7917595 0.22222222 1
## 425 2.0000000 -1.38629436 1.6094379 1.95555556 1
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## 472 5.0000000 -8.04718956 0.6931472 2.03333333 0
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## 478 2.5000000 -2.29072683 1.3862944 2.22222222 1
## 479 10.0000000 -23.02585093 0.0000000 1.97777778 0
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## 482 3.3333333 -4.01324268 1.0986123 1.71111111 0
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## 485 10.0000000 -23.02585093 0.0000000 0.42222222 0
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## 487 2.5000000 -2.29072683 1.3862944 0.97777778 1
## 488 5.0000000 -8.04718956 0.6931472 0.36666667 0
## 489 3.3333333 -4.01324268 1.0986123 0.38888889 1
## 490 3.3333333 -4.01324268 1.0986123 0.37777778 0
## 491 0.3846154 0.36750440 3.2580965 2.12222222 0
## 492 1.6666667 -0.85137604 1.7917595 0.38888889 0
## 493 5.0000000 -8.04718956 0.6931472 0.17777778 0
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## 498 1.6666667 -0.85137604 1.7917595 0.98888889 0
## 499 5.0000000 -8.04718956 0.6931472 0.42222222 0
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## 501 3.3333333 -4.01324268 1.0986123 0.84444444 0
## 502 2.5000000 -2.29072683 1.3862944 2.16666667 0
## 503 5.0000000 -8.04718956 0.6931472 2.04444444 0
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## 508 3.3333333 -4.01324268 1.0986123 1.88888889 1
## 509 2.0000000 -1.38629436 1.6094379 0.27777778 1
## 510 10.0000000 -23.02585093 0.0000000 0.90555556 0
## 511 0.8333333 0.15193463 2.4849066 2.02222222 0
## 512 2.5000000 -2.29072683 1.3862944 1.66666667 0
## 513 1.2500000 -0.27892944 2.0794415 0.18888889 1
## 514 2.0000000 -1.38629436 1.6094379 0.18888889 1
## 515 2.5000000 -2.29072683 1.3862944 2.03333333 0
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## 518 1.4285714 -0.50953563 1.9459101 2.03333333 1
## 519 1.4285714 -0.50953563 1.9459101 0.07777778 1
## 520 3.3333333 -4.01324268 1.0986123 2.04444444 0
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## 522 1.6666667 -0.85137604 1.7917595 2.03333333 0
## 523 0.7142857 0.24033731 2.6390573 1.96666667 1
## 524 0.9090909 0.08664562 2.3978953 0.85555556 1
## 525 0.4761905 0.35330350 3.0445224 1.36666667 0
## 526 5.0000000 -8.04718956 0.6931472 1.62222222 0
## 527 0.9090909 0.08664562 2.3978953 1.12777778 0
## 528 1.4285714 -0.50953563 1.9459101 2.00000000 1
## 529 3.3333333 -4.01324268 1.0986123 0.87777778 0
## 530 1.6666667 -0.85137604 1.7917595 1.11666667 0
## 531 10.0000000 -23.02585093 0.0000000 0.71666667 0
## 532 5.0000000 -8.04718956 0.6931472 2.02777778 0
## 533 2.0000000 -1.38629436 1.6094379 0.88333333 0
## 534 3.3333333 -4.01324268 1.0986123 1.96666667 0
## 535 5.0000000 -8.04718956 0.6931472 0.39444444 1
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## 538 5.0000000 -8.04718956 0.6931472 2.06666667 0
## 539 1.4285714 -0.50953563 1.9459101 0.27777778 0
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## 542 1.6666667 -0.85137604 1.7917595 0.95000000 0
## 543 5.0000000 -8.04718956 0.6931472 0.80555556 0
## 544 0.4761905 0.35330350 3.0445224 2.03333333 0
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## 551 10.0000000 -23.02585093 0.0000000 0.25555556 0
## 552 5.0000000 -8.04718956 0.6931472 1.42222222 0
## 553 10.0000000 -23.02585093 0.0000000 1.17777778 0
## 554 10.0000000 -23.02585093 0.0000000 0.51111111 0
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## 556 3.3333333 -4.01324268 1.0986123 0.26666667 0
## 557 2.5000000 -2.29072683 1.3862944 0.32222222 1
## 558 3.3333333 -4.01324268 1.0986123 1.98888889 0
## 559 2.0000000 -1.38629436 1.6094379 1.88888889 1
## 560 5.0000000 -8.04718956 0.6931472 2.02777778 0
## 561 2.0000000 -1.38629436 1.6094379 2.22222222 0
## 562 10.0000000 -23.02585093 0.0000000 0.62222222 0
## 563 1.6666667 -0.85137604 1.7917595 0.26666667 0
## 564 1.4285714 -0.50953563 1.9459101 0.11111111 0
## 565 0.4545455 0.35838971 3.0910425 1.96666667 1
## 566 10.0000000 -23.02585093 0.0000000 1.28888889 0
## 567 0.8333333 0.15193463 2.4849066 0.30000000 0
## 568 10.0000000 -23.02585093 0.0000000 0.26666667 0
## 569 2.0000000 -1.38629436 1.6094379 0.63333333 0
## 570 10.0000000 -23.02585093 0.0000000 0.51111111 0
## 571 1.4285714 -0.50953563 1.9459101 0.43333333 0
## 572 2.0000000 -1.38629436 1.6094379 0.18888889 1
## 573 2.5000000 -2.29072683 1.3862944 0.11666667 0
## 574 3.3333333 -4.01324268 1.0986123 2.04444444 1
## 575 0.6250000 0.29375227 2.7725887 0.05000000 1## Rows: 575
## Columns: 18
## $ ID <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, …
## $ AGE <dbl> 39, 33, 33, 32, 24, 30, 39, 27, 40, 36, 38, 29, 32, 41, 31, 27,…
## $ BECK <dbl> 9.000, 34.000, 10.000, 20.000, 5.000, 32.550, 19.000, 10.000, 2…
## $ HC <dbl> 4, 4, 2, 4, 2, 3, 4, 4, 2, 2, 2, 3, 3, 1, 1, 2, 1, 4, 3, 2, 3, …
## $ IV <dbl> 3, 2, 3, 3, 1, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 2, 1, 3, 1, …
## $ NDT <dbl> 1, 8, 3, 1, 5, 1, 34, 2, 3, 7, 8, 1, 2, 8, 1, 3, 6, 1, 15, 5, 1…
## $ RACE <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ TREAT <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, …
## $ SITE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LEN.T <dbl> 123, 25, 7, 66, 173, 16, 179, 21, 176, 124, 176, 79, 182, 174, …
## $ TIME <dbl> 188, 26, 207, 144, 551, 32, 459, 22, 210, 184, 212, 87, 598, 26…
## $ CENSOR <dbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, …
## $ Y <dbl> 5.236442, 3.258097, 5.332719, 4.969813, 6.311735, 3.465736, 6.1…
## $ ND1 <dbl> 5.0000000, 1.1111111, 2.5000000, 5.0000000, 1.6666667, 5.000000…
## $ ND2 <dbl> -8.0471896, -0.1170672, -2.2907268, -8.0471896, -0.8513760, -8.…
## $ LNDT <dbl> 0.6931472, 2.1972246, 1.3862944, 0.6931472, 1.7917595, 0.693147…
## $ FRAC <dbl> 0.68333333, 0.13888889, 0.03888889, 0.73333333, 0.96111111, 0.0…
## $ IV3 <dbl> 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, …22.7.2 Prepare the data for analysis. [Not always necessary.]
We need IV to be a factor variable.
# Although we've already done this above,
# we include it here again for completeness.
uis2 <- uis %>%
mutate(IV_fct = factor(IV, levels = c(1, 2, 3),
labels = c("Never", "Previous", "Recent")))
uis2## ID AGE BECK HC IV NDT RACE TREAT SITE LEN.T TIME CENSOR Y
## 1 1 39 9.000 4 3 1 0 1 0 123 188 1 5.236442
## 2 2 33 34.000 4 2 8 0 1 0 25 26 1 3.258097
## 3 3 33 10.000 2 3 3 0 1 0 7 207 1 5.332719
## 4 4 32 20.000 4 3 1 0 0 0 66 144 1 4.969813
## 5 5 24 5.000 2 1 5 1 1 0 173 551 0 6.311735
## 6 6 30 32.550 3 3 1 0 1 0 16 32 1 3.465736
## 7 7 39 19.000 4 3 34 0 1 0 179 459 1 6.129050
## 8 8 27 10.000 4 3 2 0 1 0 21 22 1 3.091042
## 9 9 40 29.000 2 3 3 0 1 0 176 210 1 5.347108
## 10 10 36 25.000 2 3 7 0 1 0 124 184 1 5.214936
## 11 12 38 18.900 2 3 8 0 1 0 176 212 1 5.356586
## 12 13 29 16.000 3 1 1 0 1 0 79 87 1 4.465908
## 13 14 32 36.000 3 3 2 1 1 0 182 598 0 6.393591
## 14 15 41 19.000 1 3 8 0 1 0 174 260 1 5.560682
## 15 16 31 18.000 1 3 1 0 1 0 181 210 1 5.347108
## 16 17 27 12.000 2 3 3 0 1 0 61 84 1 4.430817
## 17 18 28 34.000 1 3 6 0 1 0 177 196 1 5.278115
## 18 19 28 23.000 4 2 1 0 1 0 19 19 1 2.944439
## 19 20 36 26.000 3 1 15 1 1 0 27 441 1 6.089045
## 20 21 32 18.900 2 3 5 0 1 0 175 449 1 6.107023
## 21 22 33 15.000 3 1 1 0 0 0 12 659 0 6.490724
## 22 23 28 25.200 1 3 8 0 0 0 21 21 1 3.044522
## 23 24 29 6.632 4 2 0 0 0 0 48 53 1 3.970292
## 24 25 35 2.100 2 3 9 0 0 0 90 225 1 5.416100
## 25 26 45 26.000 1 3 6 0 0 0 91 161 1 5.081404
## 26 27 35 39.789 4 3 5 0 0 0 87 87 1 4.465908
## 27 28 24 20.000 3 1 3 0 0 0 88 89 1 4.488636
## 28 29 36 16.000 1 3 7 0 0 0 9 44 1 3.784190
## 29 31 39 22.000 1 3 9 0 0 0 94 523 0 6.259581
## 30 32 36 9.947 4 2 10 0 0 0 91 226 1 5.420535
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## 390 432 49 22.000 2 3 7 0 0 0 85 189 1 5.241747
## 391 434 36 25.000 3 1 1 1 0 0 89 193 1 5.262690
## 392 435 27 30.000 1 3 13 0 0 0 28 28 1 3.332205
## 393 436 35 23.000 1 3 1 0 0 0 90 150 1 5.010635
## 394 437 25 10.000 3 2 3 0 0 0 84 99 1 4.595120
## 395 438 33 8.000 1 3 3 0 0 0 85 510 0 6.234411
## 396 439 34 16.000 1 3 7 0 0 0 36 306 1 5.723585
## 397 440 38 9.000 1 3 10 1 0 0 74 101 1 4.615121
## 398 441 36 12.158 2 3 0 1 0 0 42 102 1 4.624973
## 399 442 27 5.000 1 3 1 0 0 0 90 510 0 6.234411
## 400 444 40 19.000 1 3 0 1 0 0 108 503 0 6.220590
## 401 445 32 23.000 3 3 3 0 0 1 49 52 1 3.951244
## 402 446 38 28.000 3 3 1 1 0 1 219 547 0 6.304449
## 403 447 38 16.000 1 3 6 0 0 1 108 168 1 5.123964
## 404 448 23 25.000 4 1 0 0 0 1 178 461 1 6.133398
## 405 449 26 22.000 4 2 2 0 0 1 42 538 0 6.287859
## 406 450 36 28.000 2 3 7 0 0 1 182 349 1 5.855072
## 407 451 30 28.000 4 1 5 0 0 1 6 44 1 3.784190
## 408 452 31 18.000 4 2 3 0 1 1 351 548 0 6.306275
## 409 453 23 15.000 3 1 1 0 1 1 12 12 1 2.484907
## 410 454 43 9.000 1 3 0 1 1 1 6 6 1 1.791759
## 411 455 24 26.000 4 1 1 0 1 1 91 575 0 6.354370
## 412 456 42 19.000 4 1 1 0 1 1 245 589 0 6.378426
## 413 457 35 26.000 4 2 1 0 1 1 372 408 1 6.011267
## 414 458 21 10.000 4 1 0 0 1 1 218 232 1 5.446737
## 415 459 45 1.000 4 2 0 1 1 1 46 143 1 4.962845
## 416 460 43 30.000 2 3 6 0 1 1 363 582 0 6.366470
## 417 461 24 7.000 4 1 0 1 1 1 133 134 1 4.897840
## 418 462 37 11.000 3 3 1 0 1 1 7 7 1 1.945910
## 419 463 40 10.000 4 2 0 0 1 1 112 548 0 6.306275
## 420 464 27 11.000 3 2 2 0 0 1 21 81 1 4.394449
## 421 465 29 11.000 2 3 1 0 0 1 169 170 1 5.135798
## 422 466 34 12.000 4 3 6 0 0 1 28 29 1 3.367296
## 423 467 29 29.000 3 3 20 0 0 1 47 78 1 4.356709
## 424 468 35 27.000 1 3 5 0 0 1 20 81 1 4.394449
## 425 469 39 20.000 1 3 4 0 1 1 352 369 1 5.910797
## 426 470 41 9.000 4 2 0 0 1 1 66 69 1 4.234107
## 427 471 37 18.000 4 1 6 1 1 1 55 115 1 4.744932
## 428 472 30 10.000 3 2 7 0 1 1 344 361 1 5.888878
## 429 473 31 1.000 4 1 0 0 1 1 153 245 1 5.501258
## 430 474 40 5.000 4 2 8 0 0 1 184 233 1 5.451038
## 431 475 32 20.000 4 1 0 0 0 1 183 227 1 5.424950
## 432 476 32 7.000 4 2 3 1 0 1 22 97 1 4.574711
## 433 477 27 7.000 4 1 0 0 0 1 183 547 0 6.304449
## 434 478 23 26.000 3 1 0 0 0 1 140 224 1 5.411646
## 435 479 23 4.000 4 1 2 0 0 1 19 211 1 5.351858
## 436 480 43 11.000 2 3 12 0 0 1 184 220 1 5.393628
## 437 481 24 20.000 4 1 0 0 0 1 50 54 1 3.988984
## 438 482 36 11.000 4 1 2 1 0 1 132 192 1 5.257495
## 439 483 29 31.000 1 3 1 0 0 1 128 138 1 4.927254
## 440 484 39 13.000 4 2 1 0 1 1 107 107 1 4.672829
## 441 485 23 6.000 4 1 0 0 1 1 368 597 0 6.391917
## 442 486 27 17.000 3 3 4 0 1 1 219 226 1 5.420535
## 443 487 26 5.000 4 2 5 0 1 1 374 434 1 6.073045
## 444 488 26 27.000 3 1 1 1 1 1 92 106 1 4.663439
## 445 489 25 9.000 4 1 0 0 1 1 45 180 1 5.192957
## 446 490 34 10.000 3 1 0 0 1 1 366 557 0 6.322565
## 447 491 45 5.000 4 3 2 0 1 1 368 556 0 6.320768
## 448 492 23 17.000 4 1 1 0 0 1 78 619 0 6.428105
## 449 493 26 7.000 4 1 0 0 0 1 184 546 0 6.302619
## 450 495 24 27.000 1 2 2 0 0 1 187 233 1 5.451038
## 451 496 30 23.000 2 3 2 1 0 1 101 102 1 4.624973
## 452 497 22 26.000 3 1 0 0 0 1 141 548 0 6.306275
## 453 498 25 10.000 3 1 1 0 0 1 24 99 1 4.595120
## 454 499 30 8.400 3 2 40 0 0 1 36 36 1 3.583519
## 455 501 33 23.000 4 1 0 1 1 1 56 78 1 4.356709
## 456 502 34 15.000 3 2 8 0 1 1 367 502 1 6.218600
## 457 503 29 24.000 3 1 2 0 1 1 70 71 1 4.262680
## 458 504 39 33.000 4 2 6 0 1 1 58 59 1 4.077537
## 459 506 26 21.000 3 1 4 0 1 1 366 533 0 6.278521
## 460 507 32 23.000 2 3 6 0 1 1 10 10 1 2.302585
## 461 508 42 23.100 1 3 2 0 0 1 214 274 1 5.613128
## 462 509 39 25.000 1 2 8 0 0 1 197 255 1 5.541264
## 463 510 36 2.000 4 1 0 1 0 1 89 503 0 6.220590
## 464 511 22 20.000 3 1 1 0 0 1 56 256 1 5.545177
## 465 512 27 23.000 4 1 1 0 0 1 9 9 1 2.197225
## 466 514 28 9.000 4 1 0 0 0 1 186 386 1 5.955837
## 467 515 36 28.000 3 2 1 0 1 1 303 547 0 6.304449
## 468 516 31 13.000 3 1 3 0 1 1 32 45 1 3.806662
## 469 517 27 22.000 3 2 4 0 1 1 8 58 1 4.060443
## 470 518 23 17.000 3 1 1 0 1 1 63 124 1 4.820282
## 471 519 24 20.000 3 2 20 0 0 1 108 540 0 6.291569
## 472 520 38 5.000 3 2 1 0 0 1 183 243 1 5.493061
## 473 521 25 8.000 4 1 1 0 1 1 151 549 0 6.308098
## 474 522 26 20.000 3 1 0 0 0 1 7 12 1 2.484907
## 475 523 22 34.000 3 1 2 0 0 1 38 51 1 3.931826
## 476 524 33 13.000 4 1 2 0 1 1 176 562 0 6.331502
## 477 525 30 23.000 1 3 7 0 1 1 93 94 1 4.543295
## 478 526 45 8.000 4 3 3 0 0 1 200 204 1 5.318120
## 479 527 24 15.000 3 2 0 0 0 1 178 238 1 5.472271
## 480 528 27 22.000 4 1 0 0 1 1 78 140 1 4.941642
## 481 529 36 19.000 4 2 10 0 1 1 119 120 1 4.787492
## 482 530 38 23.000 4 2 2 1 0 1 154 154 1 5.036953
## 483 531 31 17.000 2 3 2 0 1 1 163 177 1 5.176150
## 484 532 40 22.000 4 2 7 0 1 1 118 119 1 4.779123
## 485 533 22 12.000 3 1 0 1 1 1 76 83 1 4.418841
## 486 534 31 13.000 4 1 0 1 1 1 116 130 1 4.867534
## 487 536 39 7.000 3 3 3 1 0 1 88 159 1 5.068904
## 488 538 33 14.000 3 1 1 0 0 1 33 33 1 3.496508
## 489 539 27 10.000 3 3 2 0 1 1 70 72 1 4.276666
## 490 540 37 7.000 4 1 2 1 1 1 68 161 1 5.081404
## 491 541 35 16.000 4 2 25 0 0 1 191 191 1 5.252273
## 492 542 25 11.000 3 1 5 0 0 1 35 181 1 5.198497
## 493 543 27 11.000 3 1 1 1 1 1 32 546 0 6.302619
## 494 544 34 15.000 4 1 0 0 0 1 28 540 0 6.291569
## 495 545 30 15.000 3 1 3 0 0 1 15 76 1 4.330733
## 496 546 35 17.000 1 3 7 0 0 1 7 7 1 1.945910
## 497 547 34 23.000 4 1 0 0 0 1 43 44 1 3.784190
## 498 548 25 23.000 3 2 5 0 0 1 89 103 1 4.634729
## 499 549 34 18.000 3 1 1 0 0 1 38 79 1 4.369448
## 500 550 24 23.000 4 3 3 0 0 1 204 339 1 5.826000
## 501 551 24 20.000 4 1 2 0 0 1 76 90 1 4.499810
## 502 552 40 36.000 4 1 3 0 0 1 195 542 0 6.295266
## 503 553 33 9.000 3 1 1 1 0 1 184 384 1 5.950643
## 504 554 38 14.000 4 2 1 1 1 1 254 255 1 5.541264
## 505 555 32 1.000 3 1 0 0 1 1 371 431 1 6.066108
## 506 556 33 3.000 4 1 1 0 0 1 196 587 0 6.375025
## 507 557 28 40.000 3 1 2 1 0 1 198 198 1 5.288267
## 508 558 31 13.000 3 3 2 0 0 1 170 551 0 6.311735
## 509 559 31 39.000 2 3 4 0 1 1 50 110 1 4.700480
## 510 560 33 24.000 4 1 0 0 1 1 163 541 0 6.293419
## 511 561 24 26.000 3 1 11 0 0 1 182 242 1 5.488938
## 512 562 26 18.000 3 1 3 0 0 1 150 537 0 6.285998
## 513 563 31 19.000 2 3 7 0 1 1 34 56 1 4.025352
## 514 564 40 14.700 2 3 4 0 1 1 34 34 1 3.526361
## 515 566 34 2.000 3 1 3 0 1 1 366 549 0 6.308098
## 516 567 30 11.000 3 2 7 0 0 1 133 133 1 4.890349
## 517 568 36 0.000 3 2 3 0 0 1 69 226 1 5.420535
## 518 569 38 17.000 2 3 6 0 1 1 366 401 1 5.993961
## 519 570 31 20.000 1 3 6 1 1 1 14 14 1 2.639057
## 520 571 27 22.000 2 2 2 0 0 1 184 548 0 6.306275
## 521 572 32 21.000 1 3 15 0 1 1 89 224 1 5.411646
## 522 573 35 23.000 3 1 5 1 0 1 183 540 0 6.291569
## 523 574 44 29.000 2 3 13 0 0 1 177 237 1 5.468060
## 524 575 31 5.000 2 3 10 0 1 1 154 354 1 5.869297
## 525 576 28 23.000 3 2 20 0 0 1 123 123 1 4.812184
## 526 577 40 8.000 4 2 1 0 0 1 146 170 1 5.135798
## 527 578 25 12.000 3 1 10 1 1 1 203 203 1 5.313206
## 528 579 32 10.000 1 3 6 0 1 1 360 360 1 5.886104
## 529 580 29 15.750 4 1 2 0 0 1 79 139 1 4.934474
## 530 581 40 2.000 2 2 5 0 1 1 201 215 1 5.370638
## 531 582 27 9.000 4 2 0 0 1 1 129 129 1 4.859812
## 532 583 26 2.000 3 1 1 0 1 1 365 396 1 5.981414
## 533 584 34 15.000 3 1 4 1 1 1 159 547 0 6.304449
## 534 585 49 4.000 4 2 2 0 0 1 177 547 0 6.304449
## 535 586 21 25.000 1 3 1 0 1 1 71 71 1 4.262680
## 536 587 39 23.000 3 3 2 0 1 1 108 168 1 5.123964
## 537 588 33 15.000 4 2 4 0 1 1 198 228 1 5.429346
## 538 589 32 3.000 3 1 1 0 1 1 372 551 0 6.311735
## 539 590 35 9.000 4 2 6 0 0 1 25 654 0 6.483107
## 540 591 31 20.000 4 1 0 1 1 1 48 51 1 3.931826
## 541 592 28 5.000 4 1 3 0 0 1 191 548 0 6.306275
## 542 593 27 29.000 3 2 5 0 1 1 171 231 1 5.442418
## 543 594 29 21.000 2 1 1 1 1 1 145 280 1 5.634790
## 544 595 30 1.000 2 1 20 0 0 1 183 184 1 5.214936
## 545 596 27 18.000 4 1 3 1 0 1 72 86 1 4.454347
## 546 598 40 15.000 4 2 1 0 1 1 44 46 1 3.828641
## 547 599 37 20.000 3 1 2 1 1 1 140 200 1 5.298317
## 548 600 33 10.000 4 1 0 0 0 1 184 244 1 5.497168
## 549 601 28 20.000 4 1 2 0 0 1 94 182 1 5.204007
## 550 602 40 15.000 4 2 8 0 1 1 296 296 1 5.690359
## 551 603 48 20.000 4 1 0 1 0 1 23 24 1 3.178054
## 552 604 38 25.000 3 1 1 0 0 1 128 142 1 4.955827
## 553 605 35 13.000 4 1 0 0 0 1 106 120 1 4.787492
## 554 606 37 13.000 4 2 0 0 0 1 46 47 1 3.850148
## 555 607 25 15.000 3 1 0 1 1 1 150 519 1 6.251904
## 556 608 26 8.000 4 1 2 0 1 1 48 248 1 5.513429
## 557 609 30 9.000 3 3 3 0 0 1 29 31 1 3.433987
## 558 610 28 16.000 4 2 2 0 0 1 179 567 0 6.340359
## 559 611 23 11.000 2 3 4 0 0 1 170 353 1 5.866468
## 560 612 36 31.000 4 1 1 0 1 1 365 458 1 6.126869
## 561 613 36 13.000 4 2 4 0 1 1 400 554 0 6.317165
## 562 614 24 5.000 4 1 0 1 0 1 56 116 1 4.753590
## 563 615 33 9.000 3 2 5 0 0 1 24 74 1 4.304065
## 564 616 38 15.000 4 2 6 0 0 1 10 10 1 2.302585
## 565 617 41 20.000 3 3 21 0 1 1 354 355 1 5.872118
## 566 618 31 21.000 3 1 0 1 1 1 232 232 1 5.446737
## 567 619 31 23.000 4 2 11 0 1 1 54 68 1 4.219508
## 568 620 37 5.000 4 1 0 1 1 1 48 48 1 3.871201
## 569 621 37 17.000 4 2 4 1 0 1 57 60 1 4.094345
## 570 622 33 13.000 4 1 0 0 0 1 46 50 1 3.912023
## 571 624 53 9.000 4 2 6 0 0 1 39 126 1 4.836282
## 572 625 37 20.000 2 3 4 0 0 1 17 18 1 2.890372
## 573 626 28 10.000 4 2 3 0 1 1 21 35 1 3.555348
## 574 627 35 17.000 1 3 2 0 0 1 184 379 1 5.937536
## 575 628 46 31.500 1 3 15 1 1 1 9 377 1 5.932245
## ND1 ND2 LNDT FRAC IV3 IV_fct
## 1 5.0000000 -8.04718956 0.6931472 0.68333333 1 Recent
## 2 1.1111111 -0.11706724 2.1972246 0.13888889 0 Previous
## 3 2.5000000 -2.29072683 1.3862944 0.03888889 1 Recent
## 4 5.0000000 -8.04718956 0.6931472 0.73333333 1 Recent
## 5 1.6666667 -0.85137604 1.7917595 0.96111111 0 Never
## 6 5.0000000 -8.04718956 0.6931472 0.08888889 1 Recent
## 7 0.2857143 0.35793228 3.5553481 0.99444444 1 Recent
## 8 3.3333333 -4.01324268 1.0986123 0.11666667 1 Recent
## 9 2.5000000 -2.29072683 1.3862944 0.97777778 1 Recent
## 10 1.2500000 -0.27892944 2.0794415 0.68888889 1 Recent
## 11 1.1111111 -0.11706724 2.1972246 0.97777778 1 Recent
## 12 5.0000000 -8.04718956 0.6931472 0.43888889 0 Never
## 13 3.3333333 -4.01324268 1.0986123 1.01111111 1 Recent
## 14 1.1111111 -0.11706724 2.1972246 0.96666667 1 Recent
## 15 5.0000000 -8.04718956 0.6931472 1.00555556 1 Recent
## 16 2.5000000 -2.29072683 1.3862944 0.33888889 1 Recent
## 17 1.4285714 -0.50953563 1.9459101 0.98333333 1 Recent
## 18 5.0000000 -8.04718956 0.6931472 0.10555556 0 Previous
## 19 0.6250000 0.29375227 2.7725887 0.15000000 0 Never
## 20 1.6666667 -0.85137604 1.7917595 0.97222222 1 Recent
## 21 5.0000000 -8.04718956 0.6931472 0.13333333 0 Never
## 22 1.1111111 -0.11706724 2.1972246 0.23333333 1 Recent
## 23 10.0000000 -23.02585093 0.0000000 0.53333333 0 Previous
## 24 1.0000000 0.00000000 2.3025851 1.00000000 1 Recent
## 25 1.4285714 -0.50953563 1.9459101 1.01111111 1 Recent
## 26 1.6666667 -0.85137604 1.7917595 0.96666667 1 Recent
## 27 2.5000000 -2.29072683 1.3862944 0.97777778 0 Never
## 28 1.2500000 -0.27892944 2.0794415 0.10000000 1 Recent
## 29 1.0000000 0.00000000 2.3025851 1.04444444 1 Recent
## 30 0.9090909 0.08664562 2.3978953 1.01111111 0 Previous
## 31 5.0000000 -8.04718956 0.6931472 1.00000000 1 Recent
## 32 5.0000000 -8.04718956 0.6931472 0.98888889 1 Recent
## 33 1.6666667 -0.85137604 1.7917595 0.98888889 1 Recent
## 34 1.4285714 -0.50953563 1.9459101 1.11111111 0 Never
## 35 2.5000000 -2.29072683 1.3862944 0.74444444 0 Never
## 36 1.2500000 -0.27892944 2.0794415 0.13888889 1 Recent
## 37 5.0000000 -8.04718956 0.6931472 0.13333333 0 Never
## 38 5.0000000 -8.04718956 0.6931472 0.87777778 0 Never
## 39 3.3333333 -4.01324268 1.0986123 0.87777778 0 Never
## 40 10.0000000 -23.02585093 0.0000000 0.43333333 1 Recent
## 41 3.3333333 -4.01324268 1.0986123 0.46666667 0 Previous
## 42 1.4285714 -0.50953563 1.9459101 0.50555556 1 Recent
## 43 5.0000000 -8.04718956 0.6931472 0.90000000 1 Recent
## 44 10.0000000 -23.02585093 0.0000000 0.25000000 1 Recent
## 45 5.0000000 -8.04718956 0.6931472 0.33888889 1 Recent
## 46 10.0000000 -23.02585093 0.0000000 0.10555556 1 Recent
## 47 3.3333333 -4.01324268 1.0986123 0.20555556 0 Never
## 48 1.1111111 -0.11706724 2.1972246 0.28333333 1 Recent
## 49 5.0000000 -8.04718956 0.6931472 0.33333333 1 Recent
## 50 0.3846154 0.36750440 3.2580965 0.98333333 1 Recent
## 51 10.0000000 -23.02585093 0.0000000 0.23888889 0 Never
## 52 2.5000000 -2.29072683 1.3862944 0.11666667 0 Never
## 53 3.3333333 -4.01324268 1.0986123 0.97777778 1 Recent
## 54 1.4285714 -0.50953563 1.9459101 1.06666667 1 Recent
## 55 0.9090909 0.08664562 2.3978953 1.23333333 1 Recent
## 56 0.9090909 0.08664562 2.3978953 0.42222222 1 Recent
## 57 1.2500000 -0.27892944 2.0794415 0.16666667 0 Never
## 58 1.6666667 -0.85137604 1.7917595 0.55555556 0 Previous
## 59 1.6666667 -0.85137604 1.7917595 0.67777778 1 Recent
## 60 5.0000000 -8.04718956 0.6931472 0.34444444 0 Never
## 61 3.3333333 -4.01324268 1.0986123 0.12222222 0 Never
## 62 1.4285714 -0.50953563 1.9459101 1.00000000 1 Recent
## 63 1.6666667 -0.85137604 1.7917595 0.12222222 1 Recent
## 64 1.6666667 -0.85137604 1.7917595 0.51111111 1 Recent
## 65 10.0000000 -23.02585093 0.0000000 0.42222222 1 Recent
## 66 10.0000000 -23.02585093 0.0000000 1.00000000 1 Recent
## 67 1.6666667 -0.85137604 1.7917595 0.97777778 1 Recent
## 68 0.7692308 0.20181866 2.5649494 1.01111111 1 Recent
## 69 1.4285714 -0.50953563 1.9459101 0.94444444 0 Previous
## 70 10.0000000 -23.02585093 0.0000000 1.00000000 0 Never
## 71 1.6666667 -0.85137604 1.7917595 0.57777778 1 Recent
## 72 2.5000000 -2.29072683 1.3862944 0.97777778 1 Recent
## 73 0.7142857 0.24033731 2.6390573 0.47777778 1 Recent
## 74 1.1111111 -0.11706724 2.1972246 0.41111111 1 Recent
## 75 10.0000000 -23.02585093 0.0000000 0.96666667 0 Never
## 76 3.3333333 -4.01324268 1.0986123 0.22222222 0 Previous
## 77 5.0000000 -8.04718956 0.6931472 0.10000000 0 Never
## 78 5.0000000 -8.04718956 0.6931472 0.94444444 1 Recent
## 79 3.3333333 -4.01324268 1.0986123 0.20000000 1 Recent
## 80 0.7692308 0.20181866 2.5649494 0.78888889 1 Recent
## 81 3.3333333 -4.01324268 1.0986123 0.97777778 1 Recent
## 82 5.0000000 -8.04718956 0.6931472 0.74444444 0 Previous
## 83 2.0000000 -1.38629436 1.6094379 0.33333333 1 Recent
## 84 5.0000000 -8.04718956 0.6931472 0.37777778 0 Never
## 85 1.0000000 0.00000000 2.3025851 1.01111111 0 Previous
## 86 5.0000000 -8.04718956 0.6931472 1.01111111 1 Recent
## 87 5.0000000 -8.04718956 0.6931472 0.81111111 0 Never
## 88 5.0000000 -8.04718956 0.6931472 0.22222222 1 Recent
## 89 5.0000000 -8.04718956 0.6931472 0.98333333 0 Never
## 90 3.3333333 -4.01324268 1.0986123 1.00555556 0 Previous
## 91 0.6250000 0.29375227 2.7725887 0.93333333 1 Recent
## 92 1.6666667 -0.85137604 1.7917595 0.50000000 1 Recent
## 93 1.6666667 -0.85137604 1.7917595 0.33888889 1 Recent
## 94 2.5000000 -2.29072683 1.3862944 0.35000000 0 Never
## 95 1.1111111 -0.11706724 2.1972246 0.67222222 0 Previous
## 96 5.0000000 -8.04718956 0.6931472 0.98888889 0 Never
## 97 2.5000000 -2.29072683 1.3862944 0.28333333 0 Never
## 98 5.0000000 -8.04718956 0.6931472 0.97777778 0 Never
## 99 5.0000000 -8.04718956 0.6931472 0.27777778 1 Recent
## 100 2.5000000 -2.29072683 1.3862944 0.92222222 0 Never
## 101 3.3333333 -4.01324268 1.0986123 0.98888889 0 Never
## 102 5.0000000 -8.04718956 0.6931472 0.26666667 1 Recent
## 103 0.4761905 0.35330350 3.0445224 0.07777778 1 Recent
## 104 5.0000000 -8.04718956 0.6931472 0.94444444 0 Never
## 105 10.0000000 -23.02585093 0.0000000 0.98888889 0 Never
## 106 2.0000000 -1.38629436 1.6094379 1.01111111 1 Recent
## 107 10.0000000 -23.02585093 0.0000000 0.98888889 0 Never
## 108 10.0000000 -23.02585093 0.0000000 0.91111111 0 Never
## 109 2.5000000 -2.29072683 1.3862944 0.93333333 1 Recent
## 110 5.0000000 -8.04718956 0.6931472 0.33333333 1 Recent
## 111 1.4285714 -0.50953563 1.9459101 0.07777778 0 Never
## 112 2.5000000 -2.29072683 1.3862944 0.93333333 1 Recent
## 113 10.0000000 -23.02585093 0.0000000 0.77777778 1 Recent
## 114 3.3333333 -4.01324268 1.0986123 0.84444444 0 Previous
## 115 5.0000000 -8.04718956 0.6931472 0.98888889 0 Never
## 116 5.0000000 -8.04718956 0.6931472 0.98888889 1 Recent
## 117 1.4285714 -0.50953563 1.9459101 0.48333333 1 Recent
## 118 10.0000000 -23.02585093 0.0000000 0.97222222 0 Previous
## 119 2.5000000 -2.29072683 1.3862944 0.48333333 0 Never
## 120 0.5882353 0.31213427 2.8332133 0.61111111 0 Never
## 121 5.0000000 -8.04718956 0.6931472 0.11666667 1 Recent
## 122 2.5000000 -2.29072683 1.3862944 0.77222222 0 Never
## 123 0.6250000 0.29375227 2.7725887 1.00555556 1 Recent
## 124 3.3333333 -4.01324268 1.0986123 0.18333333 1 Recent
## 125 5.0000000 -8.04718956 0.6931472 0.21666667 1 Recent
## 126 1.2500000 -0.27892944 2.0794415 0.02222222 1 Recent
## 127 2.0000000 -1.38629436 1.6094379 1.02222222 1 Recent
## 128 5.0000000 -8.04718956 0.6931472 0.68333333 0 Never
## 129 5.0000000 -8.04718956 0.6931472 0.97777778 0 Never
## 130 3.3333333 -4.01324268 1.0986123 0.96666667 1 Recent
## 131 5.0000000 -8.04718956 0.6931472 1.00555556 1 Recent
## 132 3.3333333 -4.01324268 1.0986123 0.62777778 1 Recent
## 133 2.5000000 -2.29072683 1.3862944 0.91111111 1 Recent
## 134 5.0000000 -8.04718956 0.6931472 0.93333333 1 Recent
## 135 1.1111111 -0.11706724 2.1972246 0.88888889 1 Recent
## 136 2.0000000 -1.38629436 1.6094379 1.01111111 1 Recent
## 137 2.5000000 -2.29072683 1.3862944 1.07777778 0 Never
## 138 2.5000000 -2.29072683 1.3862944 0.56666667 0 Never
## 139 2.5000000 -2.29072683 1.3862944 1.01111111 1 Recent
## 140 2.0000000 -1.38629436 1.6094379 1.00000000 0 Never
## 141 1.4285714 -0.50953563 1.9459101 0.51111111 1 Recent
## 142 5.0000000 -8.04718956 0.6931472 0.84444444 0 Never
## 143 3.3333333 -4.01324268 1.0986123 0.83333333 1 Recent
## 144 2.0000000 -1.38629436 1.6094379 1.01111111 1 Recent
## 145 1.6666667 -0.85137604 1.7917595 1.00000000 1 Recent
## 146 1.4285714 -0.50953563 1.9459101 0.03333333 1 Recent
## 147 2.5000000 -2.29072683 1.3862944 0.04444444 1 Recent
## 148 5.0000000 -8.04718956 0.6931472 0.18333333 0 Never
## 149 1.1111111 -0.11706724 2.1972246 0.17222222 1 Recent
## 150 5.0000000 -8.04718956 0.6931472 0.96666667 0 Never
## 151 3.3333333 -4.01324268 1.0986123 0.18888889 0 Never
## 152 2.5000000 -2.29072683 1.3862944 0.33333333 0 Previous
## 153 1.6666667 -0.85137604 1.7917595 0.43333333 0 Previous
## 154 3.3333333 -4.01324268 1.0986123 1.01111111 0 Never
## 155 5.0000000 -8.04718956 0.6931472 1.01111111 0 Never
## 156 10.0000000 -23.02585093 0.0000000 0.43333333 0 Never
## 157 5.0000000 -8.04718956 0.6931472 0.30555556 0 Never
## 158 3.3333333 -4.01324268 1.0986123 1.23888889 0 Never
## 159 0.4761905 0.35330350 3.0445224 0.13888889 0 Never
## 160 10.0000000 -23.02585093 0.0000000 0.35000000 0 Never
## 161 5.0000000 -8.04718956 0.6931472 0.73888889 0 Never
## 162 1.6666667 -0.85137604 1.7917595 0.85555556 1 Recent
## 163 3.3333333 -4.01324268 1.0986123 0.38888889 0 Never
## 164 5.0000000 -8.04718956 0.6931472 0.36666667 0 Previous
## 165 2.0000000 -1.38629436 1.6094379 0.22222222 1 Recent
## 166 5.0000000 -8.04718956 0.6931472 0.41666667 0 Never
## 167 3.3333333 -4.01324268 1.0986123 1.03888889 0 Previous
## 168 3.3333333 -4.01324268 1.0986123 1.01666667 0 Never
## 169 1.1111111 -0.11706724 2.1972246 1.01111111 0 Never
## 170 3.3333333 -4.01324268 1.0986123 1.06666667 0 Previous
## 171 2.0000000 -1.38629436 1.6094379 0.90000000 1 Recent
## 172 3.3333333 -4.01324268 1.0986123 1.07222222 1 Recent
## 173 3.3333333 -4.01324268 1.0986123 0.61666667 0 Never
## 174 1.1111111 -0.11706724 2.1972246 1.01111111 1 Recent
## 175 2.0000000 -1.38629436 1.6094379 1.00000000 0 Never
## 176 10.0000000 -23.02585093 0.0000000 0.51666667 0 Never
## 177 1.2500000 -0.27892944 2.0794415 0.92777778 0 Never
## 178 3.3333333 -4.01324268 1.0986123 1.08888889 0 Never
## 179 2.0000000 -1.38629436 1.6094379 0.58888889 0 Previous
## 180 1.4285714 -0.50953563 1.9459101 0.87777778 1 Recent
## 181 5.0000000 -8.04718956 0.6931472 1.01111111 0 Previous
## 182 2.0000000 -1.38629436 1.6094379 0.98888889 0 Never
## 183 3.3333333 -4.01324268 1.0986123 0.98888889 0 Previous
## 184 0.9090909 0.08664562 2.3978953 0.97777778 1 Recent
## 185 10.0000000 -23.02585093 0.0000000 1.05555556 0 Never
## 186 10.0000000 -23.02585093 0.0000000 0.05555556 1 Recent
## 187 1.1111111 -0.11706724 2.1972246 0.35555556 1 Recent
## 188 3.3333333 -4.01324268 1.0986123 1.02222222 0 Previous
## 189 2.5000000 -2.29072683 1.3862944 0.73333333 0 Never
## 190 1.1111111 -0.11706724 2.1972246 1.00000000 0 Never
## 191 2.5000000 -2.29072683 1.3862944 1.03333333 0 Previous
## 192 3.3333333 -4.01324268 1.0986123 0.98888889 0 Never
## 193 10.0000000 -23.02585093 0.0000000 1.01111111 0 Previous
## 194 1.1111111 -0.11706724 2.1972246 0.62222222 1 Recent
## 195 1.2500000 -0.27892944 2.0794415 1.00000000 1 Recent
## 196 2.5000000 -2.29072683 1.3862944 0.81111111 0 Never
## 197 1.6666667 -0.85137604 1.7917595 0.94444444 0 Never
## 198 2.5000000 -2.29072683 1.3862944 0.25555556 1 Recent
## 199 2.5000000 -2.29072683 1.3862944 0.94444444 1 Recent
## 200 1.6666667 -0.85137604 1.7917595 1.00000000 1 Recent
## 201 2.0000000 -1.38629436 1.6094379 0.58888889 0 Never
## 202 3.3333333 -4.01324268 1.0986123 1.06666667 1 Recent
## 203 3.3333333 -4.01324268 1.0986123 0.92222222 0 Previous
## 204 1.1111111 -0.11706724 2.1972246 0.60000000 1 Recent
## 205 1.0000000 0.00000000 2.3025851 0.87777778 0 Previous
## 206 2.5000000 -2.29072683 1.3862944 0.90000000 1 Recent
## 207 2.0000000 -1.38629436 1.6094379 0.20000000 0 Never
## 208 2.0000000 -1.38629436 1.6094379 1.02222222 1 Recent
## 209 0.9090909 0.08664562 2.3978953 0.21666667 0 Previous
## 210 10.0000000 -23.02585093 0.0000000 0.98333333 0 Never
## 211 3.3333333 -4.01324268 1.0986123 0.67777778 1 Recent
## 212 2.0000000 -1.38629436 1.6094379 0.98888889 0 Never
## 213 1.2500000 -0.27892944 2.0794415 0.96111111 0 Never
## 214 0.3125000 0.36348463 3.4657359 0.29444444 1 Recent
## 215 1.6666667 -0.85137604 1.7917595 0.52222222 1 Recent
## 216 3.3333333 -4.01324268 1.0986123 0.90555556 0 Never
## 217 1.4285714 -0.50953563 1.9459101 0.88888889 0 Previous
## 218 1.4285714 -0.50953563 1.9459101 0.33888889 0 Previous
## 219 0.7692308 0.20181866 2.5649494 0.22777778 0 Never
## 220 1.6666667 -0.85137604 1.7917595 0.29444444 1 Recent
## 221 2.0000000 -1.38629436 1.6094379 0.29444444 1 Recent
## 222 0.8333333 0.15193463 2.4849066 0.07222222 0 Previous
## 223 2.5000000 -2.29072683 1.3862944 1.01666667 0 Never
## 224 2.0000000 -1.38629436 1.6094379 1.01111111 1 Recent
## 225 2.5000000 -2.29072683 1.3862944 1.01666667 1 Recent
## 226 1.0000000 0.00000000 2.3025851 0.35000000 1 Recent
## 227 1.2500000 -0.27892944 2.0794415 0.61666667 1 Recent
## 228 3.3333333 -4.01324268 1.0986123 0.96666667 0 Previous
## 229 2.5000000 -2.29072683 1.3862944 0.96111111 1 Recent
## 230 3.3333333 -4.01324268 1.0986123 0.66111111 0 Never
## 231 2.5000000 -2.29072683 1.3862944 1.00000000 0 Never
## 232 3.3333333 -4.01324268 1.0986123 0.54444444 0 Never
## 233 2.0000000 -1.38629436 1.6094379 0.27777778 0 Never
## 234 5.0000000 -8.04718956 0.6931472 0.98888889 0 Previous
## 235 0.5882353 0.31213427 2.8332133 0.55555556 1 Recent
## 236 2.0000000 -1.38629436 1.6094379 0.51666667 0 Previous
## 237 2.5000000 -2.29072683 1.3862944 0.91666667 1 Recent
## 238 10.0000000 -23.02585093 0.0000000 0.51666667 0 Never
## 239 1.6666667 -0.85137604 1.7917595 0.48888889 1 Recent
## 240 1.6666667 -0.85137604 1.7917595 0.85555556 0 Never
## 241 10.0000000 -23.02585093 0.0000000 1.01111111 0 Never
## 242 3.3333333 -4.01324268 1.0986123 1.05555556 0 Never
## 243 5.0000000 -8.04718956 0.6931472 0.91111111 1 Recent
## 244 1.6666667 -0.85137604 1.7917595 0.84444444 0 Previous
## 245 2.5000000 -2.29072683 1.3862944 0.05555556 0 Previous
## 246 1.2500000 -0.27892944 2.0794415 0.76666667 1 Recent
## 247 0.7692308 0.20181866 2.5649494 1.00000000 0 Never
## 248 1.1111111 -0.11706724 2.1972246 0.21111111 0 Never
## 249 2.0000000 -1.38629436 1.6094379 0.66666667 0 Never
## 250 10.0000000 -23.02585093 0.0000000 0.76666667 0 Never
## 251 1.1111111 -0.11706724 2.1972246 0.94444444 0 Previous
## 252 2.5000000 -2.29072683 1.3862944 1.02222222 1 Recent
## 253 2.5000000 -2.29072683 1.3862944 0.61111111 0 Never
## 254 1.0000000 0.00000000 2.3025851 0.22222222 1 Recent
## 255 3.3333333 -4.01324268 1.0986123 0.96666667 1 Recent
## 256 10.0000000 -23.02585093 0.0000000 1.01111111 0 Never
## 257 10.0000000 -23.02585093 0.0000000 0.10000000 0 Never
## 258 1.4285714 -0.50953563 1.9459101 0.24444444 1 Recent
## 259 5.0000000 -8.04718956 0.6931472 0.96666667 0 Never
## 260 0.9090909 0.08664562 2.3978953 0.95555556 1 Recent
## 261 1.2500000 -0.27892944 2.0794415 0.94444444 1 Recent
## 262 10.0000000 -23.02585093 0.0000000 0.92222222 0 Never
## 263 1.4285714 -0.50953563 1.9459101 0.92222222 0 Never
## 264 1.4285714 -0.50953563 1.9459101 1.02222222 0 Previous
## 265 2.5000000 -2.29072683 1.3862944 0.94444444 0 Previous
## 266 1.4285714 -0.50953563 1.9459101 0.40000000 0 Never
## 267 3.3333333 -4.01324268 1.0986123 0.96666667 1 Recent
## 268 10.0000000 -23.02585093 0.0000000 0.31111111 0 Never
## 269 1.6666667 -0.85137604 1.7917595 0.52222222 1 Recent
## 270 10.0000000 -23.02585093 0.0000000 0.41111111 1 Recent
## 271 5.0000000 -8.04718956 0.6931472 1.03333333 1 Recent
## 272 3.3333333 -4.01324268 1.0986123 0.98888889 0 Previous
## 273 2.5000000 -2.29072683 1.3862944 0.46666667 1 Recent
## 274 10.0000000 -23.02585093 0.0000000 0.07222222 0 Previous
## 275 0.7692308 0.20181866 2.5649494 0.47222222 1 Recent
## 276 2.5000000 -2.29072683 1.3862944 0.05000000 0 Previous
## 277 2.0000000 -1.38629436 1.6094379 0.90000000 0 Previous
## 278 0.4761905 0.35330350 3.0445224 0.25555556 1 Recent
## 279 0.4761905 0.35330350 3.0445224 0.28888889 1 Recent
## 280 1.0000000 0.00000000 2.3025851 0.93333333 0 Previous
## 281 1.6666667 -0.85137604 1.7917595 0.25555556 1 Recent
## 282 3.3333333 -4.01324268 1.0986123 0.95555556 0 Never
## 283 2.0000000 -1.38629436 1.6094379 1.00000000 1 Recent
## 284 1.1111111 -0.11706724 2.1972246 0.81111111 1 Recent
## 285 1.2500000 -0.27892944 2.0794415 0.84444444 1 Recent
## 286 5.0000000 -8.04718956 0.6931472 0.10000000 0 Never
## 287 2.5000000 -2.29072683 1.3862944 0.52222222 0 Never
## 288 3.3333333 -4.01324268 1.0986123 0.42222222 0 Never
## 289 2.5000000 -2.29072683 1.3862944 0.22222222 0 Never
## 290 2.5000000 -2.29072683 1.3862944 0.97777778 0 Never
## 291 10.0000000 -23.02585093 0.0000000 0.57777778 0 Previous
## 292 0.6250000 0.29375227 2.7725887 0.02777778 1 Recent
## 293 1.4285714 -0.50953563 1.9459101 0.99444444 0 Never
## 294 1.1111111 -0.11706724 2.1972246 0.19444444 1 Recent
## 295 5.0000000 -8.04718956 0.6931472 0.13333333 0 Previous
## 296 0.2777778 0.35581496 3.5835189 0.45555556 1 Recent
## 297 2.5000000 -2.29072683 1.3862944 0.15555556 0 Never
## 298 10.0000000 -23.02585093 0.0000000 0.45000000 0 Never
## 299 3.3333333 -4.01324268 1.0986123 0.02222222 0 Never
## 300 2.0000000 -1.38629436 1.6094379 0.53888889 1 Recent
## 301 10.0000000 -23.02585093 0.0000000 0.43333333 0 Never
## 302 2.5000000 -2.29072683 1.3862944 1.00555556 0 Never
## 303 5.0000000 -8.04718956 0.6931472 0.16111111 0 Previous
## 304 5.0000000 -8.04718956 0.6931472 0.77222222 0 Never
## 305 3.3333333 -4.01324268 1.0986123 0.84444444 1 Recent
## 306 1.6666667 -0.85137604 1.7917595 0.50000000 0 Previous
## 307 2.0000000 -1.38629436 1.6094379 0.34444444 1 Recent
## 308 0.7142857 0.24033731 2.6390573 0.61111111 0 Previous
## 309 0.6666667 0.27031007 2.7080502 0.08333333 0 Never
## 310 1.6666667 -0.85137604 1.7917595 0.37777778 0 Never
## 311 10.0000000 -23.02585093 0.0000000 0.10555556 0 Never
## 312 1.4285714 -0.50953563 1.9459101 0.25555556 1 Recent
## 313 0.3225806 0.36496842 3.4339872 1.02222222 1 Recent
## 314 1.1111111 -0.11706724 2.1972246 1.04444444 1 Recent
## 315 1.4285714 -0.50953563 1.9459101 0.34444444 1 Recent
## 316 2.5000000 -2.29072683 1.3862944 0.31111111 0 Previous
## 317 0.9090909 0.08664562 2.3978953 0.64444444 1 Recent
## 318 1.4285714 -0.50953563 1.9459101 1.25555556 1 Recent
## 319 2.5000000 -2.29072683 1.3862944 0.77777778 0 Never
## 320 2.0000000 -1.38629436 1.6094379 1.00000000 1 Recent
## 321 1.4285714 -0.50953563 1.9459101 0.61111111 1 Recent
## 322 1.6666667 -0.85137604 1.7917595 0.98888889 1 Recent
## 323 1.2500000 -0.27892944 2.0794415 0.78888889 1 Recent
## 324 1.6666667 -0.85137604 1.7917595 0.93333333 0 Never
## 325 2.0000000 -1.38629436 1.6094379 0.86666667 0 Never
## 326 2.0000000 -1.38629436 1.6094379 0.66666667 0 Never
## 327 1.4285714 -0.50953563 1.9459101 0.91111111 1 Recent
## 328 1.6666667 -0.85137604 1.7917595 0.90000000 0 Previous
## 329 2.5000000 -2.29072683 1.3862944 0.19444444 1 Recent
## 330 0.4347826 0.36213440 3.1354942 0.08888889 1 Recent
## 331 5.0000000 -8.04718956 0.6931472 0.03888889 0 Previous
## 332 2.0000000 -1.38629436 1.6094379 0.16666667 0 Never
## 333 2.5000000 -2.29072683 1.3862944 0.58888889 0 Never
## 334 0.9090909 0.08664562 2.3978953 0.96666667 0 Never
## 335 5.0000000 -8.04718956 0.6931472 0.80000000 0 Never
## 336 5.0000000 -8.04718956 0.6931472 0.13333333 0 Previous
## 337 2.5000000 -2.29072683 1.3862944 0.09444444 0 Previous
## 338 2.0000000 -1.38629436 1.6094379 0.53888889 0 Never
## 339 10.0000000 -23.02585093 0.0000000 0.14444444 1 Recent
## 340 3.3333333 -4.01324268 1.0986123 0.17222222 0 Never
## 341 2.5000000 -2.29072683 1.3862944 0.15555556 1 Recent
## 342 3.3333333 -4.01324268 1.0986123 0.83333333 1 Recent
## 343 1.4285714 -0.50953563 1.9459101 0.22222222 1 Recent
## 344 2.0000000 -1.38629436 1.6094379 1.15555556 0 Never
## 345 0.7692308 0.20181866 2.5649494 0.94444444 1 Recent
## 346 10.0000000 -23.02585093 0.0000000 1.22222222 0 Never
## 347 0.5882353 0.31213427 2.8332133 1.11111111 1 Recent
## 348 5.0000000 -8.04718956 0.6931472 0.81111111 1 Recent
## 349 1.4285714 -0.50953563 1.9459101 0.72222222 1 Recent
## 350 0.4761905 0.35330350 3.0445224 0.83333333 1 Recent
## 351 5.0000000 -8.04718956 0.6931472 0.92222222 0 Never
## 352 0.5555556 0.32654815 2.8903718 0.08333333 0 Previous
## 353 0.3225806 0.36496842 3.4339872 0.24444444 1 Recent
## 354 2.5000000 -2.29072683 1.3862944 0.03888889 0 Never
## 355 2.0000000 -1.38629436 1.6094379 0.11111111 1 Recent
## 356 0.7692308 0.20181866 2.5649494 0.97222222 1 Recent
## 357 2.5000000 -2.29072683 1.3862944 0.39444444 0 Never
## 358 3.3333333 -4.01324268 1.0986123 0.14444444 0 Never
## 359 2.5000000 -2.29072683 1.3862944 0.89444444 1 Recent
## 360 1.0000000 0.00000000 2.3025851 0.20000000 0 Never
## 361 1.1111111 -0.11706724 2.1972246 0.16666667 1 Recent
## 362 2.5000000 -2.29072683 1.3862944 0.99444444 0 Previous
## 363 3.3333333 -4.01324268 1.0986123 1.10555556 1 Recent
## 364 2.5000000 -2.29072683 1.3862944 1.01111111 1 Recent
## 365 2.5000000 -2.29072683 1.3862944 1.24444444 0 Never
## 366 1.4285714 -0.50953563 1.9459101 0.08888889 1 Recent
## 367 3.3333333 -4.01324268 1.0986123 0.20000000 0 Never
## 368 1.4285714 -0.50953563 1.9459101 0.22222222 0 Never
## 369 2.5000000 -2.29072683 1.3862944 0.97777778 1 Recent
## 370 1.4285714 -0.50953563 1.9459101 0.97777778 0 Never
## 371 0.4761905 0.35330350 3.0445224 0.84444444 1 Recent
## 372 5.0000000 -8.04718956 0.6931472 0.24444444 0 Never
## 373 2.0000000 -1.38629436 1.6094379 1.22222222 1 Recent
## 374 2.5000000 -2.29072683 1.3862944 0.94444444 1 Recent
## 375 2.5000000 -2.29072683 1.3862944 0.11111111 1 Recent
## 376 0.5555556 0.32654815 2.8903718 0.87222222 1 Recent
## 377 0.3703704 0.36787103 3.2958369 0.73888889 1 Recent
## 378 2.5000000 -2.29072683 1.3862944 0.46111111 0 Never
## 379 2.0000000 -1.38629436 1.6094379 0.84444444 0 Never
## 380 2.0000000 -1.38629436 1.6094379 0.93888889 0 Never
## 381 2.5000000 -2.29072683 1.3862944 0.49444444 0 Previous
## 382 1.6666667 -0.85137604 1.7917595 0.51111111 1 Recent
## 383 2.0000000 -1.38629436 1.6094379 0.11666667 0 Previous
## 384 0.3225806 0.36496842 3.4339872 0.17222222 1 Recent
## 385 5.0000000 -8.04718956 0.6931472 0.17222222 0 Never
## 386 0.5882353 0.31213427 2.8332133 0.73888889 1 Recent
## 387 2.0000000 -1.38629436 1.6094379 0.85000000 1 Recent
## 388 10.0000000 -23.02585093 0.0000000 1.00000000 0 Never
## 389 0.4761905 0.35330350 3.0445224 1.13333333 1 Recent
## 390 1.2500000 -0.27892944 2.0794415 0.94444444 1 Recent
## 391 5.0000000 -8.04718956 0.6931472 0.98888889 0 Never
## 392 0.7142857 0.24033731 2.6390573 0.31111111 1 Recent
## 393 5.0000000 -8.04718956 0.6931472 1.00000000 1 Recent
## 394 2.5000000 -2.29072683 1.3862944 0.93333333 0 Previous
## 395 2.5000000 -2.29072683 1.3862944 0.94444444 1 Recent
## 396 1.2500000 -0.27892944 2.0794415 0.40000000 1 Recent
## 397 0.9090909 0.08664562 2.3978953 0.82222222 1 Recent
## 398 10.0000000 -23.02585093 0.0000000 0.46666667 1 Recent
## 399 5.0000000 -8.04718956 0.6931472 1.00000000 1 Recent
## 400 10.0000000 -23.02585093 0.0000000 1.20000000 1 Recent
## 401 2.5000000 -2.29072683 1.3862944 0.54444444 1 Recent
## 402 5.0000000 -8.04718956 0.6931472 2.43333333 1 Recent
## 403 1.4285714 -0.50953563 1.9459101 1.20000000 1 Recent
## 404 10.0000000 -23.02585093 0.0000000 1.97777778 0 Never
## 405 3.3333333 -4.01324268 1.0986123 0.46666667 0 Previous
## 406 1.2500000 -0.27892944 2.0794415 2.02222222 1 Recent
## 407 1.6666667 -0.85137604 1.7917595 0.06666667 0 Never
## 408 2.5000000 -2.29072683 1.3862944 1.95000000 0 Previous
## 409 5.0000000 -8.04718956 0.6931472 0.06666667 0 Never
## 410 10.0000000 -23.02585093 0.0000000 0.03333333 1 Recent
## 411 5.0000000 -8.04718956 0.6931472 0.50555556 0 Never
## 412 5.0000000 -8.04718956 0.6931472 1.36111111 0 Never
## 413 5.0000000 -8.04718956 0.6931472 2.06666667 0 Previous
## 414 10.0000000 -23.02585093 0.0000000 1.21111111 0 Never
## 415 10.0000000 -23.02585093 0.0000000 0.25555556 0 Previous
## 416 1.4285714 -0.50953563 1.9459101 2.01666667 1 Recent
## 417 10.0000000 -23.02585093 0.0000000 0.73888889 0 Never
## 418 5.0000000 -8.04718956 0.6931472 0.03888889 1 Recent
## 419 10.0000000 -23.02585093 0.0000000 0.62222222 0 Previous
## 420 3.3333333 -4.01324268 1.0986123 0.23333333 0 Previous
## 421 5.0000000 -8.04718956 0.6931472 1.87777778 1 Recent
## 422 1.4285714 -0.50953563 1.9459101 0.31111111 1 Recent
## 423 0.4761905 0.35330350 3.0445224 0.52222222 1 Recent
## 424 1.6666667 -0.85137604 1.7917595 0.22222222 1 Recent
## 425 2.0000000 -1.38629436 1.6094379 1.95555556 1 Recent
## 426 10.0000000 -23.02585093 0.0000000 0.36666667 0 Previous
## 427 1.4285714 -0.50953563 1.9459101 0.30555556 0 Never
## 428 1.2500000 -0.27892944 2.0794415 1.91111111 0 Previous
## 429 10.0000000 -23.02585093 0.0000000 0.85000000 0 Never
## 430 1.1111111 -0.11706724 2.1972246 2.04444444 0 Previous
## 431 10.0000000 -23.02585093 0.0000000 2.03333333 0 Never
## 432 2.5000000 -2.29072683 1.3862944 0.24444444 0 Previous
## 433 10.0000000 -23.02585093 0.0000000 2.03333333 0 Never
## 434 10.0000000 -23.02585093 0.0000000 1.55555556 0 Never
## 435 3.3333333 -4.01324268 1.0986123 0.21111111 0 Never
## 436 0.7692308 0.20181866 2.5649494 2.04444444 1 Recent
## 437 10.0000000 -23.02585093 0.0000000 0.55555556 0 Never
## 438 3.3333333 -4.01324268 1.0986123 1.46666667 0 Never
## 439 5.0000000 -8.04718956 0.6931472 1.42222222 1 Recent
## 440 5.0000000 -8.04718956 0.6931472 0.59444444 0 Previous
## 441 10.0000000 -23.02585093 0.0000000 2.04444444 0 Never
## 442 2.0000000 -1.38629436 1.6094379 1.21666667 1 Recent
## 443 1.6666667 -0.85137604 1.7917595 2.07777778 0 Previous
## 444 5.0000000 -8.04718956 0.6931472 0.51111111 0 Never
## 445 10.0000000 -23.02585093 0.0000000 0.25000000 0 Never
## 446 10.0000000 -23.02585093 0.0000000 2.03333333 0 Never
## 447 3.3333333 -4.01324268 1.0986123 2.04444444 1 Recent
## 448 5.0000000 -8.04718956 0.6931472 0.86666667 0 Never
## 449 10.0000000 -23.02585093 0.0000000 2.04444444 0 Never
## 450 3.3333333 -4.01324268 1.0986123 2.07777778 0 Previous
## 451 3.3333333 -4.01324268 1.0986123 1.12222222 1 Recent
## 452 10.0000000 -23.02585093 0.0000000 1.56666667 0 Never
## 453 5.0000000 -8.04718956 0.6931472 0.26666667 0 Never
## 454 0.2439024 0.34414316 3.7135721 0.40000000 0 Previous
## 455 10.0000000 -23.02585093 0.0000000 0.31111111 0 Never
## 456 1.1111111 -0.11706724 2.1972246 2.03888889 0 Previous
## 457 3.3333333 -4.01324268 1.0986123 0.38888889 0 Never
## 458 1.4285714 -0.50953563 1.9459101 0.32222222 0 Previous
## 459 2.0000000 -1.38629436 1.6094379 2.03333333 0 Never
## 460 1.4285714 -0.50953563 1.9459101 0.05555556 1 Recent
## 461 3.3333333 -4.01324268 1.0986123 2.37777778 1 Recent
## 462 1.1111111 -0.11706724 2.1972246 2.18888889 0 Previous
## 463 10.0000000 -23.02585093 0.0000000 0.98888889 0 Never
## 464 5.0000000 -8.04718956 0.6931472 0.62222222 0 Never
## 465 5.0000000 -8.04718956 0.6931472 0.10000000 0 Never
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## 467 5.0000000 -8.04718956 0.6931472 1.68333333 0 Previous
## 468 2.5000000 -2.29072683 1.3862944 0.17777778 0 Never
## 469 2.0000000 -1.38629436 1.6094379 0.04444444 0 Previous
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## 472 5.0000000 -8.04718956 0.6931472 2.03333333 0 Previous
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## 474 10.0000000 -23.02585093 0.0000000 0.07777778 0 Never
## 475 3.3333333 -4.01324268 1.0986123 0.42222222 0 Never
## 476 3.3333333 -4.01324268 1.0986123 0.97777778 0 Never
## 477 1.2500000 -0.27892944 2.0794415 0.51666667 1 Recent
## 478 2.5000000 -2.29072683 1.3862944 2.22222222 1 Recent
## 479 10.0000000 -23.02585093 0.0000000 1.97777778 0 Previous
## 480 10.0000000 -23.02585093 0.0000000 0.43333333 0 Never
## 481 0.9090909 0.08664562 2.3978953 0.66111111 0 Previous
## 482 3.3333333 -4.01324268 1.0986123 1.71111111 0 Previous
## 483 3.3333333 -4.01324268 1.0986123 0.90555556 1 Recent
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## 486 10.0000000 -23.02585093 0.0000000 0.64444444 0 Never
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## 492 1.6666667 -0.85137604 1.7917595 0.38888889 0 Never
## 493 5.0000000 -8.04718956 0.6931472 0.17777778 0 Never
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## 496 1.2500000 -0.27892944 2.0794415 0.07777778 1 Recent
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## 508 3.3333333 -4.01324268 1.0986123 1.88888889 1 Recent
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## 517 2.5000000 -2.29072683 1.3862944 0.76666667 0 Previous
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## 519 1.4285714 -0.50953563 1.9459101 0.07777778 1 Recent
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## 521 0.6250000 0.29375227 2.7725887 0.49444444 1 Recent
## 522 1.6666667 -0.85137604 1.7917595 2.03333333 0 Never
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## 524 0.9090909 0.08664562 2.3978953 0.85555556 1 Recent
## 525 0.4761905 0.35330350 3.0445224 1.36666667 0 Previous
## 526 5.0000000 -8.04718956 0.6931472 1.62222222 0 Previous
## 527 0.9090909 0.08664562 2.3978953 1.12777778 0 Never
## 528 1.4285714 -0.50953563 1.9459101 2.00000000 1 Recent
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## 572 2.0000000 -1.38629436 1.6094379 0.18888889 1 Recent
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## 574 3.3333333 -4.01324268 1.0986123 2.04444444 1 Recent
## 575 0.6250000 0.29375227 2.7725887 0.05000000 1 Recent## Rows: 575
## Columns: 19
## $ ID <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, …
## $ AGE <dbl> 39, 33, 33, 32, 24, 30, 39, 27, 40, 36, 38, 29, 32, 41, 31, 27,…
## $ BECK <dbl> 9.000, 34.000, 10.000, 20.000, 5.000, 32.550, 19.000, 10.000, 2…
## $ HC <dbl> 4, 4, 2, 4, 2, 3, 4, 4, 2, 2, 2, 3, 3, 1, 1, 2, 1, 4, 3, 2, 3, …
## $ IV <dbl> 3, 2, 3, 3, 1, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 2, 1, 3, 1, …
## $ NDT <dbl> 1, 8, 3, 1, 5, 1, 34, 2, 3, 7, 8, 1, 2, 8, 1, 3, 6, 1, 15, 5, 1…
## $ RACE <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ TREAT <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, …
## $ SITE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LEN.T <dbl> 123, 25, 7, 66, 173, 16, 179, 21, 176, 124, 176, 79, 182, 174, …
## $ TIME <dbl> 188, 26, 207, 144, 551, 32, 459, 22, 210, 184, 212, 87, 598, 26…
## $ CENSOR <dbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, …
## $ Y <dbl> 5.236442, 3.258097, 5.332719, 4.969813, 6.311735, 3.465736, 6.1…
## $ ND1 <dbl> 5.0000000, 1.1111111, 2.5000000, 5.0000000, 1.6666667, 5.000000…
## $ ND2 <dbl> -8.0471896, -0.1170672, -2.2907268, -8.0471896, -0.8513760, -8.…
## $ LNDT <dbl> 0.6931472, 2.1972246, 1.3862944, 0.6931472, 1.7917595, 0.693147…
## $ FRAC <dbl> 0.68333333, 0.13888889, 0.03888889, 0.73333333, 0.96111111, 0.0…
## $ IV3 <dbl> 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, …
## $ IV_fct <fct> Recent, Previous, Recent, Recent, Never, Recent, Recent, Recent…22.7.3 Make tables or plots to explore the data visually.
We should calculate group statistics:
## IV_fct n percent
## Never 223 0.3878261
## Previous 109 0.1895652
## Recent 243 0.4226087
## Total 575 1.0000000## mean(BECK)
## 1 17.36743## # A tibble: 3 × 2
## IV_fct `mean(BECK)`
## <fct> <dbl>
## 1 Never 15.9
## 2 Previous 16.6
## 3 Recent 19.0Here are two graphs that are appropriate for one categorical and one numerical variable: a side-by-side boxplot and a stacked histogram.
Both graphs show that the distribution of depression scores in each group is similar.
The distributions look reasonably normal, or perhaps a bit right skewed, but we can also check the QQ plots:
There is one mild outlier in the “Previous” group, but with sample sizes as large as we have in each group, it’s unlikely that this outlier will be influential. So we’ll just leave it in the data and not worry about it.
22.8 Hypotheses
22.8.1 Identify the sample (or samples) and a reasonable population (or populations) of interest.
The sample consists of people who participated in the UIS drug treatment study. Because the UIS studied the effects of residential treatment for drug abuse, the population is, presumably, all drug addicts.
22.8.2 Express the null and alternative hypotheses as contextually meaningful full sentences.
\(H_{0}:\) There is no difference in depression levels among those who have no history of IV drug use, those who have some previous IV drug use, and those who have recent IV drug use.
\(H_{A}:\) There is a difference in depression levels among those who have no history of IV drug use, those who have some previous IV drug use, and those who have recent IV drug use.
22.8.3 Express the null and alternative hypotheses in symbols (when possible).
\(H_{0}: \mu_{never} = \mu_{previous} = \mu_{recent}\)
There is no easy way to express the alternate hypothesis in symbols because any deviation in any of the categories can lead to rejection of the null. You can’t just say \(\mu_{never} \neq \mu_{previous} \neq \mu_{recent}\) because two of these categories might be the same and the third different and that would still be consistent with the alternative hypothesis.
So the only requirement here is to express the null in symbols.
22.9 Model
22.9.1 Identify the sampling distribution model.
We will use an F model with \(df_{G} = 2\) and \(df_{E} = 572\).
Commentary: Remember that
\[ df_{G} = k - 1 = 3 - 1 = 2, \]
(\(k\) is the number of groups, in this case, 3), and
\[ df_{E} = n - k = 575 - 3 = 572. \]
22.9.2 Check the relevant conditions to ensure that model assumptions are met.
- Random
- We have little information about how this sample was collected, so we have to hope it’s representative.
- 10%
- 575 is definitely less than 10% of all drug addicts.
- Nearly normal
- The earlier stacked histograms and QQ plots showed that each group is nearly normal. (There was one outlier in one group, but our sample sizes are quite large.)
- Constant variance
- The spread of data looks pretty consistent from group to group in the stacked histogram and side-by-side boxplot.
22.10 Mechanics
22.10.1 Compute the test statistic.
BECK_IV_F <- uis2 %>%
specify(response = BECK, explanatory = IV_fct) %>%
calculate(stat = "F")
BECK_IV_F## Response: BECK (numeric)
## Explanatory: IV_fct (factor)
## # A tibble: 1 × 1
## stat
## <dbl>
## 1 6.7222.10.2 Report the test statistic in context (when possible).
The F score is 6.721405.
Commentary: F scores (much like chi-square values earlier in the course) are not particularly interpretable on their own, so there isn’t really any context we can provide. It’s only required that you report the F score in a full sentence.
22.10.3 Plot the null distribution.
BECK_IV_test <- uis2 %>%
specify(response = BECK, explanatory = IV_fct) %>%
hypothesize(null = "independence") %>%
assume(distribution = "F")
BECK_IV_test## An F distribution with 2 and 572 degrees of freedom.
22.11 Conclusion
22.11.2 State (but do not overstate) a contextually meaningful conclusion.
There is sufficient evidence that there is a difference in depression levels among those who have no history of IV drug use, those who have some previous IV drug use, and those who have recent IV drug use.
22.11.3 Express reservations or uncertainty about the generalizability of the conclusion.
Our lack of uncertainty about the sample means we don’t know for sure if we can generalize to a larger population of drug users. We hope that the researchers would obtain a representative sample. Also, the study in question is from the 1990s, so we should not suppose that the conclusions are still true today.
22.11.4 Identify the possibility of either a Type I or Type II error and state what making such an error means in the context of the hypotheses.
If we’ve made a Type I error, that means that there really isn’t a difference among the three groups, but our sample is an unusual one that did detect a difference.
Exercise 5(a)
Everything we saw earlier in the exploratory data analysis pointed toward failing to reject the null. All three groups look very similar in all the plots, and the means are not all that far from each other. So why did we get such a tiny P-value and reject the null? In other words, what is it about our data that allows for small effects to be statistically significant?
Please write up your answer here.
Exercise 5(b)
If you were a psychologist working with drug addicts, would the statistical conclusion (rejecting the null and concluding that there was a difference among groups) be of clinical importance to you? In other words, if there is a difference, is it of practical significance and not just statistical significance?
Please write up your answer here.
There is no confidence interval for ANOVA. We are not hypothesizing about the value of any particular parameter, so there’s nothing to estimate with a confidence interval.
22.12 Your turn
Using the penguins data, determine if there is a difference in the average body masses among the three species represented in the data (Adelie, Chinstrap, and Gentoo).
There are two missing values of body mass, and as we saw earlier in the book, that does affect certain functions. To make it a little easier on you, here is some code to remove those missing values:
For this whole section, be sure to use penguins2.
The rubric outline is reproduced below. You may refer to the worked example above and modify it accordingly. Remember to strip out all the commentary. That is just exposition for your benefit in understanding the steps, but is not meant to form part of the formal inference process.
Another word of warning: the copy/paste process is not a substitute for your brain. You will often need to modify more than just the names of the data frames and variables to adapt the worked examples to your own work. Do not blindly copy and paste code without understanding what it does. And you should never copy and paste text. All the sentences and paragraphs you write are expressions of your own analysis. They must reflect your own understanding of the inferential process.
Also, so that your answers here don’t mess up the code chunks above, use new variable names everywhere.
Exploratory data analysis
Use data documentation (help files, code books, Google, etc.) to determine as much as possible about the data provenance and structure.
Hypotheses
Identify the sample (or samples) and a reasonable population (or populations) of interest.
Please write up your answer here.
22.13 Bonus section: post-hoc analysis
Suppose our ANOVA test leads us to reject the null hypothesis. Then we have statistically significant evidence that there is some difference between the means of the various groups. However, ANOVA doesn’t tell us which groups are actually different – unsatisfying!
We could consider just doing a bunch of individual t-tests between each pair of groups. However, the problem with this approach is that it greatly increases the chances that we might commit a Type I error. (For an exploration of this problem, please see the following XKCD comic.)
Fortunately, there is a tool called post-hoc analysis that allows us to determine which groups differ from the others in a way that doesn’t inflate the Type I error rate.
There are several methods for conducting post-hoc analysis. You may have heard of the Bonferroni correction, in which the usual significance level is divided by the number of pairwise comparisons contemplated. Another method, and the one we’ll explore here, is called the Tukey Honestly-Significant-Difference test. The precise details of this test are a little outside the scope of this course, but here’s how it’s done in R.
We’ll start by using a different function, called aov, to conduct the ANOVA test. This function produces a slightly different format of outputs than we’re used to, but it produces all the same values as our other tools:
## Df Sum Sq Mean Sq F value Pr(>F)
## IV_fct 2 1148 574.0 6.721 0.0013 **
## Residuals 572 48850 85.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Notice in particular that the F score and the P-value are the same as we obtained using infer tools above.
Now that we have the result of the aov command stored in a new variable, we can feed it into the new command TukeyHSD:
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = BECK ~ IV_fct, data = uis2)
##
## $IV_fct
## diff lwr upr p adj
## Previous-Never 0.692054 -1.8458349 3.229943 0.7976511
## Recent-Never 3.043674 1.0299195 5.057429 0.0012039
## Recent-Previous 2.351620 -0.1517446 4.854986 0.0707718Here’s how to read these results: Start by looking at the p adj column, which tells us adjusted p-values. Look for a p-value that is below the usual significance level \(\alpha = 0.05\). In our example, the second p-value is the only one that is small enough to reach significance.
Once you’ve located the significant p-values, read the row to determine which comparisons are significant. Here, the second row is the meaningful one: this is the comparison between the “Recent” group and the “Never” group.
The column labeled diff reports the difference between the means of the two groups; the order of subtraction is reported in the first column. Here, the difference in Beck depression scores is 3.043674, which is computed by subtracting the mean of the “Never” group from the mean of the “Recent” group.
As usual, we report our results in a contextually-meaningful sentence. Here’s our example:
Tukey’s HSD test reports that recent IV drug users have a Beck inventory score that is 3.043674 points higher than those who have never used IV drugs.
22.14 Conclusion
When analyzing a numerical response variable across three or more levels of a categorical predictor variable, ANOVA provides a way of comparing the variability of the response between the groups to the variability within the groups. When there is more variability between the groups than within the groups, this is evidence that the groups are truly different from one another (rather than simply arising from random sampling variability). The result of comparing the two sources of variability gives rise to the F distribution, which can be used to determine when the difference is more than one would expect from chance alone.
22.14.1 Preparing and submitting your assignment
- From the “Run” menu, select “Restart R and Run All Chunks”.
- Deal with any code errors that crop up. Repeat steps 1–-2 until there are no more code errors.
- Spell check your document by clicking the icon with “ABC” and a check mark.
- Hit the “Preview” button one last time to generate the final draft of the
.nb.htmlfile. - Proofread the HTML file carefully. If there are errors, go back and fix them, then repeat steps 1–5 again.
If you have completed this chapter as part of a statistics course, follow the directions you receive from your professor to submit your assignment.