Homework 7
Samantha Barbaro
2026-04-08
problems 6.2 and 6.3
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.5.2## Warning: package 'ggplot2' was built under R version 4.5.2## Warning: package 'tibble' was built under R version 4.5.2## Warning: package 'readr' was built under R version 4.5.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.6
## ✔ forcats 1.0.1 ✔ stringr 1.5.2
## ✔ ggplot2 4.0.1 ✔ tibble 3.3.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(AppliedPredictiveModeling)## Warning: package 'AppliedPredictiveModeling' was built under R version 4.5.3library(caret)## Warning: package 'caret' was built under R version 4.5.2## Loading required package: lattice
##
## Attaching package: 'caret'
##
## The following object is masked from 'package:purrr':
##
## liftDeveloping a model to predict permeability (see section 1.4) could save significant resources for a pharmeceutical company, while at the same time more rapidly idenfitying molecules that have sufficient permeability to become a drug. (a) Start R and use these commands to load the data:
library(AppliedPredictiveModeling)
data(permeability)
The matrix fingerprints contains the 1,107 binary molecular predictors for the 165 compounds, while permeability contains permeability response.
- The fingerprint predictors indicate the presence or absence of substructures of a molecule and are often sparse, meaning that relatively few of the molecules contain each substructure. Filter out the predictors that have low frequencies using the nearZeroVar function from the caret package.
How many predictors are left for modeling?
data(permeability)
nzv_results <- nearZeroVar(fingerprints, saveMetrics = TRUE)
nzv_results## freqRatio percentUnique zeroVar nzv
## X1 3.342105 1.2121212 FALSE FALSE
## X2 3.459459 1.2121212 FALSE FALSE
## X3 3.714286 1.2121212 FALSE FALSE
## X4 3.714286 1.2121212 FALSE FALSE
## X5 3.714286 1.2121212 FALSE FALSE
## X6 1.229730 1.2121212 FALSE FALSE
## X7 0.000000 0.6060606 TRUE TRUE
## X8 0.000000 0.6060606 TRUE TRUE
## X9 54.000000 1.2121212 FALSE TRUE
## X10 40.250000 1.2121212 FALSE TRUE
## X11 6.173913 1.2121212 FALSE FALSE
## X12 2.437500 1.2121212 FALSE FALSE
## X13 22.571429 1.2121212 FALSE TRUE
## X14 81.500000 1.2121212 FALSE TRUE
## X15 7.250000 1.2121212 FALSE FALSE
## X16 2.666667 1.2121212 FALSE FALSE
## X17 54.000000 1.2121212 FALSE TRUE
## X18 0.000000 0.6060606 TRUE TRUE
## X19 0.000000 0.6060606 TRUE TRUE
## X20 3.714286 1.2121212 FALSE FALSE
## X21 3.714286 1.2121212 FALSE FALSE
## X22 0.000000 0.6060606 TRUE TRUE
## X23 0.000000 0.6060606 TRUE TRUE
## X24 0.000000 0.6060606 TRUE TRUE
## X25 2.666667 1.2121212 FALSE FALSE
## X26 3.714286 1.2121212 FALSE FALSE
## X27 3.714286 1.2121212 FALSE FALSE
## X28 3.714286 1.2121212 FALSE FALSE
## X29 3.714286 1.2121212 FALSE FALSE
## X30 0.000000 0.6060606 TRUE TRUE
## X31 0.000000 0.6060606 TRUE TRUE
## X32 0.000000 0.6060606 TRUE TRUE
## X33 26.500000 1.2121212 FALSE TRUE
## X34 26.500000 1.2121212 FALSE TRUE
## X35 3.459459 1.2121212 FALSE FALSE
## X36 4.689655 1.2121212 FALSE FALSE
## X37 2.666667 1.2121212 FALSE FALSE
## X38 3.714286 1.2121212 FALSE FALSE
## X39 3.714286 1.2121212 FALSE FALSE
## X40 3.714286 1.2121212 FALSE FALSE
## X41 2.173077 1.2121212 FALSE FALSE
## X42 2.235294 1.2121212 FALSE FALSE
## X43 2.235294 1.2121212 FALSE FALSE
## X44 2.235294 1.2121212 FALSE FALSE
## X45 54.000000 1.2121212 FALSE TRUE
## X46 3.714286 1.2121212 FALSE FALSE
## X47 3.714286 1.2121212 FALSE FALSE
## X48 4.322581 1.2121212 FALSE FALSE
## X49 2.666667 1.2121212 FALSE FALSE
## X50 3.714286 1.2121212 FALSE FALSE
## X51 2.173077 1.2121212 FALSE FALSE
## X52 2.235294 1.2121212 FALSE FALSE
## X53 2.235294 1.2121212 FALSE FALSE
## X54 3.714286 1.2121212 FALSE FALSE
## X55 3.714286 1.2121212 FALSE FALSE
## X56 3.714286 1.2121212 FALSE FALSE
## X57 4.322581 1.2121212 FALSE FALSE
## X58 4.322581 1.2121212 FALSE FALSE
## X59 3.714286 1.2121212 FALSE FALSE
## X60 4.322581 1.2121212 FALSE FALSE
## X61 4.322581 1.2121212 FALSE FALSE
## X62 3.714286 1.2121212 FALSE FALSE
## X63 3.714286 1.2121212 FALSE FALSE
## X64 3.714286 1.2121212 FALSE FALSE
## X65 3.714286 1.2121212 FALSE FALSE
## X66 4.322581 1.2121212 FALSE FALSE
## X67 4.322581 1.2121212 FALSE FALSE
## X68 4.322581 1.2121212 FALSE FALSE
## X69 4.322581 1.2121212 FALSE FALSE
## X70 3.714286 1.2121212 FALSE FALSE
## X71 2.666667 1.2121212 FALSE FALSE
## X72 2.666667 1.2121212 FALSE FALSE
## X73 3.342105 1.2121212 FALSE FALSE
## X74 3.342105 1.2121212 FALSE FALSE
## X75 3.342105 1.2121212 FALSE FALSE
## X76 3.342105 1.2121212 FALSE FALSE
## X77 81.500000 1.2121212 FALSE TRUE
## X78 2.173077 1.2121212 FALSE FALSE
## X79 2.235294 1.2121212 FALSE FALSE
## X80 2.173077 1.2121212 FALSE FALSE
## X81 0.000000 0.6060606 TRUE TRUE
## X82 0.000000 0.6060606 TRUE TRUE
## X83 26.500000 1.2121212 FALSE TRUE
## X84 26.500000 1.2121212 FALSE TRUE
## X85 26.500000 1.2121212 FALSE TRUE
## X86 2.367347 1.2121212 FALSE FALSE
## X87 1.357143 1.2121212 FALSE FALSE
## X88 4.892857 1.2121212 FALSE FALSE
## X89 0.000000 0.6060606 TRUE TRUE
## X90 0.000000 0.6060606 TRUE TRUE
## X91 26.500000 1.2121212 FALSE TRUE
## X92 0.000000 0.6060606 TRUE TRUE
## X93 3.125000 1.2121212 FALSE FALSE
## X94 14.000000 1.2121212 FALSE FALSE
## X95 54.000000 1.2121212 FALSE TRUE
## X96 3.583333 1.2121212 FALSE FALSE
## X97 1.012195 1.2121212 FALSE FALSE
## X98 3.024390 1.2121212 FALSE FALSE
## X99 10.000000 1.2121212 FALSE FALSE
## X100 40.250000 1.2121212 FALSE TRUE
## X101 2.367347 1.2121212 FALSE FALSE
## X102 1.426471 1.2121212 FALSE FALSE
## X103 5.600000 1.2121212 FALSE FALSE
## X104 81.500000 1.2121212 FALSE TRUE
## X105 19.625000 1.2121212 FALSE TRUE
## X106 81.500000 1.2121212 FALSE TRUE
## X107 40.250000 1.2121212 FALSE TRUE
## X108 5.600000 1.2121212 FALSE FALSE
## X109 81.500000 1.2121212 FALSE TRUE
## X110 164.000000 1.2121212 FALSE TRUE
## X111 1.426471 1.2121212 FALSE FALSE
## X112 40.250000 1.2121212 FALSE TRUE
## X113 81.500000 1.2121212 FALSE TRUE
## X114 164.000000 1.2121212 FALSE TRUE
## X115 0.000000 0.6060606 TRUE TRUE
## X116 40.250000 1.2121212 FALSE TRUE
## X117 22.571429 1.2121212 FALSE TRUE
## X118 12.750000 1.2121212 FALSE FALSE
## X119 54.000000 1.2121212 FALSE TRUE
## X120 19.625000 1.2121212 FALSE TRUE
## X121 2.113208 1.2121212 FALSE FALSE
## X122 54.000000 1.2121212 FALSE TRUE
## X123 81.500000 1.2121212 FALSE TRUE
## X124 81.500000 1.2121212 FALSE TRUE
## X125 5.600000 1.2121212 FALSE FALSE
## X126 10.785714 1.2121212 FALSE FALSE
## X127 10.785714 1.2121212 FALSE FALSE
## X128 81.500000 1.2121212 FALSE TRUE
## X129 5.111111 1.2121212 FALSE FALSE
## X130 5.600000 1.2121212 FALSE FALSE
## X131 54.000000 1.2121212 FALSE TRUE
## X132 54.000000 1.2121212 FALSE TRUE
## X133 2.666667 1.2121212 FALSE FALSE
## X134 32.000000 1.2121212 FALSE TRUE
## X135 32.000000 1.2121212 FALSE TRUE
## X136 54.000000 1.2121212 FALSE TRUE
## X137 54.000000 1.2121212 FALSE TRUE
## X138 2.666667 1.2121212 FALSE FALSE
## X139 54.000000 1.2121212 FALSE TRUE
## X140 54.000000 1.2121212 FALSE TRUE
## X141 1.894737 1.2121212 FALSE FALSE
## X142 3.714286 1.2121212 FALSE FALSE
## X143 7.684211 1.2121212 FALSE FALSE
## X144 26.500000 1.2121212 FALSE TRUE
## X145 0.000000 0.6060606 TRUE TRUE
## X146 17.333333 1.2121212 FALSE FALSE
## X147 0.000000 0.6060606 TRUE TRUE
## X148 164.000000 1.2121212 FALSE TRUE
## X149 32.000000 1.2121212 FALSE TRUE
## X150 1.796610 1.2121212 FALSE FALSE
## X151 81.500000 1.2121212 FALSE TRUE
## X152 1.946429 1.2121212 FALSE FALSE
## X153 1.894737 1.2121212 FALSE FALSE
## X154 1.894737 1.2121212 FALSE FALSE
## X155 81.500000 1.2121212 FALSE TRUE
## X156 3.852941 1.2121212 FALSE FALSE
## X157 4.156250 1.2121212 FALSE FALSE
## X158 1.426471 1.2121212 FALSE FALSE
## X159 1.115385 1.2121212 FALSE FALSE
## X160 32.000000 1.2121212 FALSE TRUE
## X161 81.500000 1.2121212 FALSE TRUE
## X162 1.946429 1.2121212 FALSE FALSE
## X163 1.894737 1.2121212 FALSE FALSE
## X164 81.500000 1.2121212 FALSE TRUE
## X165 81.500000 1.2121212 FALSE TRUE
## X166 81.500000 1.2121212 FALSE TRUE
## X167 1.894737 1.2121212 FALSE FALSE
## X168 1.894737 1.2121212 FALSE FALSE
## X169 1.894737 1.2121212 FALSE FALSE
## X170 1.894737 1.2121212 FALSE FALSE
## X171 17.333333 1.2121212 FALSE FALSE
## X172 1.894737 1.2121212 FALSE FALSE
## X173 1.894737 1.2121212 FALSE FALSE
## X174 1.894737 1.2121212 FALSE FALSE
## X175 1.844828 1.2121212 FALSE FALSE
## X176 1.844828 1.2121212 FALSE FALSE
## X177 1.844828 1.2121212 FALSE FALSE
## X178 1.844828 1.2121212 FALSE FALSE
## X179 1.946429 1.2121212 FALSE FALSE
## X180 1.894737 1.2121212 FALSE FALSE
## X181 1.796610 1.2121212 FALSE FALSE
## X182 2.113208 1.2121212 FALSE FALSE
## X183 1.894737 1.2121212 FALSE FALSE
## X184 1.844828 1.2121212 FALSE FALSE
## X185 1.844828 1.2121212 FALSE FALSE
## X186 1.844828 1.2121212 FALSE FALSE
## X187 1.946429 1.2121212 FALSE FALSE
## X188 1.894737 1.2121212 FALSE FALSE
## X189 1.894737 1.2121212 FALSE FALSE
## X190 1.796610 1.2121212 FALSE FALSE
## X191 1.796610 1.2121212 FALSE FALSE
## X192 1.894737 1.2121212 FALSE FALSE
## X193 1.844828 1.2121212 FALSE FALSE
## X194 1.844828 1.2121212 FALSE FALSE
## X195 1.946429 1.2121212 FALSE FALSE
## X196 1.946429 1.2121212 FALSE FALSE
## X197 1.946429 1.2121212 FALSE FALSE
## X198 1.894737 1.2121212 FALSE FALSE
## X199 1.894737 1.2121212 FALSE FALSE
## X200 1.796610 1.2121212 FALSE FALSE
## X201 1.796610 1.2121212 FALSE FALSE
## X202 1.796610 1.2121212 FALSE FALSE
## X203 1.796610 1.2121212 FALSE FALSE
## X204 1.894737 1.2121212 FALSE FALSE
## X205 1.894737 1.2121212 FALSE FALSE
## X206 2.113208 1.2121212 FALSE FALSE
## X207 2.113208 1.2121212 FALSE FALSE
## X208 1.796610 1.2121212 FALSE FALSE
## X209 1.796610 1.2121212 FALSE FALSE
## X210 1.796610 1.2121212 FALSE FALSE
## X211 1.796610 1.2121212 FALSE FALSE
## X212 1.844828 1.2121212 FALSE FALSE
## X213 1.844828 1.2121212 FALSE FALSE
## X214 1.844828 1.2121212 FALSE FALSE
## X215 17.333333 1.2121212 FALSE FALSE
## X216 54.000000 1.2121212 FALSE TRUE
## X217 19.625000 1.2121212 FALSE TRUE
## X218 26.500000 1.2121212 FALSE TRUE
## X219 54.000000 1.2121212 FALSE TRUE
## X220 19.625000 1.2121212 FALSE TRUE
## X221 2.586957 1.2121212 FALSE FALSE
## X222 26.500000 1.2121212 FALSE TRUE
## X223 2.586957 1.2121212 FALSE FALSE
## X224 2.586957 1.2121212 FALSE FALSE
## X225 17.333333 1.2121212 FALSE FALSE
## X226 3.583333 1.2121212 FALSE FALSE
## X227 3.583333 1.2121212 FALSE FALSE
## X228 3.583333 1.2121212 FALSE FALSE
## X229 1.538462 1.2121212 FALSE FALSE
## X230 2.928571 1.2121212 FALSE FALSE
## X231 4.689655 1.2121212 FALSE FALSE
## X232 4.689655 1.2121212 FALSE FALSE
## X233 4.689655 1.2121212 FALSE FALSE
## X234 4.689655 1.2121212 FALSE FALSE
## X235 1.260274 1.2121212 FALSE FALSE
## X236 1.578125 1.2121212 FALSE FALSE
## X237 1.142857 1.2121212 FALSE FALSE
## X238 2.367347 1.2121212 FALSE FALSE
## X239 4.156250 1.2121212 FALSE FALSE
## X240 4.156250 1.2121212 FALSE FALSE
## X241 3.024390 1.2121212 FALSE FALSE
## X242 1.229730 1.2121212 FALSE FALSE
## X243 81.500000 1.2121212 FALSE TRUE
## X244 4.156250 1.2121212 FALSE FALSE
## X245 4.156250 1.2121212 FALSE FALSE
## X246 4.156250 1.2121212 FALSE FALSE
## X247 2.750000 1.2121212 FALSE FALSE
## X248 1.426471 1.2121212 FALSE FALSE
## X249 3.024390 1.2121212 FALSE FALSE
## X250 1.229730 1.2121212 FALSE FALSE
## X251 4.322581 1.2121212 FALSE FALSE
## X252 81.500000 1.2121212 FALSE TRUE
## X253 4.156250 1.2121212 FALSE FALSE
## X254 4.156250 1.2121212 FALSE FALSE
## X255 2.750000 1.2121212 FALSE FALSE
## X256 1.426471 1.2121212 FALSE FALSE
## X257 1.229730 1.2121212 FALSE FALSE
## X258 8.705882 1.2121212 FALSE FALSE
## X259 81.500000 1.2121212 FALSE TRUE
## X260 2.837209 1.2121212 FALSE FALSE
## X261 3.024390 1.2121212 FALSE FALSE
## X262 3.230769 1.2121212 FALSE FALSE
## X263 2.055556 1.2121212 FALSE FALSE
## X264 1.661290 1.2121212 FALSE FALSE
## X265 2.837209 1.2121212 FALSE FALSE
## X266 3.230769 1.2121212 FALSE FALSE
## X267 1.062500 1.2121212 FALSE FALSE
## X268 2.235294 1.2121212 FALSE FALSE
## X269 1.115385 1.2121212 FALSE FALSE
## X270 1.088608 1.2121212 FALSE FALSE
## X271 1.894737 1.2121212 FALSE FALSE
## X272 1.462687 1.2121212 FALSE FALSE
## X273 164.000000 1.2121212 FALSE TRUE
## X274 1.894737 1.2121212 FALSE FALSE
## X275 19.625000 1.2121212 FALSE TRUE
## X276 4.689655 1.2121212 FALSE FALSE
## X277 81.500000 1.2121212 FALSE TRUE
## X278 4.322581 1.2121212 FALSE FALSE
## X279 4.322581 1.2121212 FALSE FALSE
## X280 4.500000 1.2121212 FALSE FALSE
## X281 4.500000 1.2121212 FALSE FALSE
## X282 26.500000 1.2121212 FALSE TRUE
## X283 81.500000 1.2121212 FALSE TRUE
## X284 4.322581 1.2121212 FALSE FALSE
## X285 4.322581 1.2121212 FALSE FALSE
## X286 4.322581 1.2121212 FALSE FALSE
## X287 81.500000 1.2121212 FALSE TRUE
## X288 81.500000 1.2121212 FALSE TRUE
## X289 26.500000 1.2121212 FALSE TRUE
## X290 4.322581 1.2121212 FALSE FALSE
## X291 4.322581 1.2121212 FALSE FALSE
## X292 26.500000 1.2121212 FALSE TRUE
## X293 10.785714 1.2121212 FALSE FALSE
## X294 10.000000 1.2121212 FALSE FALSE
## X295 8.705882 1.2121212 FALSE FALSE
## X296 2.173077 1.2121212 FALSE FALSE
## X297 6.500000 1.2121212 FALSE FALSE
## X298 4.500000 1.2121212 FALSE FALSE
## X299 4.500000 1.2121212 FALSE FALSE
## X300 4.500000 1.2121212 FALSE FALSE
## X301 2.173077 1.2121212 FALSE FALSE
## X302 2.173077 1.2121212 FALSE FALSE
## X303 6.500000 1.2121212 FALSE FALSE
## X304 4.500000 1.2121212 FALSE FALSE
## X305 4.500000 1.2121212 FALSE FALSE
## X306 11.692308 1.2121212 FALSE FALSE
## X307 10.000000 1.2121212 FALSE FALSE
## X308 10.000000 1.2121212 FALSE FALSE
## X309 6.857143 1.2121212 FALSE FALSE
## X310 4.500000 1.2121212 FALSE FALSE
## X311 3.230769 1.2121212 FALSE FALSE
## X312 2.586957 1.2121212 FALSE FALSE
## X313 3.024390 1.2121212 FALSE FALSE
## X314 2.837209 1.2121212 FALSE FALSE
## X315 1.462687 1.2121212 FALSE FALSE
## X316 10.000000 1.2121212 FALSE FALSE
## X317 10.000000 1.2121212 FALSE FALSE
## X318 10.000000 1.2121212 FALSE FALSE
## X319 4.156250 1.2121212 FALSE FALSE
## X320 4.322581 1.2121212 FALSE FALSE
## X321 4.322581 1.2121212 FALSE FALSE
## X322 3.125000 1.2121212 FALSE FALSE
## X323 3.714286 1.2121212 FALSE FALSE
## X324 3.714286 1.2121212 FALSE FALSE
## X325 2.586957 1.2121212 FALSE FALSE
## X326 3.024390 1.2121212 FALSE FALSE
## X327 3.024390 1.2121212 FALSE FALSE
## X328 3.024390 1.2121212 FALSE FALSE
## X329 6.173913 1.2121212 FALSE FALSE
## X330 3.024390 1.2121212 FALSE FALSE
## X331 3.024390 1.2121212 FALSE FALSE
## X332 3.024390 1.2121212 FALSE FALSE
## X333 3.024390 1.2121212 FALSE FALSE
## X334 10.000000 1.2121212 FALSE FALSE
## X335 6.173913 1.2121212 FALSE FALSE
## X336 8.166667 1.2121212 FALSE FALSE
## X337 11.692308 1.2121212 FALSE FALSE
## X338 2.113208 1.2121212 FALSE FALSE
## X339 2.113208 1.2121212 FALSE FALSE
## X340 7.684211 1.2121212 FALSE FALSE
## X341 2.113208 1.2121212 FALSE FALSE
## X342 2.235294 1.2121212 FALSE FALSE
## X343 2.235294 1.2121212 FALSE FALSE
## X344 7.684211 1.2121212 FALSE FALSE
## X345 17.333333 1.2121212 FALSE FALSE
## X346 54.000000 1.2121212 FALSE TRUE
## X347 26.500000 1.2121212 FALSE TRUE
## X348 26.500000 1.2121212 FALSE TRUE
## X349 40.250000 1.2121212 FALSE TRUE
## X350 54.000000 1.2121212 FALSE TRUE
## X351 54.000000 1.2121212 FALSE TRUE
## X352 54.000000 1.2121212 FALSE TRUE
## X353 54.000000 1.2121212 FALSE TRUE
## X354 54.000000 1.2121212 FALSE TRUE
## X355 3.125000 1.2121212 FALSE FALSE
## X356 2.837209 1.2121212 FALSE FALSE
## X357 3.583333 1.2121212 FALSE FALSE
## X358 3.230769 1.2121212 FALSE FALSE
## X359 4.689655 1.2121212 FALSE FALSE
## X360 4.689655 1.2121212 FALSE FALSE
## X361 7.250000 1.2121212 FALSE FALSE
## X362 5.111111 1.2121212 FALSE FALSE
## X363 40.250000 1.2121212 FALSE TRUE
## X364 40.250000 1.2121212 FALSE TRUE
## X365 54.000000 1.2121212 FALSE TRUE
## X366 4.500000 1.2121212 FALSE FALSE
## X367 3.583333 1.2121212 FALSE FALSE
## X368 2.837209 1.2121212 FALSE FALSE
## X369 26.500000 1.2121212 FALSE TRUE
## X370 3.342105 1.2121212 FALSE FALSE
## X371 3.230769 1.2121212 FALSE FALSE
## X372 11.692308 1.2121212 FALSE FALSE
## X373 11.692308 1.2121212 FALSE FALSE
## X374 6.857143 1.2121212 FALSE FALSE
## X375 26.500000 1.2121212 FALSE TRUE
## X376 10.785714 1.2121212 FALSE FALSE
## X377 9.312500 1.2121212 FALSE FALSE
## X378 9.312500 1.2121212 FALSE FALSE
## X379 40.250000 1.2121212 FALSE TRUE
## X380 9.312500 1.2121212 FALSE FALSE
## X381 9.312500 1.2121212 FALSE FALSE
## X382 9.312500 1.2121212 FALSE FALSE
## X383 9.312500 1.2121212 FALSE FALSE
## X384 40.250000 1.2121212 FALSE TRUE
## X385 9.312500 1.2121212 FALSE FALSE
## X386 9.312500 1.2121212 FALSE FALSE
## X387 9.312500 1.2121212 FALSE FALSE
## X388 9.312500 1.2121212 FALSE FALSE
## X389 9.312500 1.2121212 FALSE FALSE
## X390 9.312500 1.2121212 FALSE FALSE
## X391 26.500000 1.2121212 FALSE TRUE
## X392 9.312500 1.2121212 FALSE FALSE
## X393 32.000000 1.2121212 FALSE TRUE
## X394 9.312500 1.2121212 FALSE FALSE
## X395 9.312500 1.2121212 FALSE FALSE
## X396 9.312500 1.2121212 FALSE FALSE
## X397 40.250000 1.2121212 FALSE TRUE
## X398 9.312500 1.2121212 FALSE FALSE
## X399 32.000000 1.2121212 FALSE TRUE
## X400 9.312500 1.2121212 FALSE FALSE
## X401 9.312500 1.2121212 FALSE FALSE
## X402 32.000000 1.2121212 FALSE TRUE
## X403 9.312500 1.2121212 FALSE FALSE
## X404 26.500000 1.2121212 FALSE TRUE
## X405 54.000000 1.2121212 FALSE TRUE
## X406 9.312500 1.2121212 FALSE FALSE
## X407 26.500000 1.2121212 FALSE TRUE
## X408 22.571429 1.2121212 FALSE TRUE
## X409 22.571429 1.2121212 FALSE TRUE
## X410 32.000000 1.2121212 FALSE TRUE
## X411 22.571429 1.2121212 FALSE TRUE
## X412 32.000000 1.2121212 FALSE TRUE
## X413 32.000000 1.2121212 FALSE TRUE
## X414 32.000000 1.2121212 FALSE TRUE
## X415 164.000000 1.2121212 FALSE TRUE
## X416 164.000000 1.2121212 FALSE TRUE
## X417 164.000000 1.2121212 FALSE TRUE
## X418 164.000000 1.2121212 FALSE TRUE
## X419 164.000000 1.2121212 FALSE TRUE
## X420 164.000000 1.2121212 FALSE TRUE
## X421 164.000000 1.2121212 FALSE TRUE
## X422 164.000000 1.2121212 FALSE TRUE
## X423 164.000000 1.2121212 FALSE TRUE
## X424 164.000000 1.2121212 FALSE TRUE
## X425 164.000000 1.2121212 FALSE TRUE
## X426 164.000000 1.2121212 FALSE TRUE
## X427 164.000000 1.2121212 FALSE TRUE
## X428 164.000000 1.2121212 FALSE TRUE
## X429 164.000000 1.2121212 FALSE TRUE
## X430 164.000000 1.2121212 FALSE TRUE
## X431 164.000000 1.2121212 FALSE TRUE
## X432 164.000000 1.2121212 FALSE TRUE
## X433 164.000000 1.2121212 FALSE TRUE
## X434 164.000000 1.2121212 FALSE TRUE
## X435 164.000000 1.2121212 FALSE TRUE
## X436 164.000000 1.2121212 FALSE TRUE
## X437 164.000000 1.2121212 FALSE TRUE
## X438 164.000000 1.2121212 FALSE TRUE
## X439 26.500000 1.2121212 FALSE TRUE
## X440 26.500000 1.2121212 FALSE TRUE
## X441 40.250000 1.2121212 FALSE TRUE
## X442 164.000000 1.2121212 FALSE TRUE
## X443 164.000000 1.2121212 FALSE TRUE
## X444 164.000000 1.2121212 FALSE TRUE
## X445 164.000000 1.2121212 FALSE TRUE
## X446 164.000000 1.2121212 FALSE TRUE
## X447 40.250000 1.2121212 FALSE TRUE
## X448 164.000000 1.2121212 FALSE TRUE
## X449 164.000000 1.2121212 FALSE TRUE
## X450 164.000000 1.2121212 FALSE TRUE
## X451 164.000000 1.2121212 FALSE TRUE
## X452 164.000000 1.2121212 FALSE TRUE
## X453 26.500000 1.2121212 FALSE TRUE
## X454 26.500000 1.2121212 FALSE TRUE
## X455 26.500000 1.2121212 FALSE TRUE
## X456 164.000000 1.2121212 FALSE TRUE
## X457 54.000000 1.2121212 FALSE TRUE
## X458 164.000000 1.2121212 FALSE TRUE
## X459 164.000000 1.2121212 FALSE TRUE
## X460 164.000000 1.2121212 FALSE TRUE
## X461 164.000000 1.2121212 FALSE TRUE
## X462 164.000000 1.2121212 FALSE TRUE
## X463 164.000000 1.2121212 FALSE TRUE
## X464 164.000000 1.2121212 FALSE TRUE
## X465 164.000000 1.2121212 FALSE TRUE
## X466 164.000000 1.2121212 FALSE TRUE
## X467 164.000000 1.2121212 FALSE TRUE
## X468 164.000000 1.2121212 FALSE TRUE
## X469 164.000000 1.2121212 FALSE TRUE
## X470 164.000000 1.2121212 FALSE TRUE
## X471 164.000000 1.2121212 FALSE TRUE
## X472 164.000000 1.2121212 FALSE TRUE
## X473 164.000000 1.2121212 FALSE TRUE
## X474 164.000000 1.2121212 FALSE TRUE
## X475 81.500000 1.2121212 FALSE TRUE
## X476 81.500000 1.2121212 FALSE TRUE
## X477 81.500000 1.2121212 FALSE TRUE
## X478 81.500000 1.2121212 FALSE TRUE
## X479 81.500000 1.2121212 FALSE TRUE
## X480 81.500000 1.2121212 FALSE TRUE
## X481 81.500000 1.2121212 FALSE TRUE
## X482 81.500000 1.2121212 FALSE TRUE
## X483 81.500000 1.2121212 FALSE TRUE
## X484 81.500000 1.2121212 FALSE TRUE
## X485 81.500000 1.2121212 FALSE TRUE
## X486 81.500000 1.2121212 FALSE TRUE
## X487 81.500000 1.2121212 FALSE TRUE
## X488 81.500000 1.2121212 FALSE TRUE
## X489 81.500000 1.2121212 FALSE TRUE
## X490 81.500000 1.2121212 FALSE TRUE
## X491 81.500000 1.2121212 FALSE TRUE
## X492 81.500000 1.2121212 FALSE TRUE
## X493 81.500000 1.2121212 FALSE TRUE
## X494 81.500000 1.2121212 FALSE TRUE
## X495 81.500000 1.2121212 FALSE TRUE
## X496 6.173913 1.2121212 FALSE FALSE
## X497 5.875000 1.2121212 FALSE FALSE
## X498 26.500000 1.2121212 FALSE TRUE
## X499 5.600000 1.2121212 FALSE FALSE
## X500 19.625000 1.2121212 FALSE TRUE
## X501 19.625000 1.2121212 FALSE TRUE
## X502 81.500000 1.2121212 FALSE TRUE
## X503 7.250000 1.2121212 FALSE FALSE
## X504 3.583333 1.2121212 FALSE FALSE
## X505 3.583333 1.2121212 FALSE FALSE
## X506 3.583333 1.2121212 FALSE FALSE
## X507 4.892857 1.2121212 FALSE FALSE
## X508 5.600000 1.2121212 FALSE FALSE
## X509 4.156250 1.2121212 FALSE FALSE
## X510 9.312500 1.2121212 FALSE FALSE
## X511 12.750000 1.2121212 FALSE FALSE
## X512 10.000000 1.2121212 FALSE FALSE
## X513 81.500000 1.2121212 FALSE TRUE
## X514 12.750000 1.2121212 FALSE FALSE
## X515 12.750000 1.2121212 FALSE FALSE
## X516 10.785714 1.2121212 FALSE FALSE
## X517 10.785714 1.2121212 FALSE FALSE
## X518 12.750000 1.2121212 FALSE FALSE
## X519 10.000000 1.2121212 FALSE FALSE
## X520 8.705882 1.2121212 FALSE FALSE
## X521 8.705882 1.2121212 FALSE FALSE
## X522 12.750000 1.2121212 FALSE FALSE
## X523 81.500000 1.2121212 FALSE TRUE
## X524 10.785714 1.2121212 FALSE FALSE
## X525 81.500000 1.2121212 FALSE TRUE
## X526 81.500000 1.2121212 FALSE TRUE
## X527 81.500000 1.2121212 FALSE TRUE
## X528 0.000000 0.6060606 TRUE TRUE
## X529 12.750000 1.2121212 FALSE FALSE
## X530 40.250000 1.2121212 FALSE TRUE
## X531 32.000000 1.2121212 FALSE TRUE
## X532 32.000000 1.2121212 FALSE TRUE
## X533 32.000000 1.2121212 FALSE TRUE
## X534 32.000000 1.2121212 FALSE TRUE
## X535 32.000000 1.2121212 FALSE TRUE
## X536 40.250000 1.2121212 FALSE TRUE
## X537 40.250000 1.2121212 FALSE TRUE
## X538 40.250000 1.2121212 FALSE TRUE
## X539 40.250000 1.2121212 FALSE TRUE
## X540 40.250000 1.2121212 FALSE TRUE
## X541 40.250000 1.2121212 FALSE TRUE
## X542 40.250000 1.2121212 FALSE TRUE
## X543 40.250000 1.2121212 FALSE TRUE
## X544 32.000000 1.2121212 FALSE TRUE
## X545 32.000000 1.2121212 FALSE TRUE
## X546 40.250000 1.2121212 FALSE TRUE
## X547 40.250000 1.2121212 FALSE TRUE
## X548 32.000000 1.2121212 FALSE TRUE
## X549 3.459459 1.2121212 FALSE FALSE
## X550 40.250000 1.2121212 FALSE TRUE
## X551 15.500000 1.2121212 FALSE FALSE
## X552 40.250000 1.2121212 FALSE TRUE
## X553 15.500000 1.2121212 FALSE FALSE
## X554 15.500000 1.2121212 FALSE FALSE
## X555 40.250000 1.2121212 FALSE TRUE
## X556 3.459459 1.2121212 FALSE FALSE
## X557 3.459459 1.2121212 FALSE FALSE
## X558 3.459459 1.2121212 FALSE FALSE
## X559 3.459459 1.2121212 FALSE FALSE
## X560 3.459459 1.2121212 FALSE FALSE
## X561 17.333333 1.2121212 FALSE FALSE
## X562 19.625000 1.2121212 FALSE TRUE
## X563 19.625000 1.2121212 FALSE TRUE
## X564 19.625000 1.2121212 FALSE TRUE
## X565 3.459459 1.2121212 FALSE FALSE
## X566 19.625000 1.2121212 FALSE TRUE
## X567 164.000000 1.2121212 FALSE TRUE
## X568 17.333333 1.2121212 FALSE FALSE
## X569 0.000000 0.6060606 TRUE TRUE
## X570 0.000000 0.6060606 TRUE TRUE
## X571 5.346154 1.2121212 FALSE FALSE
## X572 22.571429 1.2121212 FALSE TRUE
## X573 4.322581 1.2121212 FALSE FALSE
## X574 5.346154 1.2121212 FALSE FALSE
## X575 40.250000 1.2121212 FALSE TRUE
## X576 5.346154 1.2121212 FALSE FALSE
## X577 10.000000 1.2121212 FALSE FALSE
## X578 40.250000 1.2121212 FALSE TRUE
## X579 0.000000 0.6060606 TRUE TRUE
## X580 0.000000 0.6060606 TRUE TRUE
## X581 0.000000 0.6060606 TRUE TRUE
## X582 22.571429 1.2121212 FALSE TRUE
## X583 22.571429 1.2121212 FALSE TRUE
## X584 32.000000 1.2121212 FALSE TRUE
## X585 32.000000 1.2121212 FALSE TRUE
## X586 22.571429 1.2121212 FALSE TRUE
## X587 19.625000 1.2121212 FALSE TRUE
## X588 32.000000 1.2121212 FALSE TRUE
## X589 32.000000 1.2121212 FALSE TRUE
## X590 14.000000 1.2121212 FALSE FALSE
## X591 14.000000 1.2121212 FALSE FALSE
## X592 10.785714 1.2121212 FALSE FALSE
## X593 10.785714 1.2121212 FALSE FALSE
## X594 11.692308 1.2121212 FALSE FALSE
## X595 14.000000 1.2121212 FALSE FALSE
## X596 0.000000 0.6060606 TRUE TRUE
## X597 6.857143 1.2121212 FALSE FALSE
## X598 6.500000 1.2121212 FALSE FALSE
## X599 4.500000 1.2121212 FALSE FALSE
## X600 6.857143 1.2121212 FALSE FALSE
## X601 4.500000 1.2121212 FALSE FALSE
## X602 4.322581 1.2121212 FALSE FALSE
## X603 4.500000 1.2121212 FALSE FALSE
## X604 7.250000 1.2121212 FALSE FALSE
## X605 81.500000 1.2121212 FALSE TRUE
## X606 164.000000 1.2121212 FALSE TRUE
## X607 164.000000 1.2121212 FALSE TRUE
## X608 164.000000 1.2121212 FALSE TRUE
## X609 164.000000 1.2121212 FALSE TRUE
## X610 164.000000 1.2121212 FALSE TRUE
## X611 164.000000 1.2121212 FALSE TRUE
## X612 164.000000 1.2121212 FALSE TRUE
## X613 15.500000 1.2121212 FALSE FALSE
## X614 22.571429 1.2121212 FALSE TRUE
## X615 22.571429 1.2121212 FALSE TRUE
## X616 22.571429 1.2121212 FALSE TRUE
## X617 22.571429 1.2121212 FALSE TRUE
## X618 22.571429 1.2121212 FALSE TRUE
## X619 22.571429 1.2121212 FALSE TRUE
## X620 22.571429 1.2121212 FALSE TRUE
## X621 15.500000 1.2121212 FALSE FALSE
## X622 22.571429 1.2121212 FALSE TRUE
## X623 164.000000 1.2121212 FALSE TRUE
## X624 81.500000 1.2121212 FALSE TRUE
## X625 81.500000 1.2121212 FALSE TRUE
## X626 19.625000 1.2121212 FALSE TRUE
## X627 164.000000 1.2121212 FALSE TRUE
## X628 164.000000 1.2121212 FALSE TRUE
## X629 164.000000 1.2121212 FALSE TRUE
## X630 164.000000 1.2121212 FALSE TRUE
## X631 164.000000 1.2121212 FALSE TRUE
## X632 164.000000 1.2121212 FALSE TRUE
## X633 164.000000 1.2121212 FALSE TRUE
## X634 164.000000 1.2121212 FALSE TRUE
## X635 164.000000 1.2121212 FALSE TRUE
## X636 164.000000 1.2121212 FALSE TRUE
## X637 164.000000 1.2121212 FALSE TRUE
## X638 164.000000 1.2121212 FALSE TRUE
## X639 164.000000 1.2121212 FALSE TRUE
## X640 164.000000 1.2121212 FALSE TRUE
## X641 81.500000 1.2121212 FALSE TRUE
## X642 81.500000 1.2121212 FALSE TRUE
## X643 81.500000 1.2121212 FALSE TRUE
## X644 81.500000 1.2121212 FALSE TRUE
## X645 164.000000 1.2121212 FALSE TRUE
## X646 81.500000 1.2121212 FALSE TRUE
## X647 164.000000 1.2121212 FALSE TRUE
## X648 54.000000 1.2121212 FALSE TRUE
## X649 19.625000 1.2121212 FALSE TRUE
## X650 81.500000 1.2121212 FALSE TRUE
## X651 54.000000 1.2121212 FALSE TRUE
## X652 54.000000 1.2121212 FALSE TRUE
## X653 19.625000 1.2121212 FALSE TRUE
## X654 19.625000 1.2121212 FALSE TRUE
## X655 19.625000 1.2121212 FALSE TRUE
## X656 19.625000 1.2121212 FALSE TRUE
## X657 19.625000 1.2121212 FALSE TRUE
## X658 19.625000 1.2121212 FALSE TRUE
## X659 19.625000 1.2121212 FALSE TRUE
## X660 32.000000 1.2121212 FALSE TRUE
## X661 32.000000 1.2121212 FALSE TRUE
## X662 81.500000 1.2121212 FALSE TRUE
## X663 32.000000 1.2121212 FALSE TRUE
## X664 32.000000 1.2121212 FALSE TRUE
## X665 19.625000 1.2121212 FALSE TRUE
## X666 81.500000 1.2121212 FALSE TRUE
## X667 26.500000 1.2121212 FALSE TRUE
## X668 32.000000 1.2121212 FALSE TRUE
## X669 32.000000 1.2121212 FALSE TRUE
## X670 32.000000 1.2121212 FALSE TRUE
## X671 32.000000 1.2121212 FALSE TRUE
## X672 32.000000 1.2121212 FALSE TRUE
## X673 32.000000 1.2121212 FALSE TRUE
## X674 32.000000 1.2121212 FALSE TRUE
## X675 32.000000 1.2121212 FALSE TRUE
## X676 32.000000 1.2121212 FALSE TRUE
## X677 26.500000 1.2121212 FALSE TRUE
## X678 54.000000 1.2121212 FALSE TRUE
## X679 10.785714 1.2121212 FALSE FALSE
## X680 164.000000 1.2121212 FALSE TRUE
## X681 81.500000 1.2121212 FALSE TRUE
## X682 164.000000 1.2121212 FALSE TRUE
## X683 164.000000 1.2121212 FALSE TRUE
## X684 164.000000 1.2121212 FALSE TRUE
## X685 164.000000 1.2121212 FALSE TRUE
## X686 164.000000 1.2121212 FALSE TRUE
## X687 164.000000 1.2121212 FALSE TRUE
## X688 164.000000 1.2121212 FALSE TRUE
## X689 164.000000 1.2121212 FALSE TRUE
## X690 164.000000 1.2121212 FALSE TRUE
## X691 0.000000 0.6060606 TRUE TRUE
## X692 0.000000 0.6060606 TRUE TRUE
## X693 0.000000 0.6060606 TRUE TRUE
## X694 0.000000 0.6060606 TRUE TRUE
## X695 0.000000 0.6060606 TRUE TRUE
## X696 22.571429 1.2121212 FALSE TRUE
## X697 54.000000 1.2121212 FALSE TRUE
## X698 15.500000 1.2121212 FALSE FALSE
## X699 10.000000 1.2121212 FALSE FALSE
## X700 8.166667 1.2121212 FALSE FALSE
## X701 8.166667 1.2121212 FALSE FALSE
## X702 8.166667 1.2121212 FALSE FALSE
## X703 10.000000 1.2121212 FALSE FALSE
## X704 10.785714 1.2121212 FALSE FALSE
## X705 8.166667 1.2121212 FALSE FALSE
## X706 32.000000 1.2121212 FALSE TRUE
## X707 40.250000 1.2121212 FALSE TRUE
## X708 32.000000 1.2121212 FALSE TRUE
## X709 32.000000 1.2121212 FALSE TRUE
## X710 32.000000 1.2121212 FALSE TRUE
## X711 40.250000 1.2121212 FALSE TRUE
## X712 32.000000 1.2121212 FALSE TRUE
## X713 32.000000 1.2121212 FALSE TRUE
## X714 32.000000 1.2121212 FALSE TRUE
## X715 32.000000 1.2121212 FALSE TRUE
## X716 26.500000 1.2121212 FALSE TRUE
## X717 26.500000 1.2121212 FALSE TRUE
## X718 26.500000 1.2121212 FALSE TRUE
## X719 15.500000 1.2121212 FALSE FALSE
## X720 40.250000 1.2121212 FALSE TRUE
## X721 40.250000 1.2121212 FALSE TRUE
## X722 54.000000 1.2121212 FALSE TRUE
## X723 54.000000 1.2121212 FALSE TRUE
## X724 81.500000 1.2121212 FALSE TRUE
## X725 81.500000 1.2121212 FALSE TRUE
## X726 81.500000 1.2121212 FALSE TRUE
## X727 81.500000 1.2121212 FALSE TRUE
## X728 81.500000 1.2121212 FALSE TRUE
## X729 81.500000 1.2121212 FALSE TRUE
## X730 0.000000 0.6060606 TRUE TRUE
## X731 0.000000 0.6060606 TRUE TRUE
## X732 15.500000 1.2121212 FALSE FALSE
## X733 15.500000 1.2121212 FALSE FALSE
## X734 54.000000 1.2121212 FALSE TRUE
## X735 54.000000 1.2121212 FALSE TRUE
## X736 164.000000 1.2121212 FALSE TRUE
## X737 164.000000 1.2121212 FALSE TRUE
## X738 164.000000 1.2121212 FALSE TRUE
## X739 164.000000 1.2121212 FALSE TRUE
## X740 81.500000 1.2121212 FALSE TRUE
## X741 164.000000 1.2121212 FALSE TRUE
## X742 164.000000 1.2121212 FALSE TRUE
## X743 164.000000 1.2121212 FALSE TRUE
## X744 164.000000 1.2121212 FALSE TRUE
## X745 164.000000 1.2121212 FALSE TRUE
## X746 164.000000 1.2121212 FALSE TRUE
## X747 164.000000 1.2121212 FALSE TRUE
## X748 164.000000 1.2121212 FALSE TRUE
## X749 164.000000 1.2121212 FALSE TRUE
## X750 17.333333 1.2121212 FALSE FALSE
## X751 11.692308 1.2121212 FALSE FALSE
## X752 17.333333 1.2121212 FALSE FALSE
## X753 17.333333 1.2121212 FALSE FALSE
## X754 11.692308 1.2121212 FALSE FALSE
## X755 11.692308 1.2121212 FALSE FALSE
## X756 164.000000 1.2121212 FALSE TRUE
## X757 164.000000 1.2121212 FALSE TRUE
## X758 164.000000 1.2121212 FALSE TRUE
## X759 164.000000 1.2121212 FALSE TRUE
## X760 164.000000 1.2121212 FALSE TRUE
## X761 164.000000 1.2121212 FALSE TRUE
## X762 164.000000 1.2121212 FALSE TRUE
## X763 54.000000 1.2121212 FALSE TRUE
## X764 54.000000 1.2121212 FALSE TRUE
## X765 54.000000 1.2121212 FALSE TRUE
## X766 54.000000 1.2121212 FALSE TRUE
## X767 54.000000 1.2121212 FALSE TRUE
## X768 22.571429 1.2121212 FALSE TRUE
## X769 81.500000 1.2121212 FALSE TRUE
## X770 81.500000 1.2121212 FALSE TRUE
## X771 164.000000 1.2121212 FALSE TRUE
## X772 164.000000 1.2121212 FALSE TRUE
## X773 12.750000 1.2121212 FALSE FALSE
## X774 12.750000 1.2121212 FALSE FALSE
## X775 12.750000 1.2121212 FALSE FALSE
## X776 12.750000 1.2121212 FALSE FALSE
## X777 81.500000 1.2121212 FALSE TRUE
## X778 81.500000 1.2121212 FALSE TRUE
## X779 22.571429 1.2121212 FALSE TRUE
## X780 17.333333 1.2121212 FALSE FALSE
## X781 19.625000 1.2121212 FALSE TRUE
## X782 17.333333 1.2121212 FALSE FALSE
## X783 0.000000 0.6060606 TRUE TRUE
## X784 19.625000 1.2121212 FALSE TRUE
## X785 0.000000 0.6060606 TRUE TRUE
## X786 0.000000 0.6060606 TRUE TRUE
## X787 0.000000 0.6060606 TRUE TRUE
## X788 0.000000 0.6060606 TRUE TRUE
## X789 0.000000 0.6060606 TRUE TRUE
## X790 81.500000 1.2121212 FALSE TRUE
## X791 81.500000 1.2121212 FALSE TRUE
## X792 17.333333 1.2121212 FALSE FALSE
## X793 17.333333 1.2121212 FALSE FALSE
## X794 81.500000 1.2121212 FALSE TRUE
## X795 12.750000 1.2121212 FALSE FALSE
## X796 32.000000 1.2121212 FALSE TRUE
## X797 81.500000 1.2121212 FALSE TRUE
## X798 12.750000 1.2121212 FALSE FALSE
## X799 81.500000 1.2121212 FALSE TRUE
## X800 17.333333 1.2121212 FALSE FALSE
## X801 17.333333 1.2121212 FALSE FALSE
## X802 19.625000 1.2121212 FALSE TRUE
## X803 81.500000 1.2121212 FALSE TRUE
## X804 81.500000 1.2121212 FALSE TRUE
## X805 12.750000 1.2121212 FALSE FALSE
## X806 17.333333 1.2121212 FALSE FALSE
## X807 81.500000 1.2121212 FALSE TRUE
## X808 81.500000 1.2121212 FALSE TRUE
## X809 32.000000 1.2121212 FALSE TRUE
## X810 81.500000 1.2121212 FALSE TRUE
## X811 81.500000 1.2121212 FALSE TRUE
## X812 17.333333 1.2121212 FALSE FALSE
## X813 17.333333 1.2121212 FALSE FALSE
## X814 54.000000 1.2121212 FALSE TRUE
## X815 164.000000 1.2121212 FALSE TRUE
## X816 164.000000 1.2121212 FALSE TRUE
## X817 164.000000 1.2121212 FALSE TRUE
## X818 164.000000 1.2121212 FALSE TRUE
## X819 164.000000 1.2121212 FALSE TRUE
## X820 164.000000 1.2121212 FALSE TRUE
## X821 164.000000 1.2121212 FALSE TRUE
## X822 164.000000 1.2121212 FALSE TRUE
## X823 164.000000 1.2121212 FALSE TRUE
## X824 164.000000 1.2121212 FALSE TRUE
## X825 164.000000 1.2121212 FALSE TRUE
## X826 164.000000 1.2121212 FALSE TRUE
## X827 164.000000 1.2121212 FALSE TRUE
## X828 164.000000 1.2121212 FALSE TRUE
## X829 164.000000 1.2121212 FALSE TRUE
## X830 164.000000 1.2121212 FALSE TRUE
## X831 164.000000 1.2121212 FALSE TRUE
## X832 164.000000 1.2121212 FALSE TRUE
## X833 164.000000 1.2121212 FALSE TRUE
## X834 40.250000 1.2121212 FALSE TRUE
## X835 40.250000 1.2121212 FALSE TRUE
## X836 164.000000 1.2121212 FALSE TRUE
## X837 164.000000 1.2121212 FALSE TRUE
## X838 164.000000 1.2121212 FALSE TRUE
## X839 164.000000 1.2121212 FALSE TRUE
## X840 81.500000 1.2121212 FALSE TRUE
## X841 164.000000 1.2121212 FALSE TRUE
## X842 164.000000 1.2121212 FALSE TRUE
## X843 81.500000 1.2121212 FALSE TRUE
## X844 164.000000 1.2121212 FALSE TRUE
## X845 81.500000 1.2121212 FALSE TRUE
## X846 81.500000 1.2121212 FALSE TRUE
## X847 164.000000 1.2121212 FALSE TRUE
## X848 164.000000 1.2121212 FALSE TRUE
## X849 164.000000 1.2121212 FALSE TRUE
## X850 164.000000 1.2121212 FALSE TRUE
## X851 164.000000 1.2121212 FALSE TRUE
## X852 164.000000 1.2121212 FALSE TRUE
## X853 164.000000 1.2121212 FALSE TRUE
## X854 164.000000 1.2121212 FALSE TRUE
## X855 164.000000 1.2121212 FALSE TRUE
## X856 164.000000 1.2121212 FALSE TRUE
## X857 164.000000 1.2121212 FALSE TRUE
## X858 164.000000 1.2121212 FALSE TRUE
## X859 164.000000 1.2121212 FALSE TRUE
## X860 164.000000 1.2121212 FALSE TRUE
## X861 164.000000 1.2121212 FALSE TRUE
## X862 81.500000 1.2121212 FALSE TRUE
## X863 164.000000 1.2121212 FALSE TRUE
## X864 164.000000 1.2121212 FALSE TRUE
## X865 164.000000 1.2121212 FALSE TRUE
## X866 164.000000 1.2121212 FALSE TRUE
## X867 164.000000 1.2121212 FALSE TRUE
## X868 164.000000 1.2121212 FALSE TRUE
## X869 40.250000 1.2121212 FALSE TRUE
## X870 40.250000 1.2121212 FALSE TRUE
## X871 40.250000 1.2121212 FALSE TRUE
## X872 40.250000 1.2121212 FALSE TRUE
## X873 40.250000 1.2121212 FALSE TRUE
## X874 40.250000 1.2121212 FALSE TRUE
## X875 40.250000 1.2121212 FALSE TRUE
## X876 40.250000 1.2121212 FALSE TRUE
## X877 40.250000 1.2121212 FALSE TRUE
## X878 40.250000 1.2121212 FALSE TRUE
## X879 32.000000 1.2121212 FALSE TRUE
## X880 32.000000 1.2121212 FALSE TRUE
## X881 40.250000 1.2121212 FALSE TRUE
## X882 54.000000 1.2121212 FALSE TRUE
## X883 164.000000 1.2121212 FALSE TRUE
## X884 54.000000 1.2121212 FALSE TRUE
## X885 54.000000 1.2121212 FALSE TRUE
## X886 164.000000 1.2121212 FALSE TRUE
## X887 164.000000 1.2121212 FALSE TRUE
## X888 164.000000 1.2121212 FALSE TRUE
## X889 164.000000 1.2121212 FALSE TRUE
## X890 54.000000 1.2121212 FALSE TRUE
## X891 54.000000 1.2121212 FALSE TRUE
## X892 54.000000 1.2121212 FALSE TRUE
## X893 54.000000 1.2121212 FALSE TRUE
## X894 54.000000 1.2121212 FALSE TRUE
## X895 54.000000 1.2121212 FALSE TRUE
## X896 54.000000 1.2121212 FALSE TRUE
## X897 164.000000 1.2121212 FALSE TRUE
## X898 164.000000 1.2121212 FALSE TRUE
## X899 164.000000 1.2121212 FALSE TRUE
## X900 164.000000 1.2121212 FALSE TRUE
## X901 164.000000 1.2121212 FALSE TRUE
## X902 81.500000 1.2121212 FALSE TRUE
## X903 81.500000 1.2121212 FALSE TRUE
## X904 81.500000 1.2121212 FALSE TRUE
## X905 54.000000 1.2121212 FALSE TRUE
## X906 54.000000 1.2121212 FALSE TRUE
## X907 81.500000 1.2121212 FALSE TRUE
## X908 164.000000 1.2121212 FALSE TRUE
## X909 164.000000 1.2121212 FALSE TRUE
## X910 164.000000 1.2121212 FALSE TRUE
## X911 164.000000 1.2121212 FALSE TRUE
## X912 164.000000 1.2121212 FALSE TRUE
## X913 164.000000 1.2121212 FALSE TRUE
## X914 164.000000 1.2121212 FALSE TRUE
## X915 164.000000 1.2121212 FALSE TRUE
## X916 164.000000 1.2121212 FALSE TRUE
## X917 164.000000 1.2121212 FALSE TRUE
## X918 164.000000 1.2121212 FALSE TRUE
## X919 164.000000 1.2121212 FALSE TRUE
## X920 164.000000 1.2121212 FALSE TRUE
## X921 164.000000 1.2121212 FALSE TRUE
## X922 164.000000 1.2121212 FALSE TRUE
## X923 164.000000 1.2121212 FALSE TRUE
## X924 164.000000 1.2121212 FALSE TRUE
## X925 164.000000 1.2121212 FALSE TRUE
## X926 164.000000 1.2121212 FALSE TRUE
## X927 164.000000 1.2121212 FALSE TRUE
## X928 164.000000 1.2121212 FALSE TRUE
## X929 164.000000 1.2121212 FALSE TRUE
## X930 164.000000 1.2121212 FALSE TRUE
## X931 164.000000 1.2121212 FALSE TRUE
## X932 164.000000 1.2121212 FALSE TRUE
## X933 164.000000 1.2121212 FALSE TRUE
## X934 164.000000 1.2121212 FALSE TRUE
## X935 164.000000 1.2121212 FALSE TRUE
## X936 164.000000 1.2121212 FALSE TRUE
## X937 164.000000 1.2121212 FALSE TRUE
## X938 164.000000 1.2121212 FALSE TRUE
## X939 164.000000 1.2121212 FALSE TRUE
## X940 164.000000 1.2121212 FALSE TRUE
## X941 164.000000 1.2121212 FALSE TRUE
## X942 54.000000 1.2121212 FALSE TRUE
## X943 54.000000 1.2121212 FALSE TRUE
## X944 54.000000 1.2121212 FALSE TRUE
## X945 54.000000 1.2121212 FALSE TRUE
## X946 40.250000 1.2121212 FALSE TRUE
## X947 164.000000 1.2121212 FALSE TRUE
## X948 40.250000 1.2121212 FALSE TRUE
## X949 164.000000 1.2121212 FALSE TRUE
## X950 81.500000 1.2121212 FALSE TRUE
## X951 40.250000 1.2121212 FALSE TRUE
## X952 40.250000 1.2121212 FALSE TRUE
## X953 40.250000 1.2121212 FALSE TRUE
## X954 81.500000 1.2121212 FALSE TRUE
## X955 81.500000 1.2121212 FALSE TRUE
## X956 81.500000 1.2121212 FALSE TRUE
## X957 81.500000 1.2121212 FALSE TRUE
## X958 81.500000 1.2121212 FALSE TRUE
## X959 81.500000 1.2121212 FALSE TRUE
## X960 81.500000 1.2121212 FALSE TRUE
## X961 81.500000 1.2121212 FALSE TRUE
## X962 81.500000 1.2121212 FALSE TRUE
## X963 81.500000 1.2121212 FALSE TRUE
## X964 81.500000 1.2121212 FALSE TRUE
## X965 81.500000 1.2121212 FALSE TRUE
## X966 81.500000 1.2121212 FALSE TRUE
## X967 81.500000 1.2121212 FALSE TRUE
## X968 81.500000 1.2121212 FALSE TRUE
## X969 81.500000 1.2121212 FALSE TRUE
## X970 81.500000 1.2121212 FALSE TRUE
## X971 81.500000 1.2121212 FALSE TRUE
## X972 81.500000 1.2121212 FALSE TRUE
## X973 81.500000 1.2121212 FALSE TRUE
## X974 164.000000 1.2121212 FALSE TRUE
## X975 164.000000 1.2121212 FALSE TRUE
## X976 164.000000 1.2121212 FALSE TRUE
## X977 164.000000 1.2121212 FALSE TRUE
## X978 164.000000 1.2121212 FALSE TRUE
## X979 164.000000 1.2121212 FALSE TRUE
## X980 164.000000 1.2121212 FALSE TRUE
## X981 164.000000 1.2121212 FALSE TRUE
## X982 164.000000 1.2121212 FALSE TRUE
## X983 164.000000 1.2121212 FALSE TRUE
## X984 164.000000 1.2121212 FALSE TRUE
## X985 164.000000 1.2121212 FALSE TRUE
## X986 164.000000 1.2121212 FALSE TRUE
## X987 164.000000 1.2121212 FALSE TRUE
## X988 164.000000 1.2121212 FALSE TRUE
## X989 164.000000 1.2121212 FALSE TRUE
## X990 164.000000 1.2121212 FALSE TRUE
## X991 164.000000 1.2121212 FALSE TRUE
## X992 164.000000 1.2121212 FALSE TRUE
## X993 164.000000 1.2121212 FALSE TRUE
## X994 81.500000 1.2121212 FALSE TRUE
## X995 164.000000 1.2121212 FALSE TRUE
## X996 164.000000 1.2121212 FALSE TRUE
## X997 164.000000 1.2121212 FALSE TRUE
## X998 81.500000 1.2121212 FALSE TRUE
## X999 81.500000 1.2121212 FALSE TRUE
## X1000 54.000000 1.2121212 FALSE TRUE
## X1001 54.000000 1.2121212 FALSE TRUE
## X1002 164.000000 1.2121212 FALSE TRUE
## X1003 164.000000 1.2121212 FALSE TRUE
## X1004 164.000000 1.2121212 FALSE TRUE
## X1005 164.000000 1.2121212 FALSE TRUE
## X1006 164.000000 1.2121212 FALSE TRUE
## X1007 164.000000 1.2121212 FALSE TRUE
## X1008 164.000000 1.2121212 FALSE TRUE
## X1009 164.000000 1.2121212 FALSE TRUE
## X1010 164.000000 1.2121212 FALSE TRUE
## X1011 54.000000 1.2121212 FALSE TRUE
## X1012 54.000000 1.2121212 FALSE TRUE
## X1013 164.000000 1.2121212 FALSE TRUE
## X1014 54.000000 1.2121212 FALSE TRUE
## X1015 164.000000 1.2121212 FALSE TRUE
## X1016 22.571429 1.2121212 FALSE TRUE
## X1017 81.500000 1.2121212 FALSE TRUE
## X1018 81.500000 1.2121212 FALSE TRUE
## X1019 81.500000 1.2121212 FALSE TRUE
## X1020 81.500000 1.2121212 FALSE TRUE
## X1021 81.500000 1.2121212 FALSE TRUE
## X1022 81.500000 1.2121212 FALSE TRUE
## X1023 81.500000 1.2121212 FALSE TRUE
## X1024 81.500000 1.2121212 FALSE TRUE
## X1025 81.500000 1.2121212 FALSE TRUE
## X1026 81.500000 1.2121212 FALSE TRUE
## X1027 81.500000 1.2121212 FALSE TRUE
## X1028 81.500000 1.2121212 FALSE TRUE
## X1029 81.500000 1.2121212 FALSE TRUE
## X1030 81.500000 1.2121212 FALSE TRUE
## X1031 164.000000 1.2121212 FALSE TRUE
## X1032 164.000000 1.2121212 FALSE TRUE
## X1033 164.000000 1.2121212 FALSE TRUE
## X1034 164.000000 1.2121212 FALSE TRUE
## X1035 164.000000 1.2121212 FALSE TRUE
## X1036 164.000000 1.2121212 FALSE TRUE
## X1037 164.000000 1.2121212 FALSE TRUE
## X1038 164.000000 1.2121212 FALSE TRUE
## X1039 164.000000 1.2121212 FALSE TRUE
## X1040 164.000000 1.2121212 FALSE TRUE
## X1041 164.000000 1.2121212 FALSE TRUE
## X1042 164.000000 1.2121212 FALSE TRUE
## X1043 164.000000 1.2121212 FALSE TRUE
## X1044 164.000000 1.2121212 FALSE TRUE
## X1045 164.000000 1.2121212 FALSE TRUE
## X1046 164.000000 1.2121212 FALSE TRUE
## X1047 164.000000 1.2121212 FALSE TRUE
## X1048 164.000000 1.2121212 FALSE TRUE
## X1049 164.000000 1.2121212 FALSE TRUE
## X1050 164.000000 1.2121212 FALSE TRUE
## X1051 164.000000 1.2121212 FALSE TRUE
## X1052 164.000000 1.2121212 FALSE TRUE
## X1053 164.000000 1.2121212 FALSE TRUE
## X1054 164.000000 1.2121212 FALSE TRUE
## X1055 81.500000 1.2121212 FALSE TRUE
## X1056 164.000000 1.2121212 FALSE TRUE
## X1057 164.000000 1.2121212 FALSE TRUE
## X1058 164.000000 1.2121212 FALSE TRUE
## X1059 164.000000 1.2121212 FALSE TRUE
## X1060 164.000000 1.2121212 FALSE TRUE
## X1061 164.000000 1.2121212 FALSE TRUE
## X1062 81.500000 1.2121212 FALSE TRUE
## X1063 81.500000 1.2121212 FALSE TRUE
## X1064 81.500000 1.2121212 FALSE TRUE
## X1065 81.500000 1.2121212 FALSE TRUE
## X1066 81.500000 1.2121212 FALSE TRUE
## X1067 81.500000 1.2121212 FALSE TRUE
## X1068 81.500000 1.2121212 FALSE TRUE
## X1069 81.500000 1.2121212 FALSE TRUE
## X1070 81.500000 1.2121212 FALSE TRUE
## X1071 81.500000 1.2121212 FALSE TRUE
## X1072 81.500000 1.2121212 FALSE TRUE
## X1073 81.500000 1.2121212 FALSE TRUE
## X1074 81.500000 1.2121212 FALSE TRUE
## X1075 81.500000 1.2121212 FALSE TRUE
## X1076 81.500000 1.2121212 FALSE TRUE
## X1077 81.500000 1.2121212 FALSE TRUE
## X1078 81.500000 1.2121212 FALSE TRUE
## X1079 81.500000 1.2121212 FALSE TRUE
## X1080 81.500000 1.2121212 FALSE TRUE
## X1081 164.000000 1.2121212 FALSE TRUE
## X1082 164.000000 1.2121212 FALSE TRUE
## X1083 164.000000 1.2121212 FALSE TRUE
## X1084 164.000000 1.2121212 FALSE TRUE
## X1085 164.000000 1.2121212 FALSE TRUE
## X1086 164.000000 1.2121212 FALSE TRUE
## X1087 164.000000 1.2121212 FALSE TRUE
## X1088 164.000000 1.2121212 FALSE TRUE
## X1089 164.000000 1.2121212 FALSE TRUE
## X1090 164.000000 1.2121212 FALSE TRUE
## X1091 164.000000 1.2121212 FALSE TRUE
## X1092 164.000000 1.2121212 FALSE TRUE
## X1093 164.000000 1.2121212 FALSE TRUE
## X1094 164.000000 1.2121212 FALSE TRUE
## X1095 164.000000 1.2121212 FALSE TRUE
## X1096 164.000000 1.2121212 FALSE TRUE
## X1097 164.000000 1.2121212 FALSE TRUE
## X1098 164.000000 1.2121212 FALSE TRUE
## X1099 164.000000 1.2121212 FALSE TRUE
## X1100 164.000000 1.2121212 FALSE TRUE
## X1101 164.000000 1.2121212 FALSE TRUE
## X1102 164.000000 1.2121212 FALSE TRUE
## X1103 164.000000 1.2121212 FALSE TRUE
## X1104 81.500000 1.2121212 FALSE TRUE
## X1105 164.000000 1.2121212 FALSE TRUE
## X1106 164.000000 1.2121212 FALSE TRUE
## X1107 164.000000 1.2121212 FALSE TRUE#same function, but without NAs, in case NAs are skewing things
fingerprints_no_na <- na.omit(fingerprints)
nzv_results_na <- nearZeroVar(fingerprints_no_na, saveMetrics = TRUE)
nzv_results_na## freqRatio percentUnique zeroVar nzv
## X1 3.342105 1.2121212 FALSE FALSE
## X2 3.459459 1.2121212 FALSE FALSE
## X3 3.714286 1.2121212 FALSE FALSE
## X4 3.714286 1.2121212 FALSE FALSE
## X5 3.714286 1.2121212 FALSE FALSE
## X6 1.229730 1.2121212 FALSE FALSE
## X7 0.000000 0.6060606 TRUE TRUE
## X8 0.000000 0.6060606 TRUE TRUE
## X9 54.000000 1.2121212 FALSE TRUE
## X10 40.250000 1.2121212 FALSE TRUE
## X11 6.173913 1.2121212 FALSE FALSE
## X12 2.437500 1.2121212 FALSE FALSE
## X13 22.571429 1.2121212 FALSE TRUE
## X14 81.500000 1.2121212 FALSE TRUE
## X15 7.250000 1.2121212 FALSE FALSE
## X16 2.666667 1.2121212 FALSE FALSE
## X17 54.000000 1.2121212 FALSE TRUE
## X18 0.000000 0.6060606 TRUE TRUE
## X19 0.000000 0.6060606 TRUE TRUE
## X20 3.714286 1.2121212 FALSE FALSE
## X21 3.714286 1.2121212 FALSE FALSE
## X22 0.000000 0.6060606 TRUE TRUE
## X23 0.000000 0.6060606 TRUE TRUE
## X24 0.000000 0.6060606 TRUE TRUE
## X25 2.666667 1.2121212 FALSE FALSE
## X26 3.714286 1.2121212 FALSE FALSE
## X27 3.714286 1.2121212 FALSE FALSE
## X28 3.714286 1.2121212 FALSE FALSE
## X29 3.714286 1.2121212 FALSE FALSE
## X30 0.000000 0.6060606 TRUE TRUE
## X31 0.000000 0.6060606 TRUE TRUE
## X32 0.000000 0.6060606 TRUE TRUE
## X33 26.500000 1.2121212 FALSE TRUE
## X34 26.500000 1.2121212 FALSE TRUE
## X35 3.459459 1.2121212 FALSE FALSE
## X36 4.689655 1.2121212 FALSE FALSE
## X37 2.666667 1.2121212 FALSE FALSE
## X38 3.714286 1.2121212 FALSE FALSE
## X39 3.714286 1.2121212 FALSE FALSE
## X40 3.714286 1.2121212 FALSE FALSE
## X41 2.173077 1.2121212 FALSE FALSE
## X42 2.235294 1.2121212 FALSE FALSE
## X43 2.235294 1.2121212 FALSE FALSE
## X44 2.235294 1.2121212 FALSE FALSE
## X45 54.000000 1.2121212 FALSE TRUE
## X46 3.714286 1.2121212 FALSE FALSE
## X47 3.714286 1.2121212 FALSE FALSE
## X48 4.322581 1.2121212 FALSE FALSE
## X49 2.666667 1.2121212 FALSE FALSE
## X50 3.714286 1.2121212 FALSE FALSE
## X51 2.173077 1.2121212 FALSE FALSE
## X52 2.235294 1.2121212 FALSE FALSE
## X53 2.235294 1.2121212 FALSE FALSE
## X54 3.714286 1.2121212 FALSE FALSE
## X55 3.714286 1.2121212 FALSE FALSE
## X56 3.714286 1.2121212 FALSE FALSE
## X57 4.322581 1.2121212 FALSE FALSE
## X58 4.322581 1.2121212 FALSE FALSE
## X59 3.714286 1.2121212 FALSE FALSE
## X60 4.322581 1.2121212 FALSE FALSE
## X61 4.322581 1.2121212 FALSE FALSE
## X62 3.714286 1.2121212 FALSE FALSE
## X63 3.714286 1.2121212 FALSE FALSE
## X64 3.714286 1.2121212 FALSE FALSE
## X65 3.714286 1.2121212 FALSE FALSE
## X66 4.322581 1.2121212 FALSE FALSE
## X67 4.322581 1.2121212 FALSE FALSE
## X68 4.322581 1.2121212 FALSE FALSE
## X69 4.322581 1.2121212 FALSE FALSE
## X70 3.714286 1.2121212 FALSE FALSE
## X71 2.666667 1.2121212 FALSE FALSE
## X72 2.666667 1.2121212 FALSE FALSE
## X73 3.342105 1.2121212 FALSE FALSE
## X74 3.342105 1.2121212 FALSE FALSE
## X75 3.342105 1.2121212 FALSE FALSE
## X76 3.342105 1.2121212 FALSE FALSE
## X77 81.500000 1.2121212 FALSE TRUE
## X78 2.173077 1.2121212 FALSE FALSE
## X79 2.235294 1.2121212 FALSE FALSE
## X80 2.173077 1.2121212 FALSE FALSE
## X81 0.000000 0.6060606 TRUE TRUE
## X82 0.000000 0.6060606 TRUE TRUE
## X83 26.500000 1.2121212 FALSE TRUE
## X84 26.500000 1.2121212 FALSE TRUE
## X85 26.500000 1.2121212 FALSE TRUE
## X86 2.367347 1.2121212 FALSE FALSE
## X87 1.357143 1.2121212 FALSE FALSE
## X88 4.892857 1.2121212 FALSE FALSE
## X89 0.000000 0.6060606 TRUE TRUE
## X90 0.000000 0.6060606 TRUE TRUE
## X91 26.500000 1.2121212 FALSE TRUE
## X92 0.000000 0.6060606 TRUE TRUE
## X93 3.125000 1.2121212 FALSE FALSE
## X94 14.000000 1.2121212 FALSE FALSE
## X95 54.000000 1.2121212 FALSE TRUE
## X96 3.583333 1.2121212 FALSE FALSE
## X97 1.012195 1.2121212 FALSE FALSE
## X98 3.024390 1.2121212 FALSE FALSE
## X99 10.000000 1.2121212 FALSE FALSE
## X100 40.250000 1.2121212 FALSE TRUE
## X101 2.367347 1.2121212 FALSE FALSE
## X102 1.426471 1.2121212 FALSE FALSE
## X103 5.600000 1.2121212 FALSE FALSE
## X104 81.500000 1.2121212 FALSE TRUE
## X105 19.625000 1.2121212 FALSE TRUE
## X106 81.500000 1.2121212 FALSE TRUE
## X107 40.250000 1.2121212 FALSE TRUE
## X108 5.600000 1.2121212 FALSE FALSE
## X109 81.500000 1.2121212 FALSE TRUE
## X110 164.000000 1.2121212 FALSE TRUE
## X111 1.426471 1.2121212 FALSE FALSE
## X112 40.250000 1.2121212 FALSE TRUE
## X113 81.500000 1.2121212 FALSE TRUE
## X114 164.000000 1.2121212 FALSE TRUE
## X115 0.000000 0.6060606 TRUE TRUE
## X116 40.250000 1.2121212 FALSE TRUE
## X117 22.571429 1.2121212 FALSE TRUE
## X118 12.750000 1.2121212 FALSE FALSE
## X119 54.000000 1.2121212 FALSE TRUE
## X120 19.625000 1.2121212 FALSE TRUE
## X121 2.113208 1.2121212 FALSE FALSE
## X122 54.000000 1.2121212 FALSE TRUE
## X123 81.500000 1.2121212 FALSE TRUE
## X124 81.500000 1.2121212 FALSE TRUE
## X125 5.600000 1.2121212 FALSE FALSE
## X126 10.785714 1.2121212 FALSE FALSE
## X127 10.785714 1.2121212 FALSE FALSE
## X128 81.500000 1.2121212 FALSE TRUE
## X129 5.111111 1.2121212 FALSE FALSE
## X130 5.600000 1.2121212 FALSE FALSE
## X131 54.000000 1.2121212 FALSE TRUE
## X132 54.000000 1.2121212 FALSE TRUE
## X133 2.666667 1.2121212 FALSE FALSE
## X134 32.000000 1.2121212 FALSE TRUE
## X135 32.000000 1.2121212 FALSE TRUE
## X136 54.000000 1.2121212 FALSE TRUE
## X137 54.000000 1.2121212 FALSE TRUE
## X138 2.666667 1.2121212 FALSE FALSE
## X139 54.000000 1.2121212 FALSE TRUE
## X140 54.000000 1.2121212 FALSE TRUE
## X141 1.894737 1.2121212 FALSE FALSE
## X142 3.714286 1.2121212 FALSE FALSE
## X143 7.684211 1.2121212 FALSE FALSE
## X144 26.500000 1.2121212 FALSE TRUE
## X145 0.000000 0.6060606 TRUE TRUE
## X146 17.333333 1.2121212 FALSE FALSE
## X147 0.000000 0.6060606 TRUE TRUE
## X148 164.000000 1.2121212 FALSE TRUE
## X149 32.000000 1.2121212 FALSE TRUE
## X150 1.796610 1.2121212 FALSE FALSE
## X151 81.500000 1.2121212 FALSE TRUE
## X152 1.946429 1.2121212 FALSE FALSE
## X153 1.894737 1.2121212 FALSE FALSE
## X154 1.894737 1.2121212 FALSE FALSE
## X155 81.500000 1.2121212 FALSE TRUE
## X156 3.852941 1.2121212 FALSE FALSE
## X157 4.156250 1.2121212 FALSE FALSE
## X158 1.426471 1.2121212 FALSE FALSE
## X159 1.115385 1.2121212 FALSE FALSE
## X160 32.000000 1.2121212 FALSE TRUE
## X161 81.500000 1.2121212 FALSE TRUE
## X162 1.946429 1.2121212 FALSE FALSE
## X163 1.894737 1.2121212 FALSE FALSE
## X164 81.500000 1.2121212 FALSE TRUE
## X165 81.500000 1.2121212 FALSE TRUE
## X166 81.500000 1.2121212 FALSE TRUE
## X167 1.894737 1.2121212 FALSE FALSE
## X168 1.894737 1.2121212 FALSE FALSE
## X169 1.894737 1.2121212 FALSE FALSE
## X170 1.894737 1.2121212 FALSE FALSE
## X171 17.333333 1.2121212 FALSE FALSE
## X172 1.894737 1.2121212 FALSE FALSE
## X173 1.894737 1.2121212 FALSE FALSE
## X174 1.894737 1.2121212 FALSE FALSE
## X175 1.844828 1.2121212 FALSE FALSE
## X176 1.844828 1.2121212 FALSE FALSE
## X177 1.844828 1.2121212 FALSE FALSE
## X178 1.844828 1.2121212 FALSE FALSE
## X179 1.946429 1.2121212 FALSE FALSE
## X180 1.894737 1.2121212 FALSE FALSE
## X181 1.796610 1.2121212 FALSE FALSE
## X182 2.113208 1.2121212 FALSE FALSE
## X183 1.894737 1.2121212 FALSE FALSE
## X184 1.844828 1.2121212 FALSE FALSE
## X185 1.844828 1.2121212 FALSE FALSE
## X186 1.844828 1.2121212 FALSE FALSE
## X187 1.946429 1.2121212 FALSE FALSE
## X188 1.894737 1.2121212 FALSE FALSE
## X189 1.894737 1.2121212 FALSE FALSE
## X190 1.796610 1.2121212 FALSE FALSE
## X191 1.796610 1.2121212 FALSE FALSE
## X192 1.894737 1.2121212 FALSE FALSE
## X193 1.844828 1.2121212 FALSE FALSE
## X194 1.844828 1.2121212 FALSE FALSE
## X195 1.946429 1.2121212 FALSE FALSE
## X196 1.946429 1.2121212 FALSE FALSE
## X197 1.946429 1.2121212 FALSE FALSE
## X198 1.894737 1.2121212 FALSE FALSE
## X199 1.894737 1.2121212 FALSE FALSE
## X200 1.796610 1.2121212 FALSE FALSE
## X201 1.796610 1.2121212 FALSE FALSE
## X202 1.796610 1.2121212 FALSE FALSE
## X203 1.796610 1.2121212 FALSE FALSE
## X204 1.894737 1.2121212 FALSE FALSE
## X205 1.894737 1.2121212 FALSE FALSE
## X206 2.113208 1.2121212 FALSE FALSE
## X207 2.113208 1.2121212 FALSE FALSE
## X208 1.796610 1.2121212 FALSE FALSE
## X209 1.796610 1.2121212 FALSE FALSE
## X210 1.796610 1.2121212 FALSE FALSE
## X211 1.796610 1.2121212 FALSE FALSE
## X212 1.844828 1.2121212 FALSE FALSE
## X213 1.844828 1.2121212 FALSE FALSE
## X214 1.844828 1.2121212 FALSE FALSE
## X215 17.333333 1.2121212 FALSE FALSE
## X216 54.000000 1.2121212 FALSE TRUE
## X217 19.625000 1.2121212 FALSE TRUE
## X218 26.500000 1.2121212 FALSE TRUE
## X219 54.000000 1.2121212 FALSE TRUE
## X220 19.625000 1.2121212 FALSE TRUE
## X221 2.586957 1.2121212 FALSE FALSE
## X222 26.500000 1.2121212 FALSE TRUE
## X223 2.586957 1.2121212 FALSE FALSE
## X224 2.586957 1.2121212 FALSE FALSE
## X225 17.333333 1.2121212 FALSE FALSE
## X226 3.583333 1.2121212 FALSE FALSE
## X227 3.583333 1.2121212 FALSE FALSE
## X228 3.583333 1.2121212 FALSE FALSE
## X229 1.538462 1.2121212 FALSE FALSE
## X230 2.928571 1.2121212 FALSE FALSE
## X231 4.689655 1.2121212 FALSE FALSE
## X232 4.689655 1.2121212 FALSE FALSE
## X233 4.689655 1.2121212 FALSE FALSE
## X234 4.689655 1.2121212 FALSE FALSE
## X235 1.260274 1.2121212 FALSE FALSE
## X236 1.578125 1.2121212 FALSE FALSE
## X237 1.142857 1.2121212 FALSE FALSE
## X238 2.367347 1.2121212 FALSE FALSE
## X239 4.156250 1.2121212 FALSE FALSE
## X240 4.156250 1.2121212 FALSE FALSE
## X241 3.024390 1.2121212 FALSE FALSE
## X242 1.229730 1.2121212 FALSE FALSE
## X243 81.500000 1.2121212 FALSE TRUE
## X244 4.156250 1.2121212 FALSE FALSE
## X245 4.156250 1.2121212 FALSE FALSE
## X246 4.156250 1.2121212 FALSE FALSE
## X247 2.750000 1.2121212 FALSE FALSE
## X248 1.426471 1.2121212 FALSE FALSE
## X249 3.024390 1.2121212 FALSE FALSE
## X250 1.229730 1.2121212 FALSE FALSE
## X251 4.322581 1.2121212 FALSE FALSE
## X252 81.500000 1.2121212 FALSE TRUE
## X253 4.156250 1.2121212 FALSE FALSE
## X254 4.156250 1.2121212 FALSE FALSE
## X255 2.750000 1.2121212 FALSE FALSE
## X256 1.426471 1.2121212 FALSE FALSE
## X257 1.229730 1.2121212 FALSE FALSE
## X258 8.705882 1.2121212 FALSE FALSE
## X259 81.500000 1.2121212 FALSE TRUE
## X260 2.837209 1.2121212 FALSE FALSE
## X261 3.024390 1.2121212 FALSE FALSE
## X262 3.230769 1.2121212 FALSE FALSE
## X263 2.055556 1.2121212 FALSE FALSE
## X264 1.661290 1.2121212 FALSE FALSE
## X265 2.837209 1.2121212 FALSE FALSE
## X266 3.230769 1.2121212 FALSE FALSE
## X267 1.062500 1.2121212 FALSE FALSE
## X268 2.235294 1.2121212 FALSE FALSE
## X269 1.115385 1.2121212 FALSE FALSE
## X270 1.088608 1.2121212 FALSE FALSE
## X271 1.894737 1.2121212 FALSE FALSE
## X272 1.462687 1.2121212 FALSE FALSE
## X273 164.000000 1.2121212 FALSE TRUE
## X274 1.894737 1.2121212 FALSE FALSE
## X275 19.625000 1.2121212 FALSE TRUE
## X276 4.689655 1.2121212 FALSE FALSE
## X277 81.500000 1.2121212 FALSE TRUE
## X278 4.322581 1.2121212 FALSE FALSE
## X279 4.322581 1.2121212 FALSE FALSE
## X280 4.500000 1.2121212 FALSE FALSE
## X281 4.500000 1.2121212 FALSE FALSE
## X282 26.500000 1.2121212 FALSE TRUE
## X283 81.500000 1.2121212 FALSE TRUE
## X284 4.322581 1.2121212 FALSE FALSE
## X285 4.322581 1.2121212 FALSE FALSE
## X286 4.322581 1.2121212 FALSE FALSE
## X287 81.500000 1.2121212 FALSE TRUE
## X288 81.500000 1.2121212 FALSE TRUE
## X289 26.500000 1.2121212 FALSE TRUE
## X290 4.322581 1.2121212 FALSE FALSE
## X291 4.322581 1.2121212 FALSE FALSE
## X292 26.500000 1.2121212 FALSE TRUE
## X293 10.785714 1.2121212 FALSE FALSE
## X294 10.000000 1.2121212 FALSE FALSE
## X295 8.705882 1.2121212 FALSE FALSE
## X296 2.173077 1.2121212 FALSE FALSE
## X297 6.500000 1.2121212 FALSE FALSE
## X298 4.500000 1.2121212 FALSE FALSE
## X299 4.500000 1.2121212 FALSE FALSE
## X300 4.500000 1.2121212 FALSE FALSE
## X301 2.173077 1.2121212 FALSE FALSE
## X302 2.173077 1.2121212 FALSE FALSE
## X303 6.500000 1.2121212 FALSE FALSE
## X304 4.500000 1.2121212 FALSE FALSE
## X305 4.500000 1.2121212 FALSE FALSE
## X306 11.692308 1.2121212 FALSE FALSE
## X307 10.000000 1.2121212 FALSE FALSE
## X308 10.000000 1.2121212 FALSE FALSE
## X309 6.857143 1.2121212 FALSE FALSE
## X310 4.500000 1.2121212 FALSE FALSE
## X311 3.230769 1.2121212 FALSE FALSE
## X312 2.586957 1.2121212 FALSE FALSE
## X313 3.024390 1.2121212 FALSE FALSE
## X314 2.837209 1.2121212 FALSE FALSE
## X315 1.462687 1.2121212 FALSE FALSE
## X316 10.000000 1.2121212 FALSE FALSE
## X317 10.000000 1.2121212 FALSE FALSE
## X318 10.000000 1.2121212 FALSE FALSE
## X319 4.156250 1.2121212 FALSE FALSE
## X320 4.322581 1.2121212 FALSE FALSE
## X321 4.322581 1.2121212 FALSE FALSE
## X322 3.125000 1.2121212 FALSE FALSE
## X323 3.714286 1.2121212 FALSE FALSE
## X324 3.714286 1.2121212 FALSE FALSE
## X325 2.586957 1.2121212 FALSE FALSE
## X326 3.024390 1.2121212 FALSE FALSE
## X327 3.024390 1.2121212 FALSE FALSE
## X328 3.024390 1.2121212 FALSE FALSE
## X329 6.173913 1.2121212 FALSE FALSE
## X330 3.024390 1.2121212 FALSE FALSE
## X331 3.024390 1.2121212 FALSE FALSE
## X332 3.024390 1.2121212 FALSE FALSE
## X333 3.024390 1.2121212 FALSE FALSE
## X334 10.000000 1.2121212 FALSE FALSE
## X335 6.173913 1.2121212 FALSE FALSE
## X336 8.166667 1.2121212 FALSE FALSE
## X337 11.692308 1.2121212 FALSE FALSE
## X338 2.113208 1.2121212 FALSE FALSE
## X339 2.113208 1.2121212 FALSE FALSE
## X340 7.684211 1.2121212 FALSE FALSE
## X341 2.113208 1.2121212 FALSE FALSE
## X342 2.235294 1.2121212 FALSE FALSE
## X343 2.235294 1.2121212 FALSE FALSE
## X344 7.684211 1.2121212 FALSE FALSE
## X345 17.333333 1.2121212 FALSE FALSE
## X346 54.000000 1.2121212 FALSE TRUE
## X347 26.500000 1.2121212 FALSE TRUE
## X348 26.500000 1.2121212 FALSE TRUE
## X349 40.250000 1.2121212 FALSE TRUE
## X350 54.000000 1.2121212 FALSE TRUE
## X351 54.000000 1.2121212 FALSE TRUE
## X352 54.000000 1.2121212 FALSE TRUE
## X353 54.000000 1.2121212 FALSE TRUE
## X354 54.000000 1.2121212 FALSE TRUE
## X355 3.125000 1.2121212 FALSE FALSE
## X356 2.837209 1.2121212 FALSE FALSE
## X357 3.583333 1.2121212 FALSE FALSE
## X358 3.230769 1.2121212 FALSE FALSE
## X359 4.689655 1.2121212 FALSE FALSE
## X360 4.689655 1.2121212 FALSE FALSE
## X361 7.250000 1.2121212 FALSE FALSE
## X362 5.111111 1.2121212 FALSE FALSE
## X363 40.250000 1.2121212 FALSE TRUE
## X364 40.250000 1.2121212 FALSE TRUE
## X365 54.000000 1.2121212 FALSE TRUE
## X366 4.500000 1.2121212 FALSE FALSE
## X367 3.583333 1.2121212 FALSE FALSE
## X368 2.837209 1.2121212 FALSE FALSE
## X369 26.500000 1.2121212 FALSE TRUE
## X370 3.342105 1.2121212 FALSE FALSE
## X371 3.230769 1.2121212 FALSE FALSE
## X372 11.692308 1.2121212 FALSE FALSE
## X373 11.692308 1.2121212 FALSE FALSE
## X374 6.857143 1.2121212 FALSE FALSE
## X375 26.500000 1.2121212 FALSE TRUE
## X376 10.785714 1.2121212 FALSE FALSE
## X377 9.312500 1.2121212 FALSE FALSE
## X378 9.312500 1.2121212 FALSE FALSE
## X379 40.250000 1.2121212 FALSE TRUE
## X380 9.312500 1.2121212 FALSE FALSE
## X381 9.312500 1.2121212 FALSE FALSE
## X382 9.312500 1.2121212 FALSE FALSE
## X383 9.312500 1.2121212 FALSE FALSE
## X384 40.250000 1.2121212 FALSE TRUE
## X385 9.312500 1.2121212 FALSE FALSE
## X386 9.312500 1.2121212 FALSE FALSE
## X387 9.312500 1.2121212 FALSE FALSE
## X388 9.312500 1.2121212 FALSE FALSE
## X389 9.312500 1.2121212 FALSE FALSE
## X390 9.312500 1.2121212 FALSE FALSE
## X391 26.500000 1.2121212 FALSE TRUE
## X392 9.312500 1.2121212 FALSE FALSE
## X393 32.000000 1.2121212 FALSE TRUE
## X394 9.312500 1.2121212 FALSE FALSE
## X395 9.312500 1.2121212 FALSE FALSE
## X396 9.312500 1.2121212 FALSE FALSE
## X397 40.250000 1.2121212 FALSE TRUE
## X398 9.312500 1.2121212 FALSE FALSE
## X399 32.000000 1.2121212 FALSE TRUE
## X400 9.312500 1.2121212 FALSE FALSE
## X401 9.312500 1.2121212 FALSE FALSE
## X402 32.000000 1.2121212 FALSE TRUE
## X403 9.312500 1.2121212 FALSE FALSE
## X404 26.500000 1.2121212 FALSE TRUE
## X405 54.000000 1.2121212 FALSE TRUE
## X406 9.312500 1.2121212 FALSE FALSE
## X407 26.500000 1.2121212 FALSE TRUE
## X408 22.571429 1.2121212 FALSE TRUE
## X409 22.571429 1.2121212 FALSE TRUE
## X410 32.000000 1.2121212 FALSE TRUE
## X411 22.571429 1.2121212 FALSE TRUE
## X412 32.000000 1.2121212 FALSE TRUE
## X413 32.000000 1.2121212 FALSE TRUE
## X414 32.000000 1.2121212 FALSE TRUE
## X415 164.000000 1.2121212 FALSE TRUE
## X416 164.000000 1.2121212 FALSE TRUE
## X417 164.000000 1.2121212 FALSE TRUE
## X418 164.000000 1.2121212 FALSE TRUE
## X419 164.000000 1.2121212 FALSE TRUE
## X420 164.000000 1.2121212 FALSE TRUE
## X421 164.000000 1.2121212 FALSE TRUE
## X422 164.000000 1.2121212 FALSE TRUE
## X423 164.000000 1.2121212 FALSE TRUE
## X424 164.000000 1.2121212 FALSE TRUE
## X425 164.000000 1.2121212 FALSE TRUE
## X426 164.000000 1.2121212 FALSE TRUE
## X427 164.000000 1.2121212 FALSE TRUE
## X428 164.000000 1.2121212 FALSE TRUE
## X429 164.000000 1.2121212 FALSE TRUE
## X430 164.000000 1.2121212 FALSE TRUE
## X431 164.000000 1.2121212 FALSE TRUE
## X432 164.000000 1.2121212 FALSE TRUE
## X433 164.000000 1.2121212 FALSE TRUE
## X434 164.000000 1.2121212 FALSE TRUE
## X435 164.000000 1.2121212 FALSE TRUE
## X436 164.000000 1.2121212 FALSE TRUE
## X437 164.000000 1.2121212 FALSE TRUE
## X438 164.000000 1.2121212 FALSE TRUE
## X439 26.500000 1.2121212 FALSE TRUE
## X440 26.500000 1.2121212 FALSE TRUE
## X441 40.250000 1.2121212 FALSE TRUE
## X442 164.000000 1.2121212 FALSE TRUE
## X443 164.000000 1.2121212 FALSE TRUE
## X444 164.000000 1.2121212 FALSE TRUE
## X445 164.000000 1.2121212 FALSE TRUE
## X446 164.000000 1.2121212 FALSE TRUE
## X447 40.250000 1.2121212 FALSE TRUE
## X448 164.000000 1.2121212 FALSE TRUE
## X449 164.000000 1.2121212 FALSE TRUE
## X450 164.000000 1.2121212 FALSE TRUE
## X451 164.000000 1.2121212 FALSE TRUE
## X452 164.000000 1.2121212 FALSE TRUE
## X453 26.500000 1.2121212 FALSE TRUE
## X454 26.500000 1.2121212 FALSE TRUE
## X455 26.500000 1.2121212 FALSE TRUE
## X456 164.000000 1.2121212 FALSE TRUE
## X457 54.000000 1.2121212 FALSE TRUE
## X458 164.000000 1.2121212 FALSE TRUE
## X459 164.000000 1.2121212 FALSE TRUE
## X460 164.000000 1.2121212 FALSE TRUE
## X461 164.000000 1.2121212 FALSE TRUE
## X462 164.000000 1.2121212 FALSE TRUE
## X463 164.000000 1.2121212 FALSE TRUE
## X464 164.000000 1.2121212 FALSE TRUE
## X465 164.000000 1.2121212 FALSE TRUE
## X466 164.000000 1.2121212 FALSE TRUE
## X467 164.000000 1.2121212 FALSE TRUE
## X468 164.000000 1.2121212 FALSE TRUE
## X469 164.000000 1.2121212 FALSE TRUE
## X470 164.000000 1.2121212 FALSE TRUE
## X471 164.000000 1.2121212 FALSE TRUE
## X472 164.000000 1.2121212 FALSE TRUE
## X473 164.000000 1.2121212 FALSE TRUE
## X474 164.000000 1.2121212 FALSE TRUE
## X475 81.500000 1.2121212 FALSE TRUE
## X476 81.500000 1.2121212 FALSE TRUE
## X477 81.500000 1.2121212 FALSE TRUE
## X478 81.500000 1.2121212 FALSE TRUE
## X479 81.500000 1.2121212 FALSE TRUE
## X480 81.500000 1.2121212 FALSE TRUE
## X481 81.500000 1.2121212 FALSE TRUE
## X482 81.500000 1.2121212 FALSE TRUE
## X483 81.500000 1.2121212 FALSE TRUE
## X484 81.500000 1.2121212 FALSE TRUE
## X485 81.500000 1.2121212 FALSE TRUE
## X486 81.500000 1.2121212 FALSE TRUE
## X487 81.500000 1.2121212 FALSE TRUE
## X488 81.500000 1.2121212 FALSE TRUE
## X489 81.500000 1.2121212 FALSE TRUE
## X490 81.500000 1.2121212 FALSE TRUE
## X491 81.500000 1.2121212 FALSE TRUE
## X492 81.500000 1.2121212 FALSE TRUE
## X493 81.500000 1.2121212 FALSE TRUE
## X494 81.500000 1.2121212 FALSE TRUE
## X495 81.500000 1.2121212 FALSE TRUE
## X496 6.173913 1.2121212 FALSE FALSE
## X497 5.875000 1.2121212 FALSE FALSE
## X498 26.500000 1.2121212 FALSE TRUE
## X499 5.600000 1.2121212 FALSE FALSE
## X500 19.625000 1.2121212 FALSE TRUE
## X501 19.625000 1.2121212 FALSE TRUE
## X502 81.500000 1.2121212 FALSE TRUE
## X503 7.250000 1.2121212 FALSE FALSE
## X504 3.583333 1.2121212 FALSE FALSE
## X505 3.583333 1.2121212 FALSE FALSE
## X506 3.583333 1.2121212 FALSE FALSE
## X507 4.892857 1.2121212 FALSE FALSE
## X508 5.600000 1.2121212 FALSE FALSE
## X509 4.156250 1.2121212 FALSE FALSE
## X510 9.312500 1.2121212 FALSE FALSE
## X511 12.750000 1.2121212 FALSE FALSE
## X512 10.000000 1.2121212 FALSE FALSE
## X513 81.500000 1.2121212 FALSE TRUE
## X514 12.750000 1.2121212 FALSE FALSE
## X515 12.750000 1.2121212 FALSE FALSE
## X516 10.785714 1.2121212 FALSE FALSE
## X517 10.785714 1.2121212 FALSE FALSE
## X518 12.750000 1.2121212 FALSE FALSE
## X519 10.000000 1.2121212 FALSE FALSE
## X520 8.705882 1.2121212 FALSE FALSE
## X521 8.705882 1.2121212 FALSE FALSE
## X522 12.750000 1.2121212 FALSE FALSE
## X523 81.500000 1.2121212 FALSE TRUE
## X524 10.785714 1.2121212 FALSE FALSE
## X525 81.500000 1.2121212 FALSE TRUE
## X526 81.500000 1.2121212 FALSE TRUE
## X527 81.500000 1.2121212 FALSE TRUE
## X528 0.000000 0.6060606 TRUE TRUE
## X529 12.750000 1.2121212 FALSE FALSE
## X530 40.250000 1.2121212 FALSE TRUE
## X531 32.000000 1.2121212 FALSE TRUE
## X532 32.000000 1.2121212 FALSE TRUE
## X533 32.000000 1.2121212 FALSE TRUE
## X534 32.000000 1.2121212 FALSE TRUE
## X535 32.000000 1.2121212 FALSE TRUE
## X536 40.250000 1.2121212 FALSE TRUE
## X537 40.250000 1.2121212 FALSE TRUE
## X538 40.250000 1.2121212 FALSE TRUE
## X539 40.250000 1.2121212 FALSE TRUE
## X540 40.250000 1.2121212 FALSE TRUE
## X541 40.250000 1.2121212 FALSE TRUE
## X542 40.250000 1.2121212 FALSE TRUE
## X543 40.250000 1.2121212 FALSE TRUE
## X544 32.000000 1.2121212 FALSE TRUE
## X545 32.000000 1.2121212 FALSE TRUE
## X546 40.250000 1.2121212 FALSE TRUE
## X547 40.250000 1.2121212 FALSE TRUE
## X548 32.000000 1.2121212 FALSE TRUE
## X549 3.459459 1.2121212 FALSE FALSE
## X550 40.250000 1.2121212 FALSE TRUE
## X551 15.500000 1.2121212 FALSE FALSE
## X552 40.250000 1.2121212 FALSE TRUE
## X553 15.500000 1.2121212 FALSE FALSE
## X554 15.500000 1.2121212 FALSE FALSE
## X555 40.250000 1.2121212 FALSE TRUE
## X556 3.459459 1.2121212 FALSE FALSE
## X557 3.459459 1.2121212 FALSE FALSE
## X558 3.459459 1.2121212 FALSE FALSE
## X559 3.459459 1.2121212 FALSE FALSE
## X560 3.459459 1.2121212 FALSE FALSE
## X561 17.333333 1.2121212 FALSE FALSE
## X562 19.625000 1.2121212 FALSE TRUE
## X563 19.625000 1.2121212 FALSE TRUE
## X564 19.625000 1.2121212 FALSE TRUE
## X565 3.459459 1.2121212 FALSE FALSE
## X566 19.625000 1.2121212 FALSE TRUE
## X567 164.000000 1.2121212 FALSE TRUE
## X568 17.333333 1.2121212 FALSE FALSE
## X569 0.000000 0.6060606 TRUE TRUE
## X570 0.000000 0.6060606 TRUE TRUE
## X571 5.346154 1.2121212 FALSE FALSE
## X572 22.571429 1.2121212 FALSE TRUE
## X573 4.322581 1.2121212 FALSE FALSE
## X574 5.346154 1.2121212 FALSE FALSE
## X575 40.250000 1.2121212 FALSE TRUE
## X576 5.346154 1.2121212 FALSE FALSE
## X577 10.000000 1.2121212 FALSE FALSE
## X578 40.250000 1.2121212 FALSE TRUE
## X579 0.000000 0.6060606 TRUE TRUE
## X580 0.000000 0.6060606 TRUE TRUE
## X581 0.000000 0.6060606 TRUE TRUE
## X582 22.571429 1.2121212 FALSE TRUE
## X583 22.571429 1.2121212 FALSE TRUE
## X584 32.000000 1.2121212 FALSE TRUE
## X585 32.000000 1.2121212 FALSE TRUE
## X586 22.571429 1.2121212 FALSE TRUE
## X587 19.625000 1.2121212 FALSE TRUE
## X588 32.000000 1.2121212 FALSE TRUE
## X589 32.000000 1.2121212 FALSE TRUE
## X590 14.000000 1.2121212 FALSE FALSE
## X591 14.000000 1.2121212 FALSE FALSE
## X592 10.785714 1.2121212 FALSE FALSE
## X593 10.785714 1.2121212 FALSE FALSE
## X594 11.692308 1.2121212 FALSE FALSE
## X595 14.000000 1.2121212 FALSE FALSE
## X596 0.000000 0.6060606 TRUE TRUE
## X597 6.857143 1.2121212 FALSE FALSE
## X598 6.500000 1.2121212 FALSE FALSE
## X599 4.500000 1.2121212 FALSE FALSE
## X600 6.857143 1.2121212 FALSE FALSE
## X601 4.500000 1.2121212 FALSE FALSE
## X602 4.322581 1.2121212 FALSE FALSE
## X603 4.500000 1.2121212 FALSE FALSE
## X604 7.250000 1.2121212 FALSE FALSE
## X605 81.500000 1.2121212 FALSE TRUE
## X606 164.000000 1.2121212 FALSE TRUE
## X607 164.000000 1.2121212 FALSE TRUE
## X608 164.000000 1.2121212 FALSE TRUE
## X609 164.000000 1.2121212 FALSE TRUE
## X610 164.000000 1.2121212 FALSE TRUE
## X611 164.000000 1.2121212 FALSE TRUE
## X612 164.000000 1.2121212 FALSE TRUE
## X613 15.500000 1.2121212 FALSE FALSE
## X614 22.571429 1.2121212 FALSE TRUE
## X615 22.571429 1.2121212 FALSE TRUE
## X616 22.571429 1.2121212 FALSE TRUE
## X617 22.571429 1.2121212 FALSE TRUE
## X618 22.571429 1.2121212 FALSE TRUE
## X619 22.571429 1.2121212 FALSE TRUE
## X620 22.571429 1.2121212 FALSE TRUE
## X621 15.500000 1.2121212 FALSE FALSE
## X622 22.571429 1.2121212 FALSE TRUE
## X623 164.000000 1.2121212 FALSE TRUE
## X624 81.500000 1.2121212 FALSE TRUE
## X625 81.500000 1.2121212 FALSE TRUE
## X626 19.625000 1.2121212 FALSE TRUE
## X627 164.000000 1.2121212 FALSE TRUE
## X628 164.000000 1.2121212 FALSE TRUE
## X629 164.000000 1.2121212 FALSE TRUE
## X630 164.000000 1.2121212 FALSE TRUE
## X631 164.000000 1.2121212 FALSE TRUE
## X632 164.000000 1.2121212 FALSE TRUE
## X633 164.000000 1.2121212 FALSE TRUE
## X634 164.000000 1.2121212 FALSE TRUE
## X635 164.000000 1.2121212 FALSE TRUE
## X636 164.000000 1.2121212 FALSE TRUE
## X637 164.000000 1.2121212 FALSE TRUE
## X638 164.000000 1.2121212 FALSE TRUE
## X639 164.000000 1.2121212 FALSE TRUE
## X640 164.000000 1.2121212 FALSE TRUE
## X641 81.500000 1.2121212 FALSE TRUE
## X642 81.500000 1.2121212 FALSE TRUE
## X643 81.500000 1.2121212 FALSE TRUE
## X644 81.500000 1.2121212 FALSE TRUE
## X645 164.000000 1.2121212 FALSE TRUE
## X646 81.500000 1.2121212 FALSE TRUE
## X647 164.000000 1.2121212 FALSE TRUE
## X648 54.000000 1.2121212 FALSE TRUE
## X649 19.625000 1.2121212 FALSE TRUE
## X650 81.500000 1.2121212 FALSE TRUE
## X651 54.000000 1.2121212 FALSE TRUE
## X652 54.000000 1.2121212 FALSE TRUE
## X653 19.625000 1.2121212 FALSE TRUE
## X654 19.625000 1.2121212 FALSE TRUE
## X655 19.625000 1.2121212 FALSE TRUE
## X656 19.625000 1.2121212 FALSE TRUE
## X657 19.625000 1.2121212 FALSE TRUE
## X658 19.625000 1.2121212 FALSE TRUE
## X659 19.625000 1.2121212 FALSE TRUE
## X660 32.000000 1.2121212 FALSE TRUE
## X661 32.000000 1.2121212 FALSE TRUE
## X662 81.500000 1.2121212 FALSE TRUE
## X663 32.000000 1.2121212 FALSE TRUE
## X664 32.000000 1.2121212 FALSE TRUE
## X665 19.625000 1.2121212 FALSE TRUE
## X666 81.500000 1.2121212 FALSE TRUE
## X667 26.500000 1.2121212 FALSE TRUE
## X668 32.000000 1.2121212 FALSE TRUE
## X669 32.000000 1.2121212 FALSE TRUE
## X670 32.000000 1.2121212 FALSE TRUE
## X671 32.000000 1.2121212 FALSE TRUE
## X672 32.000000 1.2121212 FALSE TRUE
## X673 32.000000 1.2121212 FALSE TRUE
## X674 32.000000 1.2121212 FALSE TRUE
## X675 32.000000 1.2121212 FALSE TRUE
## X676 32.000000 1.2121212 FALSE TRUE
## X677 26.500000 1.2121212 FALSE TRUE
## X678 54.000000 1.2121212 FALSE TRUE
## X679 10.785714 1.2121212 FALSE FALSE
## X680 164.000000 1.2121212 FALSE TRUE
## X681 81.500000 1.2121212 FALSE TRUE
## X682 164.000000 1.2121212 FALSE TRUE
## X683 164.000000 1.2121212 FALSE TRUE
## X684 164.000000 1.2121212 FALSE TRUE
## X685 164.000000 1.2121212 FALSE TRUE
## X686 164.000000 1.2121212 FALSE TRUE
## X687 164.000000 1.2121212 FALSE TRUE
## X688 164.000000 1.2121212 FALSE TRUE
## X689 164.000000 1.2121212 FALSE TRUE
## X690 164.000000 1.2121212 FALSE TRUE
## X691 0.000000 0.6060606 TRUE TRUE
## X692 0.000000 0.6060606 TRUE TRUE
## X693 0.000000 0.6060606 TRUE TRUE
## X694 0.000000 0.6060606 TRUE TRUE
## X695 0.000000 0.6060606 TRUE TRUE
## X696 22.571429 1.2121212 FALSE TRUE
## X697 54.000000 1.2121212 FALSE TRUE
## X698 15.500000 1.2121212 FALSE FALSE
## X699 10.000000 1.2121212 FALSE FALSE
## X700 8.166667 1.2121212 FALSE FALSE
## X701 8.166667 1.2121212 FALSE FALSE
## X702 8.166667 1.2121212 FALSE FALSE
## X703 10.000000 1.2121212 FALSE FALSE
## X704 10.785714 1.2121212 FALSE FALSE
## X705 8.166667 1.2121212 FALSE FALSE
## X706 32.000000 1.2121212 FALSE TRUE
## X707 40.250000 1.2121212 FALSE TRUE
## X708 32.000000 1.2121212 FALSE TRUE
## X709 32.000000 1.2121212 FALSE TRUE
## X710 32.000000 1.2121212 FALSE TRUE
## X711 40.250000 1.2121212 FALSE TRUE
## X712 32.000000 1.2121212 FALSE TRUE
## X713 32.000000 1.2121212 FALSE TRUE
## X714 32.000000 1.2121212 FALSE TRUE
## X715 32.000000 1.2121212 FALSE TRUE
## X716 26.500000 1.2121212 FALSE TRUE
## X717 26.500000 1.2121212 FALSE TRUE
## X718 26.500000 1.2121212 FALSE TRUE
## X719 15.500000 1.2121212 FALSE FALSE
## X720 40.250000 1.2121212 FALSE TRUE
## X721 40.250000 1.2121212 FALSE TRUE
## X722 54.000000 1.2121212 FALSE TRUE
## X723 54.000000 1.2121212 FALSE TRUE
## X724 81.500000 1.2121212 FALSE TRUE
## X725 81.500000 1.2121212 FALSE TRUE
## X726 81.500000 1.2121212 FALSE TRUE
## X727 81.500000 1.2121212 FALSE TRUE
## X728 81.500000 1.2121212 FALSE TRUE
## X729 81.500000 1.2121212 FALSE TRUE
## X730 0.000000 0.6060606 TRUE TRUE
## X731 0.000000 0.6060606 TRUE TRUE
## X732 15.500000 1.2121212 FALSE FALSE
## X733 15.500000 1.2121212 FALSE FALSE
## X734 54.000000 1.2121212 FALSE TRUE
## X735 54.000000 1.2121212 FALSE TRUE
## X736 164.000000 1.2121212 FALSE TRUE
## X737 164.000000 1.2121212 FALSE TRUE
## X738 164.000000 1.2121212 FALSE TRUE
## X739 164.000000 1.2121212 FALSE TRUE
## X740 81.500000 1.2121212 FALSE TRUE
## X741 164.000000 1.2121212 FALSE TRUE
## X742 164.000000 1.2121212 FALSE TRUE
## X743 164.000000 1.2121212 FALSE TRUE
## X744 164.000000 1.2121212 FALSE TRUE
## X745 164.000000 1.2121212 FALSE TRUE
## X746 164.000000 1.2121212 FALSE TRUE
## X747 164.000000 1.2121212 FALSE TRUE
## X748 164.000000 1.2121212 FALSE TRUE
## X749 164.000000 1.2121212 FALSE TRUE
## X750 17.333333 1.2121212 FALSE FALSE
## X751 11.692308 1.2121212 FALSE FALSE
## X752 17.333333 1.2121212 FALSE FALSE
## X753 17.333333 1.2121212 FALSE FALSE
## X754 11.692308 1.2121212 FALSE FALSE
## X755 11.692308 1.2121212 FALSE FALSE
## X756 164.000000 1.2121212 FALSE TRUE
## X757 164.000000 1.2121212 FALSE TRUE
## X758 164.000000 1.2121212 FALSE TRUE
## X759 164.000000 1.2121212 FALSE TRUE
## X760 164.000000 1.2121212 FALSE TRUE
## X761 164.000000 1.2121212 FALSE TRUE
## X762 164.000000 1.2121212 FALSE TRUE
## X763 54.000000 1.2121212 FALSE TRUE
## X764 54.000000 1.2121212 FALSE TRUE
## X765 54.000000 1.2121212 FALSE TRUE
## X766 54.000000 1.2121212 FALSE TRUE
## X767 54.000000 1.2121212 FALSE TRUE
## X768 22.571429 1.2121212 FALSE TRUE
## X769 81.500000 1.2121212 FALSE TRUE
## X770 81.500000 1.2121212 FALSE TRUE
## X771 164.000000 1.2121212 FALSE TRUE
## X772 164.000000 1.2121212 FALSE TRUE
## X773 12.750000 1.2121212 FALSE FALSE
## X774 12.750000 1.2121212 FALSE FALSE
## X775 12.750000 1.2121212 FALSE FALSE
## X776 12.750000 1.2121212 FALSE FALSE
## X777 81.500000 1.2121212 FALSE TRUE
## X778 81.500000 1.2121212 FALSE TRUE
## X779 22.571429 1.2121212 FALSE TRUE
## X780 17.333333 1.2121212 FALSE FALSE
## X781 19.625000 1.2121212 FALSE TRUE
## X782 17.333333 1.2121212 FALSE FALSE
## X783 0.000000 0.6060606 TRUE TRUE
## X784 19.625000 1.2121212 FALSE TRUE
## X785 0.000000 0.6060606 TRUE TRUE
## X786 0.000000 0.6060606 TRUE TRUE
## X787 0.000000 0.6060606 TRUE TRUE
## X788 0.000000 0.6060606 TRUE TRUE
## X789 0.000000 0.6060606 TRUE TRUE
## X790 81.500000 1.2121212 FALSE TRUE
## X791 81.500000 1.2121212 FALSE TRUE
## X792 17.333333 1.2121212 FALSE FALSE
## X793 17.333333 1.2121212 FALSE FALSE
## X794 81.500000 1.2121212 FALSE TRUE
## X795 12.750000 1.2121212 FALSE FALSE
## X796 32.000000 1.2121212 FALSE TRUE
## X797 81.500000 1.2121212 FALSE TRUE
## X798 12.750000 1.2121212 FALSE FALSE
## X799 81.500000 1.2121212 FALSE TRUE
## X800 17.333333 1.2121212 FALSE FALSE
## X801 17.333333 1.2121212 FALSE FALSE
## X802 19.625000 1.2121212 FALSE TRUE
## X803 81.500000 1.2121212 FALSE TRUE
## X804 81.500000 1.2121212 FALSE TRUE
## X805 12.750000 1.2121212 FALSE FALSE
## X806 17.333333 1.2121212 FALSE FALSE
## X807 81.500000 1.2121212 FALSE TRUE
## X808 81.500000 1.2121212 FALSE TRUE
## X809 32.000000 1.2121212 FALSE TRUE
## X810 81.500000 1.2121212 FALSE TRUE
## X811 81.500000 1.2121212 FALSE TRUE
## X812 17.333333 1.2121212 FALSE FALSE
## X813 17.333333 1.2121212 FALSE FALSE
## X814 54.000000 1.2121212 FALSE TRUE
## X815 164.000000 1.2121212 FALSE TRUE
## X816 164.000000 1.2121212 FALSE TRUE
## X817 164.000000 1.2121212 FALSE TRUE
## X818 164.000000 1.2121212 FALSE TRUE
## X819 164.000000 1.2121212 FALSE TRUE
## X820 164.000000 1.2121212 FALSE TRUE
## X821 164.000000 1.2121212 FALSE TRUE
## X822 164.000000 1.2121212 FALSE TRUE
## X823 164.000000 1.2121212 FALSE TRUE
## X824 164.000000 1.2121212 FALSE TRUE
## X825 164.000000 1.2121212 FALSE TRUE
## X826 164.000000 1.2121212 FALSE TRUE
## X827 164.000000 1.2121212 FALSE TRUE
## X828 164.000000 1.2121212 FALSE TRUE
## X829 164.000000 1.2121212 FALSE TRUE
## X830 164.000000 1.2121212 FALSE TRUE
## X831 164.000000 1.2121212 FALSE TRUE
## X832 164.000000 1.2121212 FALSE TRUE
## X833 164.000000 1.2121212 FALSE TRUE
## X834 40.250000 1.2121212 FALSE TRUE
## X835 40.250000 1.2121212 FALSE TRUE
## X836 164.000000 1.2121212 FALSE TRUE
## X837 164.000000 1.2121212 FALSE TRUE
## X838 164.000000 1.2121212 FALSE TRUE
## X839 164.000000 1.2121212 FALSE TRUE
## X840 81.500000 1.2121212 FALSE TRUE
## X841 164.000000 1.2121212 FALSE TRUE
## X842 164.000000 1.2121212 FALSE TRUE
## X843 81.500000 1.2121212 FALSE TRUE
## X844 164.000000 1.2121212 FALSE TRUE
## X845 81.500000 1.2121212 FALSE TRUE
## X846 81.500000 1.2121212 FALSE TRUE
## X847 164.000000 1.2121212 FALSE TRUE
## X848 164.000000 1.2121212 FALSE TRUE
## X849 164.000000 1.2121212 FALSE TRUE
## X850 164.000000 1.2121212 FALSE TRUE
## X851 164.000000 1.2121212 FALSE TRUE
## X852 164.000000 1.2121212 FALSE TRUE
## X853 164.000000 1.2121212 FALSE TRUE
## X854 164.000000 1.2121212 FALSE TRUE
## X855 164.000000 1.2121212 FALSE TRUE
## X856 164.000000 1.2121212 FALSE TRUE
## X857 164.000000 1.2121212 FALSE TRUE
## X858 164.000000 1.2121212 FALSE TRUE
## X859 164.000000 1.2121212 FALSE TRUE
## X860 164.000000 1.2121212 FALSE TRUE
## X861 164.000000 1.2121212 FALSE TRUE
## X862 81.500000 1.2121212 FALSE TRUE
## X863 164.000000 1.2121212 FALSE TRUE
## X864 164.000000 1.2121212 FALSE TRUE
## X865 164.000000 1.2121212 FALSE TRUE
## X866 164.000000 1.2121212 FALSE TRUE
## X867 164.000000 1.2121212 FALSE TRUE
## X868 164.000000 1.2121212 FALSE TRUE
## X869 40.250000 1.2121212 FALSE TRUE
## X870 40.250000 1.2121212 FALSE TRUE
## X871 40.250000 1.2121212 FALSE TRUE
## X872 40.250000 1.2121212 FALSE TRUE
## X873 40.250000 1.2121212 FALSE TRUE
## X874 40.250000 1.2121212 FALSE TRUE
## X875 40.250000 1.2121212 FALSE TRUE
## X876 40.250000 1.2121212 FALSE TRUE
## X877 40.250000 1.2121212 FALSE TRUE
## X878 40.250000 1.2121212 FALSE TRUE
## X879 32.000000 1.2121212 FALSE TRUE
## X880 32.000000 1.2121212 FALSE TRUE
## X881 40.250000 1.2121212 FALSE TRUE
## X882 54.000000 1.2121212 FALSE TRUE
## X883 164.000000 1.2121212 FALSE TRUE
## X884 54.000000 1.2121212 FALSE TRUE
## X885 54.000000 1.2121212 FALSE TRUE
## X886 164.000000 1.2121212 FALSE TRUE
## X887 164.000000 1.2121212 FALSE TRUE
## X888 164.000000 1.2121212 FALSE TRUE
## X889 164.000000 1.2121212 FALSE TRUE
## X890 54.000000 1.2121212 FALSE TRUE
## X891 54.000000 1.2121212 FALSE TRUE
## X892 54.000000 1.2121212 FALSE TRUE
## X893 54.000000 1.2121212 FALSE TRUE
## X894 54.000000 1.2121212 FALSE TRUE
## X895 54.000000 1.2121212 FALSE TRUE
## X896 54.000000 1.2121212 FALSE TRUE
## X897 164.000000 1.2121212 FALSE TRUE
## X898 164.000000 1.2121212 FALSE TRUE
## X899 164.000000 1.2121212 FALSE TRUE
## X900 164.000000 1.2121212 FALSE TRUE
## X901 164.000000 1.2121212 FALSE TRUE
## X902 81.500000 1.2121212 FALSE TRUE
## X903 81.500000 1.2121212 FALSE TRUE
## X904 81.500000 1.2121212 FALSE TRUE
## X905 54.000000 1.2121212 FALSE TRUE
## X906 54.000000 1.2121212 FALSE TRUE
## X907 81.500000 1.2121212 FALSE TRUE
## X908 164.000000 1.2121212 FALSE TRUE
## X909 164.000000 1.2121212 FALSE TRUE
## X910 164.000000 1.2121212 FALSE TRUE
## X911 164.000000 1.2121212 FALSE TRUE
## X912 164.000000 1.2121212 FALSE TRUE
## X913 164.000000 1.2121212 FALSE TRUE
## X914 164.000000 1.2121212 FALSE TRUE
## X915 164.000000 1.2121212 FALSE TRUE
## X916 164.000000 1.2121212 FALSE TRUE
## X917 164.000000 1.2121212 FALSE TRUE
## X918 164.000000 1.2121212 FALSE TRUE
## X919 164.000000 1.2121212 FALSE TRUE
## X920 164.000000 1.2121212 FALSE TRUE
## X921 164.000000 1.2121212 FALSE TRUE
## X922 164.000000 1.2121212 FALSE TRUE
## X923 164.000000 1.2121212 FALSE TRUE
## X924 164.000000 1.2121212 FALSE TRUE
## X925 164.000000 1.2121212 FALSE TRUE
## X926 164.000000 1.2121212 FALSE TRUE
## X927 164.000000 1.2121212 FALSE TRUE
## X928 164.000000 1.2121212 FALSE TRUE
## X929 164.000000 1.2121212 FALSE TRUE
## X930 164.000000 1.2121212 FALSE TRUE
## X931 164.000000 1.2121212 FALSE TRUE
## X932 164.000000 1.2121212 FALSE TRUE
## X933 164.000000 1.2121212 FALSE TRUE
## X934 164.000000 1.2121212 FALSE TRUE
## X935 164.000000 1.2121212 FALSE TRUE
## X936 164.000000 1.2121212 FALSE TRUE
## X937 164.000000 1.2121212 FALSE TRUE
## X938 164.000000 1.2121212 FALSE TRUE
## X939 164.000000 1.2121212 FALSE TRUE
## X940 164.000000 1.2121212 FALSE TRUE
## X941 164.000000 1.2121212 FALSE TRUE
## X942 54.000000 1.2121212 FALSE TRUE
## X943 54.000000 1.2121212 FALSE TRUE
## X944 54.000000 1.2121212 FALSE TRUE
## X945 54.000000 1.2121212 FALSE TRUE
## X946 40.250000 1.2121212 FALSE TRUE
## X947 164.000000 1.2121212 FALSE TRUE
## X948 40.250000 1.2121212 FALSE TRUE
## X949 164.000000 1.2121212 FALSE TRUE
## X950 81.500000 1.2121212 FALSE TRUE
## X951 40.250000 1.2121212 FALSE TRUE
## X952 40.250000 1.2121212 FALSE TRUE
## X953 40.250000 1.2121212 FALSE TRUE
## X954 81.500000 1.2121212 FALSE TRUE
## X955 81.500000 1.2121212 FALSE TRUE
## X956 81.500000 1.2121212 FALSE TRUE
## X957 81.500000 1.2121212 FALSE TRUE
## X958 81.500000 1.2121212 FALSE TRUE
## X959 81.500000 1.2121212 FALSE TRUE
## X960 81.500000 1.2121212 FALSE TRUE
## X961 81.500000 1.2121212 FALSE TRUE
## X962 81.500000 1.2121212 FALSE TRUE
## X963 81.500000 1.2121212 FALSE TRUE
## X964 81.500000 1.2121212 FALSE TRUE
## X965 81.500000 1.2121212 FALSE TRUE
## X966 81.500000 1.2121212 FALSE TRUE
## X967 81.500000 1.2121212 FALSE TRUE
## X968 81.500000 1.2121212 FALSE TRUE
## X969 81.500000 1.2121212 FALSE TRUE
## X970 81.500000 1.2121212 FALSE TRUE
## X971 81.500000 1.2121212 FALSE TRUE
## X972 81.500000 1.2121212 FALSE TRUE
## X973 81.500000 1.2121212 FALSE TRUE
## X974 164.000000 1.2121212 FALSE TRUE
## X975 164.000000 1.2121212 FALSE TRUE
## X976 164.000000 1.2121212 FALSE TRUE
## X977 164.000000 1.2121212 FALSE TRUE
## X978 164.000000 1.2121212 FALSE TRUE
## X979 164.000000 1.2121212 FALSE TRUE
## X980 164.000000 1.2121212 FALSE TRUE
## X981 164.000000 1.2121212 FALSE TRUE
## X982 164.000000 1.2121212 FALSE TRUE
## X983 164.000000 1.2121212 FALSE TRUE
## X984 164.000000 1.2121212 FALSE TRUE
## X985 164.000000 1.2121212 FALSE TRUE
## X986 164.000000 1.2121212 FALSE TRUE
## X987 164.000000 1.2121212 FALSE TRUE
## X988 164.000000 1.2121212 FALSE TRUE
## X989 164.000000 1.2121212 FALSE TRUE
## X990 164.000000 1.2121212 FALSE TRUE
## X991 164.000000 1.2121212 FALSE TRUE
## X992 164.000000 1.2121212 FALSE TRUE
## X993 164.000000 1.2121212 FALSE TRUE
## X994 81.500000 1.2121212 FALSE TRUE
## X995 164.000000 1.2121212 FALSE TRUE
## X996 164.000000 1.2121212 FALSE TRUE
## X997 164.000000 1.2121212 FALSE TRUE
## X998 81.500000 1.2121212 FALSE TRUE
## X999 81.500000 1.2121212 FALSE TRUE
## X1000 54.000000 1.2121212 FALSE TRUE
## X1001 54.000000 1.2121212 FALSE TRUE
## X1002 164.000000 1.2121212 FALSE TRUE
## X1003 164.000000 1.2121212 FALSE TRUE
## X1004 164.000000 1.2121212 FALSE TRUE
## X1005 164.000000 1.2121212 FALSE TRUE
## X1006 164.000000 1.2121212 FALSE TRUE
## X1007 164.000000 1.2121212 FALSE TRUE
## X1008 164.000000 1.2121212 FALSE TRUE
## X1009 164.000000 1.2121212 FALSE TRUE
## X1010 164.000000 1.2121212 FALSE TRUE
## X1011 54.000000 1.2121212 FALSE TRUE
## X1012 54.000000 1.2121212 FALSE TRUE
## X1013 164.000000 1.2121212 FALSE TRUE
## X1014 54.000000 1.2121212 FALSE TRUE
## X1015 164.000000 1.2121212 FALSE TRUE
## X1016 22.571429 1.2121212 FALSE TRUE
## X1017 81.500000 1.2121212 FALSE TRUE
## X1018 81.500000 1.2121212 FALSE TRUE
## X1019 81.500000 1.2121212 FALSE TRUE
## X1020 81.500000 1.2121212 FALSE TRUE
## X1021 81.500000 1.2121212 FALSE TRUE
## X1022 81.500000 1.2121212 FALSE TRUE
## X1023 81.500000 1.2121212 FALSE TRUE
## X1024 81.500000 1.2121212 FALSE TRUE
## X1025 81.500000 1.2121212 FALSE TRUE
## X1026 81.500000 1.2121212 FALSE TRUE
## X1027 81.500000 1.2121212 FALSE TRUE
## X1028 81.500000 1.2121212 FALSE TRUE
## X1029 81.500000 1.2121212 FALSE TRUE
## X1030 81.500000 1.2121212 FALSE TRUE
## X1031 164.000000 1.2121212 FALSE TRUE
## X1032 164.000000 1.2121212 FALSE TRUE
## X1033 164.000000 1.2121212 FALSE TRUE
## X1034 164.000000 1.2121212 FALSE TRUE
## X1035 164.000000 1.2121212 FALSE TRUE
## X1036 164.000000 1.2121212 FALSE TRUE
## X1037 164.000000 1.2121212 FALSE TRUE
## X1038 164.000000 1.2121212 FALSE TRUE
## X1039 164.000000 1.2121212 FALSE TRUE
## X1040 164.000000 1.2121212 FALSE TRUE
## X1041 164.000000 1.2121212 FALSE TRUE
## X1042 164.000000 1.2121212 FALSE TRUE
## X1043 164.000000 1.2121212 FALSE TRUE
## X1044 164.000000 1.2121212 FALSE TRUE
## X1045 164.000000 1.2121212 FALSE TRUE
## X1046 164.000000 1.2121212 FALSE TRUE
## X1047 164.000000 1.2121212 FALSE TRUE
## X1048 164.000000 1.2121212 FALSE TRUE
## X1049 164.000000 1.2121212 FALSE TRUE
## X1050 164.000000 1.2121212 FALSE TRUE
## X1051 164.000000 1.2121212 FALSE TRUE
## X1052 164.000000 1.2121212 FALSE TRUE
## X1053 164.000000 1.2121212 FALSE TRUE
## X1054 164.000000 1.2121212 FALSE TRUE
## X1055 81.500000 1.2121212 FALSE TRUE
## X1056 164.000000 1.2121212 FALSE TRUE
## X1057 164.000000 1.2121212 FALSE TRUE
## X1058 164.000000 1.2121212 FALSE TRUE
## X1059 164.000000 1.2121212 FALSE TRUE
## X1060 164.000000 1.2121212 FALSE TRUE
## X1061 164.000000 1.2121212 FALSE TRUE
## X1062 81.500000 1.2121212 FALSE TRUE
## X1063 81.500000 1.2121212 FALSE TRUE
## X1064 81.500000 1.2121212 FALSE TRUE
## X1065 81.500000 1.2121212 FALSE TRUE
## X1066 81.500000 1.2121212 FALSE TRUE
## X1067 81.500000 1.2121212 FALSE TRUE
## X1068 81.500000 1.2121212 FALSE TRUE
## X1069 81.500000 1.2121212 FALSE TRUE
## X1070 81.500000 1.2121212 FALSE TRUE
## X1071 81.500000 1.2121212 FALSE TRUE
## X1072 81.500000 1.2121212 FALSE TRUE
## X1073 81.500000 1.2121212 FALSE TRUE
## X1074 81.500000 1.2121212 FALSE TRUE
## X1075 81.500000 1.2121212 FALSE TRUE
## X1076 81.500000 1.2121212 FALSE TRUE
## X1077 81.500000 1.2121212 FALSE TRUE
## X1078 81.500000 1.2121212 FALSE TRUE
## X1079 81.500000 1.2121212 FALSE TRUE
## X1080 81.500000 1.2121212 FALSE TRUE
## X1081 164.000000 1.2121212 FALSE TRUE
## X1082 164.000000 1.2121212 FALSE TRUE
## X1083 164.000000 1.2121212 FALSE TRUE
## X1084 164.000000 1.2121212 FALSE TRUE
## X1085 164.000000 1.2121212 FALSE TRUE
## X1086 164.000000 1.2121212 FALSE TRUE
## X1087 164.000000 1.2121212 FALSE TRUE
## X1088 164.000000 1.2121212 FALSE TRUE
## X1089 164.000000 1.2121212 FALSE TRUE
## X1090 164.000000 1.2121212 FALSE TRUE
## X1091 164.000000 1.2121212 FALSE TRUE
## X1092 164.000000 1.2121212 FALSE TRUE
## X1093 164.000000 1.2121212 FALSE TRUE
## X1094 164.000000 1.2121212 FALSE TRUE
## X1095 164.000000 1.2121212 FALSE TRUE
## X1096 164.000000 1.2121212 FALSE TRUE
## X1097 164.000000 1.2121212 FALSE TRUE
## X1098 164.000000 1.2121212 FALSE TRUE
## X1099 164.000000 1.2121212 FALSE TRUE
## X1100 164.000000 1.2121212 FALSE TRUE
## X1101 164.000000 1.2121212 FALSE TRUE
## X1102 164.000000 1.2121212 FALSE TRUE
## X1103 164.000000 1.2121212 FALSE TRUE
## X1104 81.500000 1.2121212 FALSE TRUE
## X1105 164.000000 1.2121212 FALSE TRUE
## X1106 164.000000 1.2121212 FALSE TRUE
## X1107 164.000000 1.2121212 FALSE TRUE#How many have a near-zero variance?
count(nzv_results_na |> filter(nzv == "TRUE"))## n
## 1 719#zero?
count(nzv_results_na |> filter(zeroVar == "TRUE"))## n
## 1 38719 predictors out of 1107 have a near-zero variance and 38 have a zero variance. I will filter out the ones with a near-zero variance. We are left with 388 predictors.
nzv_cols <- nearZeroVar(fingerprints_no_na)
# remove near-zero variance columns
fingerprints_filtered <- fingerprints_no_na[, -nzv_cols]- Split the data into a training and a test set, pre-process the data, and tune a PLS model. How many latent variables are optimal and what is the corresponding resampled estimate of R2?
library(pls)## Warning: package 'pls' was built under R version 4.5.3##
## Attaching package: 'pls'## The following object is masked from 'package:caret':
##
## R2## The following object is masked from 'package:stats':
##
## loadingsset.seed(1122)
#splitting the data
train_index <- sample(nrow(fingerprints_filtered), 0.8 * nrow(fingerprints_filtered))
#train and test sets
train_x <- fingerprints_filtered[train_index, ]
test_x <- fingerprints_filtered[-train_index, ]
train_y <- permeability[train_index]
test_y <- permeability[-train_index]
#center and scale
ctrl <- trainControl(method = "cv", number = 10)
set.seed(1122)
pls_fit <- train(x = train_x, y = train_y,
method = "pls",
preProcess = c("center", "scale"),
tuneLength = 20,
trControl = ctrl)
pls_fit## Partial Least Squares
##
## 132 samples
## 388 predictors
##
## Pre-processing: centered (388), scaled (388)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 118, 117, 119, 118, 120, 118, ...
## Resampling results across tuning parameters:
##
## ncomp RMSE Rsquared MAE
## 1 13.57724 0.2837056 10.127946
## 2 12.30032 0.4482849 8.759051
## 3 12.09596 0.4700672 9.008308
## 4 12.19644 0.4492237 9.183328
## 5 11.90474 0.4689247 8.591967
## 6 11.64633 0.4824163 8.547223
## 7 11.48189 0.5005164 8.658402
## 8 11.38320 0.5006351 8.685066
## 9 11.41308 0.5002719 8.676056
## 10 11.20404 0.5043939 8.376123
## 11 11.50949 0.4830380 8.616746
## 12 11.69962 0.4772903 8.733629
## 13 12.00439 0.4693062 8.964077
## 14 12.21016 0.4550708 9.037039
## 15 12.23332 0.4539271 9.159544
## 16 12.41752 0.4441139 9.426669
## 17 12.41706 0.4509045 9.470869
## 18 12.64495 0.4398128 9.603646
## 19 12.57897 0.4477432 9.456016
## 20 12.65191 0.4474425 9.407062
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was ncomp = 10.#look at line 8 or...
pls_fit$results[pls_fit$results$ncomp == 8, ]## ncomp RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 8 8 11.3832 0.5006351 8.685066 1.886547 0.1653632 1.5833288 latent variables are optimal (I sampled up to 20). The corresponding resampled estimate of Rsquared is 0.5006351. This means a model with 8 variables will explain about 50% of variability.
- Predict the response for the test set. What is the test set estimate of R2?
#the ncomp = 8 is included just in case:
pls_pred <- predict(pls_fit, test_x, ncomp = 8)
postResample(pred = pls_pred, obs = test_y)## RMSE Rsquared MAE
## 11.6666153 0.4750516 8.5228029Rsquared is ~.4750 (explains ~48% of variability)
- Try building other models discussed in this chapter. Do any have better predictive performance?
library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectlibrary(elasticnet)## Warning: package 'elasticnet' was built under R version 4.5.2## Loading required package: lars## Warning: package 'lars' was built under R version 4.5.2## Loaded lars 1.3#ridge model
ridgeGrid <- data.frame(.lambda = seq(.01, 1, length = 10))
set.seed(1122)
ridgeRegFit <- train(x = train_x, y = train_y,
method = "ridge",
tuneGrid = ridgeGrid,
trControl = ctrl,
preProcess = c("center", "scale")
)
ridgeRegFit## Ridge Regression
##
## 132 samples
## 388 predictors
##
## Pre-processing: centered (388), scaled (388)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 118, 117, 119, 118, 120, 118, ...
## Resampling results across tuning parameters:
##
## lambda RMSE Rsquared MAE
## 0.01 14.06395 0.4192879 10.452797
## 0.12 11.51387 0.5053150 8.605559
## 0.23 11.67850 0.5113821 8.829369
## 0.34 12.01691 0.5132572 9.165252
## 0.45 12.46283 0.5133837 9.542059
## 0.56 12.99089 0.5128516 9.944985
## 0.67 13.56097 0.5127485 10.396659
## 0.78 14.19247 0.5120521 10.883624
## 0.89 14.86607 0.5112173 11.429236
## 1.00 15.57068 0.5104574 12.002643
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was lambda = 0.12.ridgemodel <- enet(x = train_x, y = train_y,
lambda = .12)
ridgePred <- predict(ridgemodel, newx = test_x,
s = 1, mode = "fraction",
type = "fit")
head(ridgePred$fit)## 6 9 12 13 21 22
## -1.433210 35.492122 3.765566 2.857956 11.584029 25.094265postResample(pred = ridgePred$fit, obs = test_y)## RMSE Rsquared MAE
## 12.3024130 0.4435375 9.2364984Rsquared is a little higher at .4435.
Let’s try elastic net:
enetGrid <- expand.grid(.fraction = seq(0.01, .5, length = 15),
.lambda = c(0.0001, 0.01, 0.1))
set.seed(1122)
elasticFit <- train(x = train_x, y = train_y,
method = "enet",
tuneGrid = enetGrid,
trControl = ctrl,
preProcess = c("center", "scale"))
elasticFit## Elasticnet
##
## 132 samples
## 388 predictors
##
## Pre-processing: centered (388), scaled (388)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 118, 117, 119, 118, 120, 118, ...
## Resampling results across tuning parameters:
##
## lambda fraction RMSE Rsquared MAE
## 1e-04 0.010 235.80243 0.3248293 133.121070
## 1e-04 0.045 988.97562 0.3321181 564.359065
## 1e-04 0.080 1662.85031 0.3482649 961.292769
## 1e-04 0.115 2314.52002 0.3517844 1349.278666
## 1e-04 0.150 2911.53543 0.3649244 1728.269071
## 1e-04 0.185 3483.75553 0.3717994 2105.286380
## 1e-04 0.220 4060.99013 0.3726633 2484.715300
## 1e-04 0.255 4650.74750 0.3745589 2871.790360
## 1e-04 0.290 5242.93273 0.3675641 3258.924833
## 1e-04 0.325 5836.92432 0.3546426 3645.682799
## 1e-04 0.360 6431.90610 0.3386418 4032.430182
## 1e-04 0.395 7027.42918 0.3243592 4419.151544
## 1e-04 0.430 7622.41106 0.3126143 4805.811821
## 1e-04 0.465 8217.87733 0.3058996 5192.293174
## 1e-04 0.500 8814.05311 0.3012705 5578.752651
## 1e-02 0.010 13.85823 0.4274922 10.843371
## 1e-02 0.045 11.87675 0.4418410 8.450941
## 1e-02 0.080 11.54108 0.4541965 8.389665
## 1e-02 0.115 11.19659 0.4756338 8.388991
## 1e-02 0.150 11.10326 0.4798895 8.280005
## 1e-02 0.185 11.08749 0.4795548 8.246434
## 1e-02 0.220 11.09991 0.4817295 8.254956
## 1e-02 0.255 11.11563 0.4886795 8.369220
## 1e-02 0.290 11.14419 0.4966530 8.470277
## 1e-02 0.325 11.27750 0.4938349 8.590632
## 1e-02 0.360 11.41130 0.4895299 8.678738
## 1e-02 0.395 11.51246 0.4863815 8.714770
## 1e-02 0.430 11.58998 0.4850011 8.712699
## 1e-02 0.465 11.68956 0.4810494 8.706457
## 1e-02 0.500 11.84406 0.4743715 8.745573
## 1e-01 0.010 14.52187 0.3934782 11.508540
## 1e-01 0.045 12.70954 0.4179220 9.494487
## 1e-01 0.080 11.95514 0.4367611 8.595315
## 1e-01 0.115 11.66694 0.4456595 8.257624
## 1e-01 0.150 11.52899 0.4547711 8.313850
## 1e-01 0.185 11.27608 0.4727605 8.295371
## 1e-01 0.220 11.06668 0.4864207 8.238790
## 1e-01 0.255 10.94864 0.4943926 8.164561
## 1e-01 0.290 10.93920 0.4968724 8.178985
## 1e-01 0.325 10.95804 0.4976470 8.220887
## 1e-01 0.360 10.98001 0.4976770 8.228610
## 1e-01 0.395 10.97619 0.5000710 8.201571
## 1e-01 0.430 10.94835 0.5043785 8.155001
## 1e-01 0.465 10.94659 0.5074863 8.161337
## 1e-01 0.500 10.93285 0.5123626 8.155905
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were fraction = 0.5 and lambda = 0.1.enetModel <- enet(x = as.matrix(train_x), y = train_y,
lambda = 0.1,
normalize = TRUE)
enetPred <- predict(enetModel,
newx = as.matrix(test_x),
s = 0.5,
mode = "fraction",
type = "fit")
names(enetPred)## [1] "s" "fraction" "mode" "fit"head(enetPred$fit)## 6 9 12 13 21 22
## -4.420626 35.142429 4.489549 5.238230 12.833648 31.158588enetCoef<- predict(enetModel, newx = test_x,
s = .255, mode = "fraction",
type = "coefficients")
tail(enetCoef$coefficients)## X800 X801 X805 X806 X812 X813
## 0 0 0 0 0 0head(enetCoef$coefficients)## X1 X2 X3 X4 X5 X6
## 0.000000 0.000000 0.000000 0.000000 0.000000 8.040296#actually checking the fit
postResample(pred = enetPred$fit, obs = test_y)## RMSE Rsquared MAE
## 12.0253553 0.4694902 9.5279536R squared is .469, accounting for about ~47% of variability.
Lasso:
lassoGrid <- expand.grid(fraction = seq(0.01, 1, length = 20))
set.seed(1122)
lassoFit <- train(x = train_x, y = train_y,
method = "lasso",
tuneGrid = lassoGrid,
trControl = ctrl,
preProcess = c("center", "scale"))
lassoPred <- predict(lassoFit, test_x)
postResample(pred = lassoPred, obs = test_y)## RMSE Rsquared MAE
## 12.6945466 0.3965324 9.4708651R squared is .397 here, slightly lower than previous models.
- Would you recommend any of your models to replace the permeability laboratory experiment?
These are all relatively similar in terms of how well they predicted variability. Elastic net model comes the closest to PLS with an r squared of ~.47, explaining about 47% of variability. However, it is less computationally efficient than PLS.
6.3. A chemical manufacturing process for a pharmaceutical product was discussed in Sect.1.4. In this problem, the objective is to understand the relationship between biological measurements of the raw materials (predictors measurements of the manufacturing process (predictors), and the response of product yield. Biological predictors cannot be changed but can be used to assess the quality of the raw material before processing. On the other hand, manufacturing process predictors can be changed in the manufacturing process. Improving product yield by 1% will boost revenue by approximately one hundred thousand dollars per batch:
- Start R and use these commands to load the data:
library(AppliedPredictiveModeling)
data(chemicalManufacturingProcess)
data(ChemicalManufacturingProcess)The matrix processPredictors contains the 57 predictors (12 describing the input biological material and 45 describing the process predictors) for the 176 manufacturing runs. yield contains the percent yield for each run.
- A small percentage of cells in the predictor set contain missing values. Use an imputation function to fill in these missing values (e.g., see Sect.3.8).
Simple KNN with the VIM package:
#check the NA values
colSums(is.na(ChemicalManufacturingProcess))## Yield BiologicalMaterial01 BiologicalMaterial02
## 0 0 0
## BiologicalMaterial03 BiologicalMaterial04 BiologicalMaterial05
## 0 0 0
## BiologicalMaterial06 BiologicalMaterial07 BiologicalMaterial08
## 0 0 0
## BiologicalMaterial09 BiologicalMaterial10 BiologicalMaterial11
## 0 0 0
## BiologicalMaterial12 ManufacturingProcess01 ManufacturingProcess02
## 0 1 3
## ManufacturingProcess03 ManufacturingProcess04 ManufacturingProcess05
## 15 1 1
## ManufacturingProcess06 ManufacturingProcess07 ManufacturingProcess08
## 2 1 1
## ManufacturingProcess09 ManufacturingProcess10 ManufacturingProcess11
## 0 9 10
## ManufacturingProcess12 ManufacturingProcess13 ManufacturingProcess14
## 1 0 1
## ManufacturingProcess15 ManufacturingProcess16 ManufacturingProcess17
## 0 0 0
## ManufacturingProcess18 ManufacturingProcess19 ManufacturingProcess20
## 0 0 0
## ManufacturingProcess21 ManufacturingProcess22 ManufacturingProcess23
## 0 1 1
## ManufacturingProcess24 ManufacturingProcess25 ManufacturingProcess26
## 1 5 5
## ManufacturingProcess27 ManufacturingProcess28 ManufacturingProcess29
## 5 5 5
## ManufacturingProcess30 ManufacturingProcess31 ManufacturingProcess32
## 5 5 0
## ManufacturingProcess33 ManufacturingProcess34 ManufacturingProcess35
## 5 5 5
## ManufacturingProcess36 ManufacturingProcess37 ManufacturingProcess38
## 5 0 0
## ManufacturingProcess39 ManufacturingProcess40 ManufacturingProcess41
## 0 1 1
## ManufacturingProcess42 ManufacturingProcess43 ManufacturingProcess44
## 0 0 0
## ManufacturingProcess45
## 0#VIM package for simple imputation
library(VIM)## Warning: package 'VIM' was built under R version 4.5.3## Loading required package: colorspace## Warning: package 'colorspace' was built under R version 4.5.3## Loading required package: grid## VIM is ready to use.## Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues##
## Attaching package: 'VIM'## The following object is masked from 'package:datasets':
##
## sleepset.seed(1122)
chem_imputed <- kNN(ChemicalManufacturingProcess, k = 5)
#check that there are no missing values
colSums(is.na(chem_imputed))## Yield BiologicalMaterial01
## 0 0
## BiologicalMaterial02 BiologicalMaterial03
## 0 0
## BiologicalMaterial04 BiologicalMaterial05
## 0 0
## BiologicalMaterial06 BiologicalMaterial07
## 0 0
## BiologicalMaterial08 BiologicalMaterial09
## 0 0
## BiologicalMaterial10 BiologicalMaterial11
## 0 0
## BiologicalMaterial12 ManufacturingProcess01
## 0 0
## ManufacturingProcess02 ManufacturingProcess03
## 0 0
## ManufacturingProcess04 ManufacturingProcess05
## 0 0
## ManufacturingProcess06 ManufacturingProcess07
## 0 0
## ManufacturingProcess08 ManufacturingProcess09
## 0 0
## ManufacturingProcess10 ManufacturingProcess11
## 0 0
## ManufacturingProcess12 ManufacturingProcess13
## 0 0
## ManufacturingProcess14 ManufacturingProcess15
## 0 0
## ManufacturingProcess16 ManufacturingProcess17
## 0 0
## ManufacturingProcess18 ManufacturingProcess19
## 0 0
## ManufacturingProcess20 ManufacturingProcess21
## 0 0
## ManufacturingProcess22 ManufacturingProcess23
## 0 0
## ManufacturingProcess24 ManufacturingProcess25
## 0 0
## ManufacturingProcess26 ManufacturingProcess27
## 0 0
## ManufacturingProcess28 ManufacturingProcess29
## 0 0
## ManufacturingProcess30 ManufacturingProcess31
## 0 0
## ManufacturingProcess32 ManufacturingProcess33
## 0 0
## ManufacturingProcess34 ManufacturingProcess35
## 0 0
## ManufacturingProcess36 ManufacturingProcess37
## 0 0
## ManufacturingProcess38 ManufacturingProcess39
## 0 0
## ManufacturingProcess40 ManufacturingProcess41
## 0 0
## ManufacturingProcess42 ManufacturingProcess43
## 0 0
## ManufacturingProcess44 ManufacturingProcess45
## 0 0
## Yield_imp BiologicalMaterial01_imp
## 0 0
## BiologicalMaterial02_imp BiologicalMaterial03_imp
## 0 0
## BiologicalMaterial04_imp BiologicalMaterial05_imp
## 0 0
## BiologicalMaterial06_imp BiologicalMaterial07_imp
## 0 0
## BiologicalMaterial08_imp BiologicalMaterial09_imp
## 0 0
## BiologicalMaterial10_imp BiologicalMaterial11_imp
## 0 0
## BiologicalMaterial12_imp ManufacturingProcess01_imp
## 0 0
## ManufacturingProcess02_imp ManufacturingProcess03_imp
## 0 0
## ManufacturingProcess04_imp ManufacturingProcess05_imp
## 0 0
## ManufacturingProcess06_imp ManufacturingProcess07_imp
## 0 0
## ManufacturingProcess08_imp ManufacturingProcess09_imp
## 0 0
## ManufacturingProcess10_imp ManufacturingProcess11_imp
## 0 0
## ManufacturingProcess12_imp ManufacturingProcess13_imp
## 0 0
## ManufacturingProcess14_imp ManufacturingProcess15_imp
## 0 0
## ManufacturingProcess16_imp ManufacturingProcess17_imp
## 0 0
## ManufacturingProcess18_imp ManufacturingProcess19_imp
## 0 0
## ManufacturingProcess20_imp ManufacturingProcess21_imp
## 0 0
## ManufacturingProcess22_imp ManufacturingProcess23_imp
## 0 0
## ManufacturingProcess24_imp ManufacturingProcess25_imp
## 0 0
## ManufacturingProcess26_imp ManufacturingProcess27_imp
## 0 0
## ManufacturingProcess28_imp ManufacturingProcess29_imp
## 0 0
## ManufacturingProcess30_imp ManufacturingProcess31_imp
## 0 0
## ManufacturingProcess32_imp ManufacturingProcess33_imp
## 0 0
## ManufacturingProcess34_imp ManufacturingProcess35_imp
## 0 0
## ManufacturingProcess36_imp ManufacturingProcess37_imp
## 0 0
## ManufacturingProcess38_imp ManufacturingProcess39_imp
## 0 0
## ManufacturingProcess40_imp ManufacturingProcess41_imp
## 0 0
## ManufacturingProcess42_imp ManufacturingProcess43_imp
## 0 0
## ManufacturingProcess44_imp ManufacturingProcess45_imp
## 0 0chem_final <- chem_imputed |>
dplyr::select(-ends_with("_imp"))- Split the data into a training and a test set, pre-process the data, and tune a model of your choice from this chapter. What is the optimal value of the performance metric?
nzv_results <- nearZeroVar(chem_final, saveMetrics = TRUE)
nzv_results## freqRatio percentUnique zeroVar nzv
## Yield 1.333333 85.227273 FALSE FALSE
## BiologicalMaterial01 1.166667 50.568182 FALSE FALSE
## BiologicalMaterial02 1.000000 60.227273 FALSE FALSE
## BiologicalMaterial03 1.000000 57.386364 FALSE FALSE
## BiologicalMaterial04 1.500000 57.954545 FALSE FALSE
## BiologicalMaterial05 1.500000 58.522727 FALSE FALSE
## BiologicalMaterial06 1.333333 59.659091 FALSE FALSE
## BiologicalMaterial07 57.666667 1.136364 FALSE TRUE
## BiologicalMaterial08 1.000000 51.136364 FALSE FALSE
## BiologicalMaterial09 1.571429 44.886364 FALSE FALSE
## BiologicalMaterial10 1.111111 44.886364 FALSE FALSE
## BiologicalMaterial11 1.000000 59.659091 FALSE FALSE
## BiologicalMaterial12 1.285714 49.431818 FALSE FALSE
## ManufacturingProcess01 1.090909 23.295455 FALSE FALSE
## ManufacturingProcess02 1.842105 15.340909 FALSE FALSE
## ManufacturingProcess03 2.052632 7.954545 FALSE FALSE
## ManufacturingProcess04 1.294118 15.909091 FALSE FALSE
## ManufacturingProcess05 1.000000 87.500000 FALSE FALSE
## ManufacturingProcess06 1.000000 22.727273 FALSE FALSE
## ManufacturingProcess07 1.070588 1.136364 FALSE FALSE
## ManufacturingProcess08 1.256410 1.136364 FALSE FALSE
## ManufacturingProcess09 1.000000 84.090909 FALSE FALSE
## ManufacturingProcess10 1.142857 20.454545 FALSE FALSE
## ManufacturingProcess11 1.416667 19.886364 FALSE FALSE
## ManufacturingProcess12 4.333333 1.136364 FALSE FALSE
## ManufacturingProcess13 1.090909 23.863636 FALSE FALSE
## ManufacturingProcess14 1.000000 64.772727 FALSE FALSE
## ManufacturingProcess15 1.000000 67.613636 FALSE FALSE
## ManufacturingProcess16 1.000000 68.181818 FALSE FALSE
## ManufacturingProcess17 1.200000 23.863636 FALSE FALSE
## ManufacturingProcess18 1.500000 60.227273 FALSE FALSE
## ManufacturingProcess19 1.000000 63.636364 FALSE FALSE
## ManufacturingProcess20 1.000000 63.636364 FALSE FALSE
## ManufacturingProcess21 2.647059 18.750000 FALSE FALSE
## ManufacturingProcess22 1.105263 7.386364 FALSE FALSE
## ManufacturingProcess23 1.000000 3.977273 FALSE FALSE
## ManufacturingProcess24 1.000000 13.636364 FALSE FALSE
## ManufacturingProcess25 1.200000 60.227273 FALSE FALSE
## ManufacturingProcess26 1.400000 59.659091 FALSE FALSE
## ManufacturingProcess27 1.200000 58.522727 FALSE FALSE
## ManufacturingProcess28 5.461538 9.659091 FALSE FALSE
## ManufacturingProcess29 1.800000 17.045455 FALSE FALSE
## ManufacturingProcess30 1.266667 18.181818 FALSE FALSE
## ManufacturingProcess31 1.181818 25.000000 FALSE FALSE
## ManufacturingProcess32 1.047619 15.340909 FALSE FALSE
## ManufacturingProcess33 1.066667 7.954545 FALSE FALSE
## ManufacturingProcess34 4.379310 2.272727 FALSE FALSE
## ManufacturingProcess35 1.083333 26.136364 FALSE FALSE
## ManufacturingProcess36 1.090909 3.409091 FALSE FALSE
## ManufacturingProcess37 1.050000 11.931818 FALSE FALSE
## ManufacturingProcess38 1.552239 1.704545 FALSE FALSE
## ManufacturingProcess39 1.150000 5.681818 FALSE FALSE
## ManufacturingProcess40 4.500000 1.136364 FALSE FALSE
## ManufacturingProcess41 6.217391 2.272727 FALSE FALSE
## ManufacturingProcess42 1.166667 9.659091 FALSE FALSE
## ManufacturingProcess43 1.000000 13.068182 FALSE FALSE
## ManufacturingProcess44 1.233333 4.545455 FALSE FALSE
## ManufacturingProcess45 1.026316 5.681818 FALSE FALSE#there's only one variable with near-zero, so we will leave this
count(nzv_results |> filter(nzv == "TRUE"))## n
## 1 1count(nzv_results |> filter(zeroVar == "TRUE"))## n
## 1 0#splitting the data
set.seed(1122)
train_index <- sample(nrow(chem_final), 0.8 * nrow(chem_final))
#train and test sets (yield + predictors are all in the same df)
train_y <- chem_final$Yield[train_index]
train_x <- chem_final[train_index, ] %>% dplyr::select(-Yield)
test_y <- chem_final$Yield[-train_index]
test_x <- chem_final[-train_index, ] %>% dplyr::select(-Yield)Let’s try Lasso:
#center and scale
ctrl <- trainControl(method = "cv", number = 10)
set.seed(1122)
lassoFit <- train(x = train_x, train_y,
method = "lasso",
trControl = ctrl,
preProcess = c("center", "scale"))lassoFit$bestTune## fraction
## 1 0.1lassoFit$results %>%
filter(fraction == lassoFit$bestTune$fraction)## fraction RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0.1 1.223858 0.609268 1.001932 0.1839246 0.1536392 0.1655507.1 means only a small number of the predictors are actually useful, as calculated by Lasso. Most of the predictors are noise. The model has shrunk coefficients to 10% of what they would be in a normal LR.
- Predict the response for the test set. What is the value of the performance metric and how does this compare with the resampled performance metric on the training set?
set.seed(1122)
lassoPred <- predict(lassoFit, test_x)
postResample(pred = lassoPred, obs = test_y)## RMSE Rsquared MAE
## 1.0935780 0.5257068 0.8451554R squared is ~.526, explaining 52.6% of variability (worse than the training data’s 60%). The RMSE is slightly lower than the training set’s at 1.093 (the training set is 1.22).
- Which predictors are most important in the model you have trained? Do either the biological or process predictors dominate the list?
enetModel <- enet(x = as.matrix(train_x),
y = train_y,
lambda = 0,
normalize = TRUE)
enetCoef <- predict(enetModel,
s = 0.1,
mode = "fraction",
type = "coefficients")
final_coefs <- enetCoef$coefficients
final_coefs[final_coefs != 0]## BiologicalMaterial03 BiologicalMaterial06 ManufacturingProcess06
## 0.01350541 0.02135923 0.02003332
## ManufacturingProcess09 ManufacturingProcess13 ManufacturingProcess17
## 0.17102400 -0.24353217 -0.21627023
## ManufacturingProcess29 ManufacturingProcess30 ManufacturingProcess32
## 0.11021801 0.10478250 0.12959251
## ManufacturingProcess34 ManufacturingProcess36 ManufacturingProcess37
## 2.47574637 -238.09367476 -0.10201989
## ManufacturingProcess39 ManufacturingProcess42 ManufacturingProcess45
## 0.03416185 0.04610938 0.01282890Without even counting, the manufacturing process predictors dominate. However, there are more processing predictors in general (45 compared to 12). By order of importance:
sort(abs(final_coefs), decreasing = TRUE)## ManufacturingProcess36 ManufacturingProcess34 ManufacturingProcess13
## 238.09367476 2.47574637 0.24353217
## ManufacturingProcess17 ManufacturingProcess09 ManufacturingProcess32
## 0.21627023 0.17102400 0.12959251
## ManufacturingProcess29 ManufacturingProcess30 ManufacturingProcess37
## 0.11021801 0.10478250 0.10201989
## ManufacturingProcess42 ManufacturingProcess39 BiologicalMaterial06
## 0.04610938 0.03416185 0.02135923
## ManufacturingProcess06 BiologicalMaterial03 ManufacturingProcess45
## 0.02003332 0.01350541 0.01282890
## BiologicalMaterial01 BiologicalMaterial02 BiologicalMaterial04
## 0.00000000 0.00000000 0.00000000
## BiologicalMaterial05 BiologicalMaterial07 BiologicalMaterial08
## 0.00000000 0.00000000 0.00000000
## BiologicalMaterial09 BiologicalMaterial10 BiologicalMaterial11
## 0.00000000 0.00000000 0.00000000
## BiologicalMaterial12 ManufacturingProcess01 ManufacturingProcess02
## 0.00000000 0.00000000 0.00000000
## ManufacturingProcess03 ManufacturingProcess04 ManufacturingProcess05
## 0.00000000 0.00000000 0.00000000
## ManufacturingProcess07 ManufacturingProcess08 ManufacturingProcess10
## 0.00000000 0.00000000 0.00000000
## ManufacturingProcess11 ManufacturingProcess12 ManufacturingProcess14
## 0.00000000 0.00000000 0.00000000
## ManufacturingProcess15 ManufacturingProcess16 ManufacturingProcess18
## 0.00000000 0.00000000 0.00000000
## ManufacturingProcess19 ManufacturingProcess20 ManufacturingProcess21
## 0.00000000 0.00000000 0.00000000
## ManufacturingProcess22 ManufacturingProcess23 ManufacturingProcess24
## 0.00000000 0.00000000 0.00000000
## ManufacturingProcess25 ManufacturingProcess26 ManufacturingProcess27
## 0.00000000 0.00000000 0.00000000
## ManufacturingProcess28 ManufacturingProcess31 ManufacturingProcess33
## 0.00000000 0.00000000 0.00000000
## ManufacturingProcess35 ManufacturingProcess38 ManufacturingProcess40
## 0.00000000 0.00000000 0.00000000
## ManufacturingProcess41 ManufacturingProcess43 ManufacturingProcess44
## 0.00000000 0.00000000 0.00000000- Explore the relationships between each of the top predictors and the response. How could this information be helpful in improving yield in future runs of the manufacturing process?
#plot(lassoFit)
#top 12 predictors (these all fit in one view)
top_vars <- names(sort(abs(final_coefs), decreasing = TRUE)[1:12])
chem_final[train_index, ] %>%
dplyr::select(Yield, all_of(top_vars)) %>%
pivot_longer(cols = -Yield, names_to = "Predictor", values_to = "Value") %>%
ggplot(aes(x = Value, y = Yield)) + geom_point(alpha = 0.4, color = "red") +
geom_smooth(method = lm) +
facet_wrap(~Predictor, scales = "free_x") ## `geom_smooth()` using formula = 'y ~ x'
Some values correlate strongly with a higher yield, like material 6 and process 32. Increasing these could mean a higher yield. 36 seems to have a correlation with a lower yield, so decreasing this may help production.
There are a few variables where a few samples that show a strong correlation, but there’s a large cluster of data on one end of the spectrum and not much on the other (for example, processes 13 and 17, bio material 06, which all have a negative slope). There may not be sufficient data to draw a definitive conclusion. I want to note that biological material 07, the predictor that’s ranked second, has many a cluster of many data points that show a whole range of yield on one side of the graph and only two data points on the other. This seems particularly unreliable.
After ~10 predictors, visually, there doesn’t seem to be much of a correlation. Process 45, for example, looks like a straight line.