Package 'caret' - The Comprehensive R Archive Network

206 Pages·2016·0.54 MB·English

Package ‘caret’
October12,2022
Title ClassificationandRegressionTraining
Version 6.0-93
Description Miscfunctionsfortrainingandplottingclassificationand
regressionmodels.
License GPL(>=2)
URL https://github.com/topepo/caret/
BugReports https://github.com/topepo/caret/issues
Depends ggplot2,lattice(>=0.20),R(>=3.2.0)
Imports e1071,foreach,grDevices,methods,ModelMetrics(>=1.2.2.2),
nlme,plyr,pROC,recipes(>=0.1.10),reshape2,stats,stats4,
utils,withr(>=2.0.0)
Suggests BradleyTerry2,covr,Cubist,dplyr,earth(>=2.2-3),
ellipse,fastICA,gam(>=1.15),ipred,kernlab,klaR,knitr,
MASS,Matrix,mda,mgcv,mlbench,MLmetrics,nnet,pamr,party
(>=0.9-99992),pls,proxy,randomForest,RANN,rmarkdown,
rpart,spls,subselect,superpc,testthat(>=0.9.1),themis
(>=0.1.3)
VignetteBuilder knitr
Encoding UTF-8
RoxygenNote 7.2.0
NeedsCompilation yes
Author MaxKuhn[aut,cre](<https://orcid.org/0000-0003-2402-136X>),
JedWing[ctb],
SteveWeston[ctb],
AndreWilliams[ctb],
ChrisKeefer[ctb],
AllanEngelhardt[ctb],
TonyCooper[ctb],
ZacharyMayer[ctb],
BrentonKenkel[ctb],
RCoreTeam[ctb],
1
2 Rtopicsdocumented:
MichaelBenesty[ctb],
ReynaldLescarbeau[ctb],
AndrewZiem[ctb],
LucaScrucca[ctb],
YuanTang[ctb],
CanCandan[ctb],
TylerHunt[ctb]
Maintainer MaxKuhn<[email protected]>
Repository CRAN
Date/Publication 2022-08-0910:00:02UTC
R topics documented:
as.matrix.confusionMatrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
avNNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
bag. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
bagEarth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
bagFDA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
BloodBrain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
BoxCoxTrans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
caretSBF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
cars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
classDist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
confusionMatrix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
confusionMatrix.train . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
cox2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
createDataPartition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
defaultSummary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
densityplot.rfe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
dhfr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
diff.resamples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
dotPlot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
dotplot.diff.resamples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
downSample. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
dummyVars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
extractPrediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
featurePlot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
filterVarImp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
findCorrelation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
findLinearCombos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
format.bagEarth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
gafs.default . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
gafsControl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
gafs_initial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
GermanCredit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
getSamplingInfo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
Rtopicsdocumented: 3
ggplot.rfe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
ggplot.train . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
histogram.train . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
icr.formula. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
index2vec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
knn3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
knnreg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
learning_curve_dat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
lift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
maxDissim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
mdrr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
modelLookup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
nearZeroVar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
negPredValue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
nullModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
oil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
oneSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
panel.lift2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
panel.needle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
pcaNNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
pickSizeBest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
plot.gafs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
plot.varImp.train . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
plotClassProbs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
plotObsVsPred . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
plsda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
pottery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
prcomp.resamples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
predict.bagEarth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
predict.gafs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
predict.knn3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
predict.knnreg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
predictors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
preProcess . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
print.confusionMatrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
print.train . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
recall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
resampleHist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
resamples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
resampleSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
rfe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
rfeControl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
Sacramento . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
safs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
safs_initial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
sbf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
sbfControl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
scat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
4 as.matrix.confusionMatrix
segmentationData . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
SLC14_1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
spatialSign. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
summary.bagEarth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
tecator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
thresholder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
train . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
trainControl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
train_model_list . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
update.safs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
update.train . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
varImp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
varImp.gafs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
var_seq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
xyplot.resamples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Index 219
as.matrix.confusionMatrix
Confusionmatrixasatable
Description
ConversionfunctionsforclassconfusionMatrix
Usage
## S3 method for class 'confusionMatrix'
as.matrix(x, what = "xtabs", ...)
Arguments
x anobjectofclassconfusionMatrix
what datatoconverttomatrix. Either"xtabs","overall"or"classes"
... notcurrentlyused
Details
Foras.table,thecross-tabulationsaresaved. Foras.matrix,thethreeobjecttypesaresavedin
matrixformat.
Value
Amatrixortable
Author(s)
MaxKuhn
avNNet 5
Examples
###################
## 2 class example
lvs <- c("normal", "abnormal")
truth <- factor(rep(lvs, times = c(86, 258)),
levels = rev(lvs))
pred <- factor(
c(
rep(lvs, times = c(54, 32)),
rep(lvs, times = c(27, 231))),
levels = rev(lvs))
xtab <- table(pred, truth)
results <- confusionMatrix(xtab)
as.table(results)
as.matrix(results)
as.matrix(results, what = "overall")
as.matrix(results, what = "classes")
###################
## 3 class example
xtab <- confusionMatrix(iris$Species, sample(iris$Species))
as.matrix(xtab)
avNNet NeuralNetworksUsingModelAveraging
Description
Aggregateseveralneuralnetworkmodels
Usage
avNNet(x, ...)
## S3 method for class 'formula'
avNNet(
formula,
data,
weights,
...,
repeats = 5,
bag = FALSE,
allowParallel = TRUE,
seeds = sample.int(1e+05, repeats),
6 avNNet
subset,
na.action,
contrasts = NULL
)
## Default S3 method:
avNNet(
x,
y,
repeats = 5,
bag = FALSE,
allowParallel = TRUE,
seeds = sample.int(1e+05, repeats),
...
)
## S3 method for class 'avNNet'
print(x, ...)
## S3 method for class 'avNNet'
predict(object, newdata, type = c("raw", "class", "prob"), ...)
Arguments
x matrixordataframeofxvaluesforexamples.
... argumentspassedtonnet
formula Aformulaoftheformclass~x1+x2+...
data Data frame from which variables specified in formula are preferentially to be
taken.
weights (case)weightsforeachexample-ifmissingdefaultsto1.
repeats thenumberofneuralnetworkswithdifferentrandomnumberseeds
bag alogicalforbaggingforeachrepeat
allowParallel ifaparallelbackendisloadedandavailable,shouldthefunctionuseit?
seeds random number seeds that can be set prior to bagging (if done) and network
creation. Thishelpsmaintainreproducibilitywhenmodelsareruninparallel.
subset Anindexvectorspecifyingthecasestobeusedinthetrainingsample. (NOTE:
Ifgiven,thisargumentmustbenamed.)
na.action AfunctiontospecifytheactiontobetakenifNAsarefound. Thedefaultaction
isfortheproceduretofail. Analternativeisna.omit,whichleadstorejection
of cases with missing values on any required variable. (NOTE: If given, this
argumentmustbenamed.)
contrasts alistofcontraststobeusedforsomeorallofthefactorsappearingasvariables
inthemodelformula.
y matrixordataframeoftargetvaluesforexamples.
object anobjectofclassavNNetasreturnedbyavNNet.
avNNet 7
newdata matrixordataframeoftestexamples. Avectorisconsideredtobearowvector
comprisingasinglecase.
type Typeofoutput,either: rawfortherawoutputs,codeforthepredictedclassor
probfortheclassprobabilities.
Details
FollowingRipley(1996),thesameneuralnetworkmodelisfitusingdifferentrandomnumberseeds.
Alltheresultingmodelsareusedforprediction. Forregression,theoutputfromeachnetworkare
averaged.Forclassification,themodelscoresarefirstaveraged,thentranslatedtopredictedclasses.
Baggingcanalsobeusedtocreatethemodels.
Ifaparallelbackendisregistered,theforeachpackageisusedtotrainthenetworksinparallel.
Value
ForavNNet,anobjectof"avNNet"or"avNNet.formula". Itemsofinterestin#’theoutputare:
model alistofthemodelsgeneratedfromnnet
repeats anechoofthemodelinput
names ifanypredictorshadonlyonedistinctvalue,thisisacharacterstringofthe#’
remainingcolumns. OtherwiseavalueofNULL
Author(s)
TheseareheavilybasedonthennetcodefromBrianRipley.
References
Ripley,B.D.(1996)PatternRecognitionandNeuralNetworks. Cambridge.
SeeAlso
nnet,preProcess
Examples
data(BloodBrain)
## Not run:
modelFit <- avNNet(bbbDescr, logBBB, size = 5, linout = TRUE, trace = FALSE)
modelFit
predict(modelFit, bbbDescr)
## End(Not run)
8 bag
bag AGeneralFrameworkForBagging
Description
bag provides a framework for bagging classification or regression models. The user can provide
theirownfunctionsformodelbuilding, predictionandaggregationofpredictions(seeDetailsbe-
low).
Usage
bag(x, ...)
bagControl(
fit = NULL,
predict = NULL,
aggregate = NULL,
downSample = FALSE,
oob = TRUE,
allowParallel = TRUE
)
## Default S3 method:
bag(x, y, B = 10, vars = ncol(x), bagControl = NULL, ...)
## S3 method for class 'bag'
predict(object, newdata = NULL, ...)
## S3 method for class 'bag'
print(x, ...)
## S3 method for class 'bag'
summary(object, ...)
## S3 method for class 'summary.bag'
print(x, digits = max(3, getOption("digits") - 3), ...)
ldaBag
plsBag
nbBag
ctreeBag
svmBag
bag 9
nnetBag
Arguments
x amatrixordataframeofpredictors
... argumentstopasstothemodelfunction
fit a function that has arguments x, y and ... and produces a model object #’
that can later be used for prediction. Example functions are found in ldaBag,
plsBag,#’nbBag,svmBagandnnetBag.
predict a function that generates predictions for each sub-model. The function should
have #’ arguments object and x. The output of the function can be any type
of object (see the #’ example below where posterior probabilities are gener-
ated. Example functions are found in ldaBag#’ , plsBag, nbBag, svmBag and
nnetBag.)
aggregate afunctionwithargumentsxandtype. Thefunctionthattakestheoutput#’of
thepredictfunctionandreducesthebaggedpredictionstoasingleprediction
per sample. #’ the type argument can be used to switch between predicting
classesorclassprobabilitiesfor#’classificationmodels. Examplefunctionsare
foundinldaBag,plsBag,nbBag,#’svmBagandnnetBag.
downSample logical: forclassification,shouldthedatasetberandomlysampledsothateach
#’classhasthesamenumberofsamplesasthesmallestclass?
oob logical: shouldout-of-bagstatisticsbecomputedandthepredictionsretained?
allowParallel aparallelbackendisloadedandavailable,shouldthefunctionuseit?
y avectorofoutcomes
B thenumberofbootstrapsamplestotrainover.
vars aninteger. IfthisargumentisnotNULL,arandomsampleofsizevarsistaken
ofthepredictorsineachbaggingiteration. IfNULL,allpredictorsareused.
bagControl alistofoptions.
object anobjectofclassbag.
newdata amatrixordataframeofsamplesforprediction. Notethatthisargumentmust
haveanon-nullvalue
digits minimalnumberofsignificantdigits.
Format
Anobjectofclasslistoflength3.
Anobjectofclasslistoflength3.
Anobjectofclasslistoflength3.
Anobjectofclasslistoflength3.
Anobjectofclasslistoflength3.
Anobjectofclasslistoflength3.
10 bag
Details
Thefunctionisbasicallyaframeworkwhereuserscanpluginanymodelintoassesstheeffectof
bagging. ExamplesfunctionscanbefoundinldaBag,plsBag,nbBag,svmBagandnnetBag. Each
haselementsfit,predandaggregate.
Onenote: whenvarsisnotNULL,thesub-settingoccurspriortothefitand#’predictfunctions
arecalled. Inthisway,theuserprobablydoesnotneedtoaccountforthe#’changeinpredictorsin
theirfunctions.
When using bag with train, classification models should use type="prob" #’ inside of the
predictfunctionsothatpredict.train(object,newdata,type="prob")will#’work.
Ifaparallelbackendisregistered,theforeachpackageisusedtotrainthemodelsinparallel.
Value
bagproducesanobjectofclassbagwithelements
fits a list with two sub-objects: the fit object has the actual model fit for that #’
baggedsamplesandthevarsobjectiseitherNULLoravectorofintegerscorre-
spondingtowhichpredictorsweresampledforthatmodel
control amirroroftheargumentspassedintobagControl
call thecall
B thenumberofbaggingiterations
dims thedimensionsofthetrainingset
Author(s)
MaxKuhn
Examples
## A simple example of bagging conditional inference regression trees:
data(BloodBrain)
## treebag <- bag(bbbDescr, logBBB, B = 10,
## bagControl = bagControl(fit = ctreeBag$fit,
## predict = ctreeBag$pred,
## aggregate = ctreeBag$aggregate))
## An example of pooling posterior probabilities to generate class predictions
data(mdrr)
## remove some zero variance predictors and linear dependencies
mdrrDescr <- mdrrDescr[, -nearZeroVar(mdrrDescr)]
mdrrDescr <- mdrrDescr[, -findCorrelation(cor(mdrrDescr), .95)]
## basicLDA <- train(mdrrDescr, mdrrClass, "lda")