Description Usage Arguments Details Value Author(s) Examples
Interfaces to caret
functions that can be used
in a pipeline implemented by magrittr
.
1 2 3 4 5 6 7 8 9 10 11 | ntbt_avNNet(data, ...)
ntbt_bagEarth(data, ...)
ntbt_bagFDA(data, ...)
ntbt_calibration(data, ...)
ntbt_dummyVars(data, ...)
ntbt_icr(data, ...)
ntbt_knn3(data, ...)
ntbt_lift(data, ...)
ntbt_pcaNNet(data, ...)
ntbt_sbf(data, ...)
ntbt_train(data, ...)
|
data |
data frame, tibble, list, ... |
... |
Other arguments passed to the corresponding interfaced function. |
Interfaces call their corresponding interfaced function.
Object returned by interfaced function.
Roberto Bertolusso
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 | ## Not run:
library(intubate)
library(magrittr)
library(caret)
## ntbt_avNNet: Neural Networks Using Model Averaging
## Not found example using formula interface, and I am
## completely ignorant to construct one.
data(BloodBrain)
BB <- list(bbbDescr, logBBB)
## Original function to interface
avNNet(bbbDescr, logBBB, size = 5, linout = TRUE, trace = FALSE)
## The interface puts data as first parameter
ntbt_avNNet(BB, bbbDescr, logBBB, size = 5, linout = TRUE, trace = FALSE)
## so it can be used easily in a pipeline.
BB %>%
ntbt_avNNet(bbbDescr, logBBB, size = 5, linout = TRUE, trace = FALSE)
## ntbt_bagEarth: Bagged Earth
## Original function to interface
bagEarth(Volume ~ ., data = trees)
## The interface puts data as first parameter
ntbt_bagEarth(trees, Volume ~ .)
## so it can be used easily in a pipeline.
trees %>%
ntbt_bagEarth(Volume ~ .)
## ntbt_bagFDA: Bagged FDA
library(mlbench)
library(earth)
data(Glass)
set.seed(36)
inTrain <- sample(1:dim(Glass)[1], 150)
trainData <- Glass[ inTrain, ]
testData <- Glass[-inTrain, ]
## Original function to interface
## bagFDA(Type ~ ., trainData) ## There is an error:
## Error in requireNamespaceQuietStop("mda") : package mda is required
## ## even when mda is installed
## For now all of this stays commented.
## The interface puts data as first parameter
## ntbt_bagFDA(trainData, Type ~ .)
## so it can be used easily in a pipeline.
## trainData %>%
## ntbt_bagFDA(Type ~ .)
## ntbt_calibration: Probability Calibration Plot
data(mdrr)
mdrrDescr <- mdrrDescr[, -nearZeroVar(mdrrDescr)]
mdrrDescr <- mdrrDescr[, -findCorrelation(cor(mdrrDescr), .5)]
inTrain <- createDataPartition(mdrrClass)
trainX <- mdrrDescr[inTrain[[1]], ]
trainY <- mdrrClass[inTrain[[1]]]
testX <- mdrrDescr[-inTrain[[1]], ]
testY <- mdrrClass[-inTrain[[1]]]
library(MASS)
ldaFit <- lda(trainX, trainY)
qdaFit <- qda(trainX, trainY)
testProbs <- data.frame(obs = testY,
lda <- predict(ldaFit, testX)$posterior[,1],
qda <- predict(qdaFit, testX)$posterior[,1])
## Original function to interface
calPlotData <- calibration(obs ~ lda + qda, data = testProbs)
xyplot(calPlotData, auto.key = list(columns = 2))
## The interface puts data as first parameter
calPlotData <- ntbt_calibration(testProbs, obs ~ lda + qda)
xyplot(calPlotData, auto.key = list(columns = 2))
## so it can be used easily in a pipeline.
testProbs %>%
ntbt_calibration(obs ~ lda + qda) %>%
xyplot(auto.key = list(columns = 2))
## ntbt_dummyVars
when <- data.frame(time = c("afternoon", "night", "afternoon",
"morning", "morning", "morning",
"morning", "afternoon", "afternoon"),
day = c("Mon", "Mon", "Mon",
"Wed", "Wed", "Fri",
"Sat", "Sat", "Fri"))
levels(when$time) <- list(morning="morning",
afternoon="afternoon",
night="night")
levels(when$day) <- list(Mon="Mon", Tue="Tue", Wed="Wed", Thu="Thu",
Fri="Fri", Sat="Sat", Sun="Sun")
## Original function to interface
mainEffects <- dummyVars(~ day + time, data = when)
mainEffects
predict(mainEffects, when[1:3,])
## The interface puts data as first parameter
mainEffects <- ntbt_dummyVars(when, ~ day + time)
mainEffects
predict(mainEffects, when[1:3,])
## so it can be used easily in a pipeline.
when %>%
ntbt_dummyVars(~ day + time) %>%
predict(when[1:3,])
## ntbt_icr: Independent Component Regression
## Not found example using formula interface, and I am
## completely ignorant to construct one.
data(BloodBrain)
BB <- list(bbbDescr, logBBB)
## Original function to interface
icr(bbbDescr, logBBB, n.comp = 5)
## The interface puts data as first parameter
ntbt_icr(BB, bbbDescr, logBBB, n.comp = 5)
## so it can be used easily in a pipeline.
BB %>%
ntbt_icr(bbbDescr, logBBB, n.comp = 5)
## ntbt_knn3: k-Nearest Neighbour Classification
## Original function to interface
knn3(Species ~ ., iris)
## The interface puts data as first parameter
ntbt_knn3(iris, Species ~ .)
## so it can be used easily in a pipeline.
iris %>%
ntbt_knn3(Species ~ .)
## ntbt_lift: Lift Plot
set.seed(1)
simulated <- data.frame(obs = factor(rep(letters[1:2], each = 100)),
perfect = sort(runif(200), decreasing = TRUE),
random = runif(200))
## Original function to interface
lift1 <- lift(obs ~ random, data = simulated)
lift1
xyplot(lift1)
## The interface puts data as first parameter
lift1 <- ntbt_lift(simulated, obs ~ random)
lift1
xyplot(lift1)
## so it can be used easily in a pipeline.
simulated %>%
ntbt_lift(obs ~ random) %>%
xyplot()
## ntbt_pcaNNet: Neural Networks with a Principal Component Step
## Not found example using formula interface, and I am
## completely ignorant to construct one.
data(BloodBrain)
BB <- list(bbbDescr, logBBB)
## Original function to interface
pcaNNet(bbbDescr[, 1:10], logBBB, size = 5, linout = TRUE, trace = FALSE)
## The interface puts data as first parameter
ntbt_pcaNNet(BB, bbbDescr[, 1:10], logBBB, size = 5, linout = TRUE, trace = FALSE)
## so it can be used easily in a pipeline.
BB %>%
ntbt_pcaNNet(bbbDescr[, 1:10], logBBB, size = 5, linout = TRUE, trace = FALSE)
## ntbt_sbf: Selection By Filtering (SBF)
## Not found example using formula interface, and I am
## completely ignorant to construct one.
data(BloodBrain)
BB <- list(bbbDescr, logBBB)
## Be prepared to wait...
## Original function to interface
sbf(bbbDescr, logBBB,
sbfControl = sbfControl(functions = rfSBF,
verbose = FALSE,
method = "cv"))
## The interface puts data as first parameter
ntbt_sbf(BB, bbbDescr, logBBB,
sbfControl = sbfControl(functions = rfSBF,
verbose = FALSE,
method = "cv"))
## so it can be used easily in a pipeline.
BB %>%
ntbt_sbf(bbbDescr, logBBB,
sbfControl = sbfControl(functions = rfSBF,
verbose = FALSE,
method = "cv"))
## ntbt_train: Fit Predictive Models over Different Tuning Parameters
library(mlbench)
data(BostonHousing)
## Original function to interface
train(medv ~ . + rm:lstat, data = BostonHousing, method = "lm")
## The interface puts data as first parameter
ntbt_train(BostonHousing, medv ~ . + rm:lstat, method = "lm")
## so it can be used easily in a pipeline.
BostonHousing %>%
ntbt_train(medv ~ . + rm:lstat, method = "lm")
## End(Not run)
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