## 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)
Run the code above in your browser using DataLab