Nothing
densityplot.train <- function(x,
data = NULL,
metric = x$metric,
...)
{
if (!is.null(match.call()$data))
warning("explicit 'data' specification ignored")
if(x$control$method %in% c("oob", "LOOCV"))
stop("Resampling plots cannot be done with leave-out-out CV or out-of-bag resampling")
resamp <- x$resample
tNames <- gsub("^\\.", "", names(x$bestTune))
# adapt formula to work with muliple metrics
mName <- names(resamp)[names(resamp) %in% metric][1]
# Look for constant tuning parameters and remove them
numVals <- unlist(
lapply(resamp,
function(u) length(unique(u))))
if(any(numVals == 1))
{
# make sure that these are tuning parameters
resamp <- resamp[, numVals > 1, drop = FALSE]
tNames <- tNames[tNames %in% names(numVals)[numVals > 1]]
}
# Create the formula based on the data
formText <- paste("~", mName)
if(any(tNames %in% colnames(resamp)))
{
formText <- paste(formText,
"|",
paste(
tNames,
collapse = "*"))
}
form <- as.formula(formText)
densityplot(form, data = resamp, ...)
}
histogram.train <- function(x,
data = NULL,
metric = x$metric,
...)
{
if (!is.null(match.call()$data))
warning("explicit 'data' specification ignored")
if(x$control$method %in% c("oob", "LOOCV"))
stop("Resampling plots cannot be done with leave-out-out CV or out-of-bag resampling")
resamp <- x$resample
tNames <- gsub("^\\.", "", names(x$bestTune))
# adapt formula to work with muliple metrics
mName <- names(resamp)[names(resamp) %in% metric][1]
# Look for constant tuning parameters and remove them
numVals <- unlist(
lapply(resamp,
function(u) length(unique(u))))
if(any(numVals == 1))
{
# make sure that these are tuning parameters
resamp <- resamp[, numVals > 1, drop = FALSE]
tNames <- tNames[tNames %in% names(numVals)[numVals > 1]]
}
# Create the formula based on the data
formText <- paste("~", mName)
if(any(tNames %in% colnames(resamp)))
{
formText <- paste(formText,
"|",
paste(
tNames,
collapse = "*"))
}
form <- as.formula(formText)
histogram(form, data = resamp, ...)
}
stripplot.train <- function(x,
data = NULL,
metric = x$metric,
...)
{
if (!is.null(match.call()$data))
warning("explicit 'data' specification ignored")
if(x$control$method %in% c("oob", "LOOCV"))
stop("Resampling plots cannot be done with leave-out-out CV or out-of-bag resampling")
resamp <- x$resample
tNames <- gsub("^\\.", "", names(x$bestTune))
# adapt formula to work with muliple metrics
mName <- names(resamp)[names(resamp) %in% metric][1]
# Look for constant tuning parameters and remove them
numVals <- unlist(
lapply(resamp,
function(u) length(unique(u))))
if(any(numVals == 1))
{
# make sure that these are tuning parameters
resamp <- resamp[, numVals > 1, drop = FALSE]
tNames <- tNames[tNames %in% names(numVals)[numVals > 1]]
}
# determine which tuning parameter has the most values
tNames1 <- names(which.max(numVals[names(numVals) %in% tNames]))
tNames2 <- tNames[!(tNames %in% tNames1)]
# The variable in tNames1 will be converted to a factor, so
# we will make sure that numeric data gets changed correctly
resamp[,tNames1] <- factor(
as.character(resamp[,tNames1]),
levels = paste(
sort(unique(resamp[,tNames1]))))
# Create the formula based on the data
if(any(tNames %in% colnames(resamp)))
{
theDots <- list(...)
if(any(names(theDots) == "horizontal"))
{
formText <- if(theDots$horizontal) paste(tNames1, "~", mName) else paste(mName, "~", tNames1)
} else formText <- paste(tNames1, "~", mName)
if(length(tNames2) > 0)
{
formText <- paste(formText,
"|",
paste(
tNames2,
collapse = "*"))
}
} else formText <- paste("~", mName)
form <- as.formula(formText)
stripplot(form, data = resamp, ...)
}
xyplot.train <- function(x,
data = NULL,
metric = x$metric,
...)
{
if (!is.null(match.call()$data))
warning("explicit 'data' specification ignored")
if(x$control$method %in% c("oob", "LOOCV"))
stop("Resampling plots cannot be done with leave-out-out CV or out-of-bag resampling")
resamp <- x$resample
tNames <- gsub("^\\.", "", names(x$bestTune))
# adapt formula to work with muliple metrics
mName <- names(resamp)[names(resamp) %in% metric][1]
# Look for constant tuning parameters and remove them
numVals <- unlist(
lapply(resamp,
function(u) length(unique(u))))
if(any(numVals == 1))
{
# make sure that these are tuning parameters
resamp <- resamp[, numVals > 1, drop = FALSE]
tNames <- tNames[tNames %in% names(numVals)[numVals > 1]]
}
# determine which tuning parameter has the most values
tNames1 <- names(which.max(numVals[names(numVals) %in% tNames]))
tNames2 <- tNames[!(tNames %in% tNames1)]
# Create the formula based on the data
if(any(tNames %in% colnames(resamp)))
{
formText <- paste(mName, "~", tNames1)
if(length(tNames2) > 0)
{
formText <- paste(formText,
"|",
paste(
tNames2,
collapse = "*"))
}
} else stop("there must be at least one tuning parameter for a scatter plot")
form <- as.formula(formText)
xyplot(form, data = resamp, ...)
}
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