perry (version 0.2.0)

perryPlot: Plot resampling-based prediction error results

Description

Plot results of resampling-based prediction error measures.

Usage

perryPlot(x, ...)
  # S3 method for perry
perryPlot (x,
    method = c("box", "density", "dot"), select = NULL,
    seFactor = NA, ...)
  # S3 method for perrySelect
perryPlot (x,
    method = c("box", "density", "dot", "line"),
    subset = NULL, select = NULL, seFactor = x$seFactor,
    ...)
  # S3 method for default
perryPlot (x,
    method = c("box", "density", "dot", "line"), mapping,
    facets = attr(x, "facets"), ...)
  # S3 method for perry
autoplot (object, ...)
  # S3 method for perrySelect
autoplot (object, ...)
  # S3 method for perry
plot (x, ...)
  # S3 method for perrySelect
plot (x, ...)

Arguments

x

an object inheriting from class "perry" or "perrySelect" that contains prediction error results, or a data frame containing all necessary information for plotting (as generated by the corresponding fortify method).

object

an object inheriting from class "perry" or "perrySelect".

method

a character string specifying the type of plot. Possible values are "box" to create a box plot, "density" to create a smooth density plot, "dot" to create a dot plot, or "line" to plot the (average) results for each model as a connected line (for objects inheriting from class "perrySelect"). Note that the first two plots are only meaningful in case of repeated resampling. The default is to use "box" in case of repeated resampling and "dot" otherwise. In any case, partial string matching allows supply abbreviations of the accepted values.

subset

a character, integer or logical vector indicating the subset of models for which to plot the prediction error results.

select

a character, integer or logical vector indicating the columns of prediction error results to be plotted.

seFactor

a numeric value giving the multiplication factor of the standard error for displaying error bars in dot plots or line plots. Error bars in those plots can be suppressed by setting this to NA.

mapping

an aesthetic mapping to override the default behavior (see aes or aes_string)

facets

a faceting formula to override the default behavior. If supplied, facet_wrap or facet_grid is called depending on whether the formula is one-sided or two-sided.

…

for the generic function perryPlot, additional arguments to be passed down to methods. For the "perry" and "perrySelect" methods of perryPlot, additional arguments to be passed down to the default method. For the default method of perryPlot, additional arguments to be passed down to geom_boxplot, geom_density, geom_pointrange or geom_line. For the methods of plot, additional arguments to be passed down to perryPlot.

Value

An object of class "ggplot" (see ggplot).

Details

For objects with multiple columns of prediction error results, conditional plots are produced.

See Also

perryFit, perrySelect, perryTuning, ggplot, autoplot, plot

Examples

# NOT RUN {
data("coleman")
set.seed(1234)  # set seed for reproducibility

## set up folds for cross-validation
folds <- cvFolds(nrow(coleman), K = 5, R = 10)

## compare LS, MM and LTS regression

# perform cross-validation for an LS regression model
fitLm <- lm(Y ~ ., data = coleman)
cvLm <- perry(fitLm, splits = folds, 
    cost = rtmspe, trim = 0.1)

# perform cross-validation for an MM regression model
fitLmrob <- lmrob(Y ~ ., data = coleman, k.max = 500)
cvLmrob <- perry(fitLmrob, splits = folds, 
    cost = rtmspe, trim = 0.1)

# perform cross-validation for an LTS regression model
fitLts <- ltsReg(Y ~ ., data = coleman)
cvLts <- perry(fitLts, splits = folds, 
    cost = rtmspe, trim = 0.1)

# combine results into one object
cv <- perrySelect(LS = cvLm, MM = cvLmrob, LTS = cvLts)
cv

# plot results for the MM regression model
plot(cvLmrob, method = "box")
plot(cvLmrob, method = "density")
plot(cvLmrob, method = "dot", seFactor = 1)

# plot combined results
plot(cv, method = "box")
plot(cv, method = "density")
plot(cv, method = "dot", seFactor = 1)
# }