robustHD (version 0.6.1)

critPlot: Optimality criterion plot of a sequence of regression models

Description

Produce a plot of the values of the optimality criterion for a sequence of regression models, such as submodels along a robust or groupwise least angle regression sequence, or sparse least trimmed squares regression models for a grid of values for the penalty parameter.

Usage

critPlot(x, ...)
# S3 method for seqModel
critPlot(x, size = c(0.5, 2), ...)
# S3 method for perrySeqModel
critPlot(x, ...)
# S3 method for tslars
critPlot(x, p, ...)
# S3 method for sparseLTS
critPlot(x, fit = c("reweighted", "raw", "both"),
  size = c(0.5, 2), ...)
# S3 method for perrySparseLTS
critPlot(x, fit = c("reweighted", "raw",
  "both"), ...)

Arguments

x

the model fit to be plotted.

…

for the generic function, additional arguments to be passed down to methods. For the "tslars" method, additional arguments to be passed down to the "seqModel" method. For the "seqModel" and "sparseLTS" methods, additional arguments to be passed down to geom_line and geom_point. For the "perrySeqModel" and "perrySparseLTS" methods, additional arguments to be passed down to the plot method for the prediction error results.

size

a numeric vector of length two giving the line width and the point size, respectively.

p

an integer giving the lag length for which to produce the plot (the default is to use the optimal lag length).

fit

a character string specifying for which estimator to produce the plot. Possible values are "reweighted" (the default) for the reweighted fits, "raw" for the raw fits, or "both" for both estimators.

Value

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

See Also

ggplot, perryPlot, rlars, grplars, rgrplars, tslarsP, rtslarsP, tslars, rtslars, sparseLTS

Examples

# NOT RUN {
## generate data
# example is not high-dimensional to keep computation time low
library("mvtnorm")
set.seed(1234)  # for reproducibility
n <- 100  # number of observations
p <- 25   # number of variables
beta <- rep.int(c(1, 0), c(5, p-5))  # coefficients
sigma <- 0.5      # controls signal-to-noise ratio
epsilon <- 0.1    # contamination level
Sigma <- 0.5^t(sapply(1:p, function(i, j) abs(i-j), 1:p))
x <- rmvnorm(n, sigma=Sigma)    # predictor matrix
e <- rnorm(n)                   # error terms
i <- 1:ceiling(epsilon*n)       # observations to be contaminated
e[i] <- e[i] + 5                # vertical outliers
y <- c(x %*% beta + sigma * e)  # response
x[i,] <- x[i,] + 5              # bad leverage points


## robust LARS
# fit model
fitRlars <- rlars(x, y, sMax = 10)
# create plot
critPlot(fitRlars)


## sparse LTS over a grid of values for lambda
# fit model
frac <- seq(0.2, 0.05, by = -0.05)
fitSparseLTS <- sparseLTS(x, y, lambda = frac, mode = "fraction")
# create plot
critPlot(fitSparseLTS)
critPlot(fitSparseLTS, fit = "both")
# }

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