plot.seqModel {robustHD}

Plot a sequence of regression models

Description

Produce a plot of the coefficients, the values of the optimality criterion, or diagnostic plots 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

## S3 method for class 'seqModel'
plot(x, method = c("coefficients", "crit", "diagnostic"), ...)

## S3 method for class 'perrySeqModel'
plot(x, method = c("crit", "diagnostic"), ...)

## S3 method for class 'tslars'
plot(x, p, method = c("coefficients", "crit", "diagnostic"), ...)

## S3 method for class 'sparseLTS'
plot(x, method = c("coefficients", "crit", "diagnostic"), ...)

## S3 method for class 'perrySparseLTS'
plot(x, method = c("crit", "diagnostic"), ...)

Arguments

x

the model fit to be plotted.

method

a character string specifying the type of plot. Possible values are "coefficients" to plot the coefficients from the submodels via coefPlot (only for the "seqModel" and "sparseLTS" methods), "crit" to plot the values of the optimality criterion for the submodels via critPlot, or "diagnostic" for diagnostic plots via diagnosticPlot.

...

additional arguments to be passed down.

p

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

Value

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

Author(s)

Andreas Alfons

See Also

coefPlot, critPlot, diagnosticPlot, rlars, grplars, rgrplars, tslarsP, rtslarsP, tslars, rtslars, sparseLTS

Examples

## 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 plots
plot(fitRlars, method = "coef")
plot(fitRlars, method = "crit")
plot(fitRlars, method = "diagnostic")


## 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 plots
plot(fitSparseLTS, method = "coef")
plot(fitSparseLTS, method = "crit")
plot(fitSparseLTS, method = "diagnostic")