critPlot {robustHD} | R Documentation |
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(object, ...)
## S3 method for class 'seqModel'
critPlot(object, which = c("line", "dot"), ...)
## S3 method for class 'tslars'
critPlot(object, p, which = c("line", "dot"), ...)
## S3 method for class 'sparseLTS'
critPlot(
object,
which = c("line", "dot"),
fit = c("reweighted", "raw", "both"),
...
)
## S3 method for class 'perrySeqModel'
critPlot(object, which = c("line", "dot", "box", "density"), ...)
## S3 method for class 'perrySparseLTS'
critPlot(
object,
which = c("line", "dot", "box", "density"),
fit = c("reweighted", "raw", "both"),
...
)
## S3 method for class 'setupCritPlot'
critPlot(object, ...)
Arguments
object |
the model fit to be plotted, , or an object containing
all necessary information for plotting (as generated by
|
... |
additional arguments to be passed down, eventually to
|
which |
a character string specifying the type of plot. Possible
values are |
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 |
Value
An object of class "ggplot"
(see ggplot
).
Note
Function perryPlot
is used to create the plot,
even if the optimality criterion does not correspond to resampling-based p
rediction error estimation. While this can be seen as as a misuse of its
functionality, it ensures that all optimality criteria are displayed in the
same way.
Author(s)
Andreas Alfons
See Also
ggplot
, perryPlot
,
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 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")