robustHD (version 0.3.0)

residuals.seqModel: Extract residuals from a sequence of regression models

Description

Extract residuals from a sequence of regression models, such as submodels along a robust 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':
residuals(object, s = NA,
    standardized = FALSE, drop = !is.null(s), ...)

  ## S3 method for class 'sparseLTS':
residuals(object, s = NA,
    fit = c("reweighted", "raw", "both"),
    standardized = FALSE, drop = !is.null(s), ...)

Arguments

object

the model fit from which to extract residuals.

s

for the "seqModel" method, an integer vector giving the steps of the submodels for which to extract the residuals (the default is to use the optimal submodel). For the "sparseLTS" method, an integer vector giving the

fit

a character string specifying which residuals to extract. Possible values are "reweighted" (the default) for the residuals from the reweighted estimator, "raw" for the residuals from the raw estimator, or "both"

standardized

a logical indicating whether the residuals should be standardized (the default is FALSE).

drop

a logical indicating whether to reduce the dimension to a vector in case of only one step.

...

additional arguments are currently ignored.

Value

• A numeric vector or matrix containing the requested residuals.

See Also

residuals, rlars, 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)
# extract residuals
residuals(fitRlars)
head(residuals(fitRlars, s = 1:5))


## 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")
# extract residuals
residuals(fitSparseLTS)
head(residuals(fitSparseLTS, fit = "both"))
head(residuals(fitSparseLTS, s = NULL))
head(residuals(fitSparseLTS, fit = "both", s = NULL))