# Extract residuals from a sequence of regression models

### Description

Extract residuals from 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

1 2 3 4 5 6 7 8 9 10 | ```
## S3 method for class 'seqModel'
residuals(object, s = NA, standardized = FALSE,
drop = !is.null(s), ...)
## S3 method for class 'tslars'
residuals(object, p, ...)
## 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 |

`standardized` |
a logical indicating whether the residuals should be
standardized (the default is |

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

`p` |
an integer giving the lag length for which to extract residuals (the default is to use the optimal lag length). |

`fit` |
a character string specifying which residuals to extract.
Possible values are |

`...` |
for the |

### Value

A numeric vector or matrix containing the requested residuals.

### Author(s)

Andreas Alfons

### See Also

`residuals`

, `rlars`

,
`grplars`

, `rgrplars`

, `tslarsP`

,
`rtslarsP`

, `tslars`

, `rtslars`

,
`sparseLTS`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ```
## 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))
``` |