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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
## 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"), ...)
``` |

`x` |
the model fit to be plotted. |

`method` |
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). |

`...` |
additional arguments to be passed down. |

An object of class `"ggplot"`

(see `ggplot`

).

Andreas Alfons

`coefPlot`

, `critPlot`

,
`diagnosticPlot`

, `rlars`

, `grplars`

,
`rgrplars`

, `tslarsP`

, `rtslarsP`

,
`tslars`

, `rtslars`

, `sparseLTS`

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)
# 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")
``` |

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