Description Usage Arguments Value Author(s) See Also Examples

View source: R/setupCritPlot.R

Extract the relevent information for 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.

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 | ```
setupCritPlot(object, ...)
## S3 method for class 'seqModel'
setupCritPlot(object, which = c("line", "dot"), ...)
## S3 method for class 'tslars'
setupCritPlot(object, p, ...)
## S3 method for class 'sparseLTS'
setupCritPlot(
object,
which = c("line", "dot"),
fit = c("reweighted", "raw", "both"),
...
)
## S3 method for class 'perrySeqModel'
setupCritPlot(object, which = c("line", "dot", "box", "density"), ...)
## S3 method for class 'perrySparseLTS'
setupCritPlot(
object,
which = c("line", "dot", "box", "density"),
fit = c("reweighted", "raw", "both"),
...
)
``` |

`object` |
the model fit from which to extract information. |

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

`which` |
a character string specifying the type of plot. Possible
values are |

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

`fit` |
a character string specifying for which estimator to extract
information. Possible values are |

An object inheriting from class `"setupCritPlot"`

with the
following components:

`data`

a data frame containing the following columns:

`Fit`

a vector or factor containing the identifiers of the models along the sequence.

`Name`

a factor specifying the estimator for which the optimality criterion was estimated (

`"reweighted"`

or`"raw"`

; only returned if both are requested in the`"sparseLTS"`

or`"perrySparseLTS"`

methods).`PE`

the estimated prediction errors (only returned if applicable).

`BIC`

the estimated values of the Bayesian information criterion (only returned if applicable).

`Lower`

the lower end points of the error bars (only returned if possible to compute).

`Upper`

the upper end points of the error bars (only returned if possible to compute).

`which`

a character string specifying the type of plot.

`grouped`

a logical indicating whether density plots should be grouped due to multiple model fits along the sequence (only returned in case of density plots for the

`"perrySeqModel"`

and`"perrySparseLTS"`

methods).`includeSE`

a logical indicating whether error bars based on standard errors are available (only returned in case of line plots or dot plots).

`mapping`

default aesthetic mapping for the plots.

`facets`

default faceting formula for the plots (only returned if both estimators are requested in the

`"sparseLTS"`

or`"perrySparseLTS"`

methods).`tuning`

a data frame containing the grid of tuning parameter values for which the optimality criterion was estimated (only returned for the

`"sparseLTS"`

and`"perrySparseLTS"`

methods).

Andreas Alfons

`critPlot`

, `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)
# extract information for plotting
setup <- setupCritPlot(fitRlars)
critPlot(setup)
## 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 information for plotting
setup1 <- setupCritPlot(fitSparseLTS)
critPlot(setup1)
setup2 <- setupCritPlot(fitSparseLTS, fit = "both")
critPlot(setup2)
``` |

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