These functions are provided for compatibility with older versions only, and may be defunct as soon as the next release.
# S3 method for seqModel
fortify(model, data, s = NA, covArgs = list(...), ...)# S3 method for sparseLTS
fortify(
model,
data,
s = NA,
fit = c("reweighted", "raw", "both"),
covArgs = list(...),
...
)
# S3 method for default
diagnosticPlot(
object,
which = c("all", "rqq", "rindex", "rfit", "rdiag"),
ask = (which == "all"),
facets = attr(object, "facets"),
size = c(2, 4),
id.n = NULL,
...
)
The fortify methods return data frame containing the columns
listed below, as well as additional information stored in the attributes
"qqLine" (intercepts and slopes of the respective reference lines
to be displayed in residual Q-Q plots), "q" (quantiles of the
Mahalanobis distribution used as cutoff points for detecting leverage
points), and "facets" (default faceting formula for the diagnostic
plots).
stepthe steps (for the "seqModel" method) or indices
(for the "sparseLTS" method) of the models (only returned if more
than one model is requested).
fitthe model fits (only returned if both the reweighted
and raw fit are requested in the "sparseLTS" method).
indexthe indices of the observations.
fittedthe fitted values.
residualthe standardized residuals.
theoreticalthe corresponding theoretical quantiles from the standard normal distribution.
qqdthe absolute distances from a reference line through the first and third sample and theoretical quartiles.
rdthe robust Mahalanobis distances computed via the MCD
(see covMcd).
xydthe pairwise maxima of the absolute values of the standardized residuals and the robust Mahalanobis distances, divided by the respective other outlier detection cutoff point.
weightthe weights indicating regression outliers.
leveragelogicals indicating leverage points (i.e., outliers in the predictor space).
classificationa factor with levels "outlier"
(regression outliers) and "good" (data points following the model).
the model fit to be converted.
currently ignored.
for the "seqModel" method, an integer vector giving
the steps of the submodels to be converted (the default is to use the
optimal submodel). For the "sparseLTS" method, an integer vector
giving the indices of the models to be converted (the default is to use the
optimal model for each of the requested fits).
a list of arguments to be passed to
covMcd for computing robust Mahalanobis distances.
for the fortify methods, additional arguments to be
passed to covMcd can be specified directly instead
of via covArgs. For the default method of diagnosticPlot,
additional arguments to be passed down to geom_point.
a character string specifying which fit to convert. Possible
values are "reweighted" (the default) to convert the reweighted fit,
"raw" to convert the raw fit, or "both" to convert both fits.
a data frame containing all necessary information for
plotting (as generated by the fortify methods).
a character string indicating which plot to show. Possible
values are "all" (the default) for all of the following, "rqq"
for a normal Q-Q plot of the standardized residuals, "rindex" for a
plot of the standardized residuals versus their index, "rfit" for a
plot of the standardized residuals versus the fitted values, or
"rdiag" for a regression diagnostic plot (standardized residuals
versus robust Mahalanobis distances of the predictor variables).
a logical indicating whether the user should be asked before
each plot (see devAskNewPage). The default is to
ask if all plots are requested and not ask otherwise.
a faceting formula to override the default behavior. If
supplied, facet_wrap or
facet_grid is called depending on whether the formula
is one-sided or two-sided.
a numeric vector of length two giving the point and label size, respectively.
an integer giving the number of the most extreme observations to be identified by a label. The default is to use the number of identified outliers, which can be different for the different plots.
Andreas Alfons
The fortify methods supplement the fitted values and residuals of a
sequence of regression models (such as robust least angle regression models
or sparse least trimmed squares regression models) with other useful
information for diagnostic plots.
The default method of diagnosticPlot creates the corresponding plot
from the data frame returned by fortify.