Extract 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) and other useful information for diagnostic plots.
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setupDiagnosticPlot(object, ...) ## S3 method for class 'seqModel' setupDiagnosticPlot(object, s = NA, covArgs = list(...), ...) ## S3 method for class 'perrySeqModel' setupDiagnosticPlot(object, ...) ## S3 method for class 'tslars' setupDiagnosticPlot(object, p, ...) ## S3 method for class 'sparseLTS' setupDiagnosticPlot( object, s = NA, fit = c("reweighted", "raw", "both"), covArgs = list(...), ... ) ## S3 method for class 'perrySparseLTS' setupDiagnosticPlot(object, ...)
the model fit from which to extract information.
additional arguments to be passed to
a list of arguments to be passed to
an integer giving the lag length for which to extract information (the default is to use the optimal lag length).
a character string specifying from which fit to extract
information. Possible values are
An object of class
"setupDiagnosticPlot" with the following
a data frame containing the columns listed below.
the steps (for the
"seqModel" method) or
indices (for the
"sparseLTS" method) of the models (only returned
if more than one model is requested).
the model fits (only returned if both the reweighted
and raw fit are requested in the
the indices of the observations.
the fitted values.
the standardized residuals.
the corresponding theoretical quantiles from the standard normal distribution.
the absolute distances from a reference line through the first and third sample and theoretical quartiles.
the robust Mahalanobis distances computed via the MCD
the pairwise maxima of the absolute values of the standardized residuals and the robust Mahalanobis distances, divided by the respective other outlier detection cutoff point.
the weights indicating regression outliers.
logicals indicating leverage points (i.e., outliers in the predictor space).
a factor with levels
(potential regression outliers) and
"Regular observation" (data
points following the model).
a data frame containing the intercepts and slopes of the respective reference lines to be displayed in residual Q-Q plots.
a data frame containing the quantiles of the Mahalanobis distribution used as cutoff points for detecting leverage points.
default faceting formula for the diagnostic plots (only returned where applicable).
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## 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 <- setupDiagnosticPlot(fitRlars) diagnosticPlot(setup) ## sparse LTS # fit model fitSparseLTS <- sparseLTS(x, y, lambda = 0.05, mode = "fraction") # extract information for plotting setup1 <- setupDiagnosticPlot(fitSparseLTS) diagnosticPlot(setup1) setup2 <- setupDiagnosticPlot(fitSparseLTS, fit = "both") diagnosticPlot(setup2)
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