Diagnostic Plots for Robustly Fitted Linear Models
DESCRIPTION:
Creates a set of plots suitable for assessing a robustly fitted linear
model of class lmRob. The plot options are (2) Normal QQ-Plot of
Residuals, (3) Estimated Kernel Density of Residuals, (4) Robust Residuals
vs Robust Distances, (5) Residuals vs Fitted Values, (6) Sqrt of
abs(Residuals) vs Fitted Values, (7) Response vs Fitted Values, (8)
Standardized Residuals vs Index (Time), (9) Overlaid Normal QQ-Plot of
Residuals, and (10) Overlaid Estimated Density of Residuals.
either "ask", "all", or an integer vector specifying which plots to
draw. If
which.plots is an integer vector,
use the plot numbers given in the description above (or in the "ask" menu).
chisq.percent
p-value used to calculate the chi-squared quantile used as the outlier
threshold for robust distances.
vertical.outlier
p-value used to calculate the standard normal quantile used as the outlier
threshold for residuals.
smooths
if TRUE, smooth curves are approximated to the scatterplots using
loess.smooth and added to the appropriate plots.
rugplot
if TRUE, a univariate histogram or rugplot is displayed along the base of
each plot, showing the occurrence of each x-value; ties are broken by
jittering.
id.n
number of outliers identified in plots.
envelope
if TRUE, a simulation envelope is added to the QQ-plot.
half.normal
if TRUE, half normal QQ-plots will be used.
robustQQline
if TRUE a robust fit is added to the QQ-plot.
mc.samples
number of samples used in computing the simulation envelope.
level
confidence level for the simulation envelope.
seed
an integer between 0 and 1023. The seed value used for random number
generation in the QQ-plot simulation envelope.
cutoff
if TRUE, bounds are added to the one variable regression plot.
Observations outside these bounds received zero weight in the analysis.
SIDE EFFECTS:
The selected plots are drawn on a graphics device.
DETAILS:
If the model has exactly one explanatory variable then there is the
additional option to plot the fit over a scatter plot of the data.
The lmRob object is coerced to an lmfm object containing a single
model. The plotting is then accomplished by calling plot.lmfm.