<!doctype s-function-doc system "s-function-doc.dtd" [
<!entity % S-OLD "INCLUDE">
]
>
<s-function-doc>
<s-topics>
<s-topic> plot.lmfm </s-topic>
</s-topics>
<s-title>
Comparison Plots for Linear Regression Models
</s-title>
<s-description>
Creates a set plots useful for comparing fitted linear models stored in a
class fit.models object belonging to virtual class lmfm.  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.
</s-description>
<s-usage>
<s-old-style-usage>
plot.lmfm(x, which.plots="ask", chisq.percent=0.99, vertical.outlier=.99,
smooths=F, rugplot=F, id.n=3, envelope=T, half.normal=F, robustQQline=T,
mc.samples=100, level=0.95, seed=289, cutoff=T, ...)
</s-old-style-usage>
</s-usage>
<s-args-required>
<s-arg name=" x ">
a fit.models object belonging to virtual class lmfm.
</s-arg>
</s-args-required>
<s-args-optional>
<s-arg name=" which.plots ">
either "ask", "all", or an integer vector specifying which plots to
draw.  If <s-expression>which.plots</s-expression> is an integer vector,
use the plot numbers given in the description above (or in the "ask" menu).
</s-arg>
<s-arg name=" chisq.percent ">
p-value used to calculate the chi-squared quantile used as the outlier
threshold for robust distances.
</s-arg>
<s-arg name=" vertical.outlier ">
p-value used to calculate the standard normal quantile used as the outlier
threshold for residuals.
</s-arg>
<s-arg name=" smooths ">
if TRUE, smooth curves are approximated to the scatterplots using
loess.smooth and added to the appropriate plots.
</s-arg>
<s-arg name=" 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.
</s-arg>
<s-arg name=" id.n ">
number of outliers identified in plots.
</s-arg>
<s-arg name=" envelope ">
if TRUE, a simulation envelope is added to the QQ-plot.
</s-arg>
<s-arg name=" half.normal ">
if TRUE, half normal QQ-plots will be used.
</s-arg>
<s-arg name=" robustQQline ">
if TRUE a robust fit is added to the QQ-plot.
</s-arg>
<s-arg name=" mc.samples ">
number of samples used in computing the simulation envelope.
</s-arg>
<s-arg name=" level ">
confidence level for the simulation envelope.
</s-arg>
<s-arg name=" seed ">
an integer between 0 and 1023.  The seed value used for random number
generation in the QQ-plot simulation envelope.
</s-arg>
<s-arg name=" cutoff ">
if TRUE, bounds are added to the one variable regression plot.
Observations outside these bounds received zero weight in the analysis.
</s-arg>
</s-args-optional>
<s-value>
x is invislbly returned.
</s-value>
<s-side-effects>
The selected plots are drawn on a graphics device.
</s-side-effects>
<s-details>
If all the linear models in the fit.models object have exactly one
explanatory variable then there is the additional option to plot the fit
over a scatter plot of the data.
</s-details>
<s-section name = "REFERENCES">
Atkinson, A. C. (1985).  Plots, Transformations and Regression. New York:
Oxford University Press.
</s-section>
<s-see>
<s-function name="plot.sgm">plot</s-function>,
<s-function name="fit.models1.sgm">fit.models</s-function>.
</s-see>
<s-examples>
<s-example>
stack.lm <- lm(Loss ~ ., data=stack.dat)
stack.lmRob <- lmRob(Loss ~ ., data=stack.dat)
stack.fm <- fit.models(stack.lmRob, stack.lm)
#
# stack.fm is a fit.models object with virtual class lmfm.
#
plot(stack.fm)
</s-example>
</s-examples>
<s-keywords>
<s-keyword>librobust</s-keyword>
<s-keyword>hplot</s-keyword>
</s-keywords>
<s-docclass>
function
</s-docclass>
</s-function-doc>
