<!doctype s-function-doc system "s-function-doc.dtd" [
<!entity % S-OLD "INCLUDE">
]
>
<s-function-doc>
<s-topics>
<s-topic> plot.asymfm </s-topic>
</s-topics>
<s-title>
Comparison Plots for Fitted Asymmetric Distributions
</s-title>
<s-description>
Creates a set of plots useful for comparing fitted asymmetric models stored
in a class fit.models object belonging to virtual class asymfm. The plot
options are (2) overlaid density estimates, and (3) response vs estimated
quantiles.
</s-description>
<s-usage>
<s-old-style-usage>
plot.asymfm(x, truncate=T, which.plots="ask", lower.q=0.999,
            plot.data=T, robustQQline=T, ...) 
</s-old-style-usage>
</s-usage>
<s-args-required>
<s-arg name=" x ">
a fit.models object belonging to virtual class asymfm.
</s-arg>
</s-args-required>
<s-args-optional>
<s-arg name=" truncate ">
if TRUE, xlim is set to c(0, h) where h is the maximum of the
<s-expression>lower.q</s-expression> quantiles of the fitted
distributions.
</s-arg>
<s-arg name=" which.plots ">
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).
</s-arg>
<s-arg name=" lower.q ">
the p-value used to calculate the quantile for truncation (see
<s-expression>truncate</s-expression>).
</s-arg>
<s-arg name=" plot.data ">
if TRUE and the data is available (either in the object or on the search
path), the density estimates are plotted over a histogram of the data.
</s-arg>
<s-arg name=" robustQQline ">
if TRUE, a robust linear fit is added to the Response vs Estimated
Quantiles plot.
</s-arg>
</s-args-optional>
<s-value>
x is invisibly returned.
</s-value>
<s-side-effects>
The selected plots are drawn on a graphics device.
</s-side-effects>
<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>
Robust <- gammaRob(los)
MLE <- gammaMLE(los)
# los.fm is a fit.models object with virtual class asymfm:
los.fm <- fit.models(Robust, MLE)
plot(los.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>
