{smcl}
{* 3nov2004}{...}
{hline}
help for {hi:regplot}
{hline}
{title:Plots of regress or similar fit}
{p 8 17 2}{cmd:regplot}
[{cmd:,}
{cmdab:plottype(}{it:plottype}{cmd:)}
{cmdab:fit:opts(}{it:graph_options}{cmd:)}
{cmdab:sep:arate(}{it:varname}{cmd:)}
{it:scatter_options}
{cmd:plot(}{it:plot}{cmd:)}
]
{p 8 17 2}{cmd:regplot}
{it:varname}
[{cmd:,}
{cmdab:plottype(}{it:plottype}{cmd:)}
{cmdab:fit:opts(}{it:graph_options}{cmd:)}
{cmdab:sep:arate(}{it:varname}{cmd:)}
{it:scatter_options}
{cmd:plot(}{it:plot}{cmd:)}
]
{title:Description}
{p 4 4 2}{cmd:regplot} plots fitted or predicted values from an immediately
previous {cmd:regress} or similar command. By default the data for the response
are also plotted.
{title:Remarks}
{p 4 4 2}With the first syntax, no {it:varname} is specified. {cmd:regplot}
shows the response and predicted values on the {it:y} axis and the predictor
named first in the {cmd:regress} or similar command on the {it:x} axis. Thus
with this syntax the plot shown is sensitive to the order in which predictors
are specified in the estimation command.
{p 4 4 2}With the second syntax, a {it:varname} is supplied, which may name any
numeric variable. This is used as the variable on the {it:x} axis. This permits
changing graphs without reissuing the estimation command.
{p 4 4 2}Thus in practice {cmd:regplot} is most useful when the fitted values
are a smooth function of the variable shown on the {it:x} axis, or a set of
such functions given also one or more dummy variables as predictors. However,
other applications also arise, such as plotting observed and predicted values
from a time series model versus time.
{p 4 4 2}By default, {cmd:regplot} shows the fitted values using
{help:twoway mspline}. The {cmd:plottype()} option may be used to specify
another {help twoway} plottype.
{p 4 4 2}In more technical detail: {cmd:regplot} plots
{p 4 4 2}1. both a single dependent or response variable as specified in
{cmd:e(depvar)} and whatever single variable is calculated by the default of
{cmd:predict} on the {it:y} axis, which makes sense whenever those variables are on
the same scale;
{p 4 4 2}2. either the {it:varname} specified or what names the first column of
{cmd:e(b)} on the {it:x} axis, which makes sense whenever {cmd:graph} can
understand that as specifying the {it:x} axis.
{p 4 4 2}If your estimation results do not meet these specifications, you are likely
to get either bizarre results or an error message. Note in particular
that {it:varname} must be specified after {cmd:nl}.
{p 4 4 2}Time series operators are allowed.
{p 4 4 2}The plot is restricted to the estimation sample.
{p 4 4 2}This command provides graphics after model fitting. Its aims
thus differ from those of {help twoway_lfit:twoway lfit},
{help twoway_qfit:twoway qfit}, {help twoway_fpfit:twoway fpfit}, etc.,
which fit models on the fly and are restricted to the application
of particular modelling commands.
{title:Options}
{p 4 8 2}
{cmd:plottype()} specifies an alternative to {cmd:mspline} as
a {help twoway} plottype for showing the fitted model values.
{p 4 8 2}
{cmd:fitopts()} specifies graph options tuning the display of fitted values.
{p 4 8 2}
{cmd:separate()} specifies that values of fitted and observed responses
be plotted as separate groups corresponding to the distinct values of
the variable specified.
{p 4 8 2}
{it:scatter_options} are options allowed with {help twoway scatter}.
{p 8 8 2}
As usual, {cmd:by(}{it:varname}{cmd:)} specifies that predictions are to be
shown separately for different categories of {it:varname}. This will often be
appropriate when the categories of {it:varname} are associated
with one or more dummy or indicator variables, but see also the {cmd:separate()}
option.
{p 4 8 2}{cmd:plot(}{help plot_option:plot}{cmd:)} provides a way to add other
plots to the generated graph; see {help plot_option}.
{title:Examples}
{p 4 8 2}{cmd:. sysuse auto}
{p}continuous variables only:
{p 4 8 2}{cmd:. regress mpg weight}{p_end}
{p 4 8 2}{cmd:. regplot}{p_end}
{p 4 8 2}{cmd:. gen weightsq = weight^2}{p_end}
{p 4 8 2}{cmd:. regress mpg weight weightsq}{p_end}
{p 4 8 2}{cmd:. regplot}
{p 4 8 2}(N.B. {cmd: weight} shown on {it:x} axis in both cases)
{p}categorical variable also:
{p 4 8 2}{cmd:. regress mpg weight foreign}{p_end}
{p 4 8 2}{cmd:. regplot, by(foreign)}{p_end}
{p 4 8 2}{cmd:. regplot, sep(foreign)}{p_end}
{p 4 8 2}{cmd:. regress mpg weight weightsq foreign}{p_end}
{p 4 8 2}{cmd:. regplot, by(foreign)}{p_end}
{p 4 8 2}{cmd:. regplot, sep(foreign)}
{p 4 8 2}{cmd:. gen fw = foreign * weight}{p_end}
{p 4 8 2}{cmd:. regress mpg weight foreign fw}{p_end}
{p 4 8 2}{cmd:. regplot, by(foreign)}{p_end}
{p 4 8 2}{cmd:. regplot, sep(foreign)}
{p}commands other than {cmd:regress}:
{p 4 8 2}{cmd:. logit foreign weight}{p_end}
{p 4 8 2}{cmd:. regplot}
{p 4 8 2}{cmd:. glm mpg weight foreign, link(log)}{p_end}
{p 4 8 2}{cmd:. regplot, by(foreign)}
{p}fit an AR(1) plus trend model to time series:
{p 4 8 2}{cmd:. regress ERvol L.ERvol date}{p_end}
{p 4 8 2}{cmd:. regplot date}
{title:Author}
{p 4 4 2}Nicholas J. Cox, University of Durham, U.K.{break}
n.j.cox@durham.ac.uk
{title:Acknowledgements}
{p 4 4 2}Ken Higbee and Kit Baum provided very helpful comments on an earlier
version.
{title:Also see}
{p 4 13 2}On-line: help for {help twoway}, {help scatter}, {help modeldiag}