{smcl}
{* 03Mar2005}{...}
{hline}
help for {cmd:svypxcat}
{hline}
{title:Predicted means or proportions for nominal X's for survey data}
{p 6 21 2}
{cmdab:svypxcat} {it:yvar} [{cmd:if} {it:exp}] [{cmd:in} {it:range}]{cmd:,}
{cmd:xvar(}{it:xvar1} [{it:xvar2}]{cmd:)}
[ {cmdab:adj:ust(}{it:covlist}{cmd:)}
{cmdab:mod:el}
{cmdab:sub:pop(}{it:subpop_spec}{cmd:)}
{cmdab:l:evel(}#{cmd:)}
{cmdab:lin:ear}
{cmdab:g:raph}
{cmdab:b:ar}
{cmdab:sav:epred(}{it:filename}{cmd:)}
{it:graph_options}
]
{title:Description}
{p 4 8 4}
{cmd:svypxcat} requires that the survey design variables be identified using {help svyset}
{p 4 8 4}
{cmd:svypxcat} calculates and optionally graphs means from linear regression
models or proportions from logistic regression models corrected for the survey
sampling scheme (weights) for one or two nominal X variables, adjusted
for covariates. If a second X is specified, means or proportions are calculated
for all possible combinations of X categories, and an interaction effect is tested.
Optionally, model estimates and/or a graph may be displayed. Dummy variables are
created for {it:xvar1}. The lowest dummy variable defaults to the reference group.
If {it:xvar2} is specified, dummy variables are created for it, as well as
interaction terms. (Updated for Version 9)
{title:Variables and options required}
{p 4}{it:yvar} is the dependent variable
{p 8 8 2}If {it:yvar} is continuous, defaults to linear regression
{p 8 8 2}If {it:yvar} is binary (0,1), defaults to logistic regression
{p 4 8 2}
{cmd:xvar(}{it:xvar1}{cmd:)} is the nominal variable for categories of estimated
means or proportions
{p 4 8 2}
{cmd:xvar(}{it:xvar1 xvar2}{cmd:)} gives categories of all combinations of
{it:xvar1} and {it:xvar2}; interaction between {it:xvar1} and {it:xvar2} is
tested (Partial F for linear regression or Wald test for logistic regression)
{title:Options}
{p 4 8 2}
{cmd:adjust(}{it:covlist}{cmd:)} lists any covariates. If none are specified,
unadjusted means or proportions are reported. Covariates are set to their mean,
based on observations used in the analysis, or can be set to user specified values
(e.g., age=50 gender=1). (Note: if an {cmd:adjust} variable is nominal with more
than 2 categories, it must be defined with dummy variables in the {cmd:adjust}
list, whereas dummy variables are created automatically in the {cmd:xvar} option)
{p 4 8 2}
{cmd:model} -- for display purposes only, this option prints the regression table
{p 4 8 2}
{cmd:subpop(}{it:subpop_spec}{cmd:)} -- specifies that estimates be computed for
the single subpopulation identified in {it:subpop_spec}
{p 4 8 2}
{cmd:level(}#{cmd:)} -- specifies the confidence level, in percent, for
calculation of confidence intervals (default=95%)
{p 4 8 2}
{cmd:graph} -- if one X ({it:xvar1}), graphs means or proportions and confidence
intervals; if both {it:xvar1} and {it:xvar2} are specified, points are graphed
for each mean or proportion, but confidence intervals are not graphed; {it:xvar1}
is used for the x-axis with separate points for categories of {it:xvar2}
{p 4 8 2}
{cmd:bar} -- can be used with the {cmd:graph} option to display a bar graph
instead of points
{p 4 8 2}
{cmd:linear} -- requests linear regression when {it:yvar} is binary (0,1); if not
specified, logistic regression is assumed
{p 4 8 2}
{cmd:savepred(}{it:filename}{cmd:)} -- saves adjusted values and CI's to a Stata file
{title:Examples}
{p 4 8 2}{cmd:. svypxcat chol, xvar(race) adjust(sbp age=50) model}
{p 8 8 2}
Uses linear regression to calculate mean cholesterol level by race category,
adjusted for mean sbp and age=50
{p 4 8 2}{cmd:. svypxcat chol, xvar(ses) adjust(sbp age) graph}
{p 8 8 2}
Uses linear regression to calculate mean cholesterol by levels of
socio-economic status, adjusted for sbp and age; displays graph
{p 4 8 2}{cmd:. svypxcat htn, xvar(gender race) adjust(age smoke etoh)}
{p 8 8 2}
Uses logistic regression to calculate proportion of hypertensives for all
combinations of gender (2 categories) and race (4 categories) for a total
of 8 proportions, adjusted for age, smoking status, and alcohol consumption;
tests for interaction between gender and race
{p 4 8 2}{cmd:. svypxcat htn, xvar(race gender) adjust(age smoke etoh) graph bar}
{p 8 8 2}
Uses logistic regression to calculate the adjusted proportion of hypertensives
for all combinations of gender and race; bar graph of proportions, race on
the x-axis
{title:Also see}
{p 4 13 2}
Manual: {hi:[U] 30 Overview of survey estimation},{break}
{hi:[SVY] intro},{break}
{hi:[SVY] svy}
{p 4 13 2}
Online: help for {help predxcat} (if loaded), {help svy}
{title:Author}
{p 4 8 2}
{hi:J.M.Garrett}, Professor, School of Medicine, University of North Carolina,
Chapel Hill, NC.
Email: {browse "mailto:joanne_garrett@med.unc.edu":joanne_garrett@med.unc.edu}