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help for svypxcat
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Predicted means or proportions for nominal X's for survey data   

      svypxcat yvar [if exp] [in range], xvar(xvar1 [xvar2]) [
                     adjust(covlist) model subpop(subpop_spec) level(#)
                     linear graph bar savepred(filename) graph_options ]


Description

    svypxcat requires that the survey design variables be identified using 
        svyset


    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 xvar1. The lowest dummy variable defaults to the
        reference group.  If xvar2 is specified, dummy variables are
        created for it, as well as interaction terms.  (Updated for Version
        9)

 
Variables and options required

    yvar is the dependent variable

        If yvar is continuous, defaults to linear regression

        If yvar is binary (0,1), defaults to logistic regression


    xvar(xvar1) is the nominal variable for categories of estimated means or
        proportions

    xvar(xvar1 xvar2) gives categories of all combinations of xvar1 and
        xvar2; interaction between xvar1 and xvar2 is tested (Partial F for
        linear regression or Wald test for logistic regression)

        
Options 

    adjust(covlist) 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 adjust
        variable is nominal with more than 2 categories, it must be defined
        with dummy variables in the adjust list, whereas dummy variables are
        created automatically in the xvar option)

    model -- for display purposes only, this option prints the regression
        table

    subpop(subpop_spec) -- specifies that estimates be computed for the
        single subpopulation identified in subpop_spec

    level(#) -- specifies the confidence level, in percent, for calculation
        of confidence intervals (default=95%)

    graph -- if one X (xvar1), graphs means or proportions and confidence
        intervals; if both xvar1 and xvar2 are specified, points are graphed
        for each mean or proportion, but confidence intervals are not
        graphed; xvar1 is used for the x-axis with separate points for
        categories of xvar2

    bar -- can be used with the graph option to display a bar graph instead
        of points

    linear -- requests linear regression when yvar is binary (0,1); if not
        specified, logistic regression is assumed

    savepred(filename) -- saves adjusted values and CI's to a Stata file


Examples

    . svypxcat chol, xvar(race) adjust(sbp age=50) model

        Uses linear regression to calculate mean cholesterol level by race
        category, adjusted for mean sbp and age=50

    . svypxcat chol, xvar(ses) adjust(sbp age) graph

        Uses linear regression to calculate mean cholesterol by levels of
        socio-economic status, adjusted for sbp and age; displays graph

    . svypxcat htn, xvar(gender race) adjust(age smoke etoh)

        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

    . svypxcat htn, xvar(race gender) adjust(age smoke etoh) graph bar

        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


Also see

    Manual:  [U] 30 Overview of survey estimation,
             [SVY] intro,
             [SVY] svy

    Online:  help for predxcat (if loaded), svy


Author

    J.M.Garrett, Professor, School of Medicine, University of North Carolina,