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help for predxcat
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Predicted means, medians, or proportions for nominal X's   

      predxcat yvar [if exp] [in range], xvar(xvar1 [xvar2]) [
                     adjust(covlist) model level(#) linear median graph bar
                     cluster(cluster_var) savepred(filename) graph_options ]


Description

    predxcat calculates and optionally graphs means from linear regression
        models, medians from quantile regression models, or proportions
        from logistic regression models for one or two nominal X variables,
        adjusted for covariates. If a second X is specified, means, medians
        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.  (Alternative to adjust
        command.)

 
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,
        medians, 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 or quantile regression and likelihood ratio test for logistic
        regression)

        
Options 

    adjust(covlist) lists any covariates. If none are specified, unadjusted
        means, medians, 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

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

    graph -- if one X (xvar1), graphs means, medians, or proportions and
        confidence intervals; if both xvar1 and xvar2 are specified, points
        are graphed for each mean, median, 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

    median -- requests quantile regression when yvar is continuous

    cluster(cluster_var) -- Name of cluster variable; adjusts standard errors
        for intraclass correlation

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


Examples

    . predxcat 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; displays model

    . predxcat 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

    . predxcat 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

    . predxcat 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] 23 Estimation and post-estimation commands,
             [R] adjust


Author

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