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help for svypxcon
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Predicted means or proportions for a continuous X variable for survey data   

        svypxcon yvar [if exp] [in range], xvar(xvar) from(#) to(#) inc(#) [
                       adjust(covlist) poly(#) class(classvar) model
                       subpop(subpop_spec) graph level(#) linear nolist
                       savepred(filename) graph_options ]


Description

    svypxcon calculates and prints predicted values and 95% confidence
        intervals for linear or logistic model estimates for a continuous X
        variable corrected for the survey sampling scheme (weights),
        adjusted for covariates.  Default prints predicted values and
        confidence intervals; model estimates and graph can be shown.
        Optionally a quadradic or a quadratic and cubic term can be added
        to the model. An interaction between X and a nominal variable can
        be estimated and graphed. Dummy variables are created for the
        nominal variable with the lowest category defaulting to the
        reference group.  (Updated for Version 9)

    svypxcon requires that the survey design variables be identified using 
        svyset


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(xvar) -- continuous independent variable (interval or ordinal)

    from(#) -- bottom value for xvar

    to(#) -- top value for xvar

    inc(#) -- increment size between bottom and top values (defaults to 1)


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)

    poly(2 or 3) -- polynomial terms added: 2=quadratic 3=quadradic and cubic

    class(classvar) -- nominal variable for an xvar by classvar interaction

    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

    graph -- displays graph of predicted values and 95% confidence intervals;
        if class() is requested, confidence intervals are not displayed

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

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

    nolist -- does not print list of predicted values and 95% CIs

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

    
Examples

    . svypxcon chol, xvar(age) from(20) to(80) inc(5) adjust(sys hra)

        Using linear regressin, calculates the predicted cholesterol values
        for 5-year increments of age from 20 years to 80 years (i.e.,
        age=20,25,30,...,80) adjusted for systolic blood pressure and heart
        rate; displays predicted values, but does not display model or graph

    . svypxcon chol, xvar(sys) f(60) t(280) i(20) adj(hra) graph class(race)

        Using linear regression, calculates the predicted cholesterol values
        for systolic blood pressure readings from 60 to 280 in 20mm/hg
        increments by categories of race, adjusted for heart rate; tests for
        an age by race interaction; displays graph and list of predicted
        values

    . svypxcon htn, xvar(chol) f(100) t(300) i(20) class(race) graph

        Using logistic regression, calculates the unadjusted probability of
        hypertension for cholesterol from 100 to 300 in 20-unit increments,
        by categories of race; displays test for interaction between
        cholesterol and race; graphs the results

    . svypxcon htn, xvar(age) f(40) t(80) poly(3) adj(chol=250) graph

        Using logistic regression, calculates the probabilitity of
        hypertension for 1-yr increments of age; terms for age-squared and
        age-cubed included in model using the poly(3) option; also adjusted
        to a cholesterol level of 250; displays graph


Author

    J.M.Garrett, Professor, School of Medicine, University of North Carolina,
        Chapel Hill, NC.  Email: joanne_garrett@med.unc.edu