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help for predxcon
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Predicted means, medians, or proportions for a continuous X variable   

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


Description

    predxcon calculates and prints predicted values and 95% confidence
        intervals for linear, quantile, or logistic model estimates for a
        continuous X variable, 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.


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, 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)

    poly(2 or 3) -- polynomial terms added: 2=quadratic 3=quadradic and cubic
        (will not work with the median option)

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

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

    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

    median -- requests quantile regression when yvar is continuous

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

    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

    . predxcon 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

    . predxcon 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 300 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

    . predxcon 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 200 in 20-unit increments,
        by categories of race; displays a likelihood ratio test for
        interaction; graphs the results

    . predxcon 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