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help for rvpplot27
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Graph residual-versus-predictor plot after regression-type command

        rvpplot27 varname [, residualtype force scale(exp) ksm(ksm_options)
                graph_options]


rvpplot27 is for use after regress and similar commands; see help on the
command of interest. It is a generalisation of official Stata's rvpplot, except
for different defaults for the l1title() option.


Description

rvpplot27 graphs a residual-versus-predictor plot (a.k.a. independent variable
plot, a.k.a. carrier plot), a graph of the residuals versus the specified
predictor varname from the last regression-type model. The residuals are, by
default, those calculated by predict, residuals or (if the previous estimation
command was glm) by predict, response.  This is a clone of rvpplot2 1.0.0 for
users of Stata 7.  Users of Stata 8 should use rvpplot2 2.0.0 or later.

Options

residualtype specifies a type of residual other than the default.  The
    following types are currently supported: anscombe, deviance, likelihood,
    pearson, residuals, response, rstandard, rstudent, score, working.

force allows you to specify a predictor variable not included in the previous
    model.

scale(exp) specifies a transformed scale on which to show the residuals using
    Stata syntax and X as a placeholder for the residual variable name. Thus
    scale(X^2) specifies squaring, to show relative contribution to residual
    variance; scale(abs(X)) specifies absolute value, to set aside sign;
    scale(sqrt(abs(X))) specifies root of absolute value, a useful scale on
    which to check for heteroscedasticity.

ksm(ksm_options) specifies that the residuals will be smoothed as a function of
    the predictor using ksm with the options named.

graph_options are any of the options allowed with graph, twoway. See help on 
    grtwoway.


Examples

        . reg width length
        . rvpplot27 length
        
        . glm price weight, link(log)
        . rvpplot27 weight, anscombe yli(0)


Author

    Nicholas J. Cox, University of Durham, U.K.
    n.j.cox@durham.ac.uk
    
    
Acknowledgements 

    Kit Baum identified an error in a previous version of this help. 


Also see

 Manual:  [R] regression diagnostics
On-line:  help for graph, regdiag; predict