-------------------------------------------------------------------------------
help for rvfplot2
-------------------------------------------------------------------------------

Plot residuals versus fitted values after model fit

        rvfplot2 [, residualtype qualifier rscale(exp) fscale(exp)
                 lowess[(lowess_options)] scatter_options plot(plot) ]


Description

    rvfplot2 graphs a residual-versus-fitted plot, a graph of the residuals
    versus the fitted values. The residuals are, by default, those calculated
    by predict, residuals or (if the previous estimation command was glm) by
    predict, response. The fitted values are those produced by predict by
    default after each estimation command.

    rvfplot2 is for use after regress and similar commands; see help on the
    command of interest. It is a generalisation of rvfplot in official Stata.


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.

    qualifier specifies one of standardized, studentized, modified, adjusted.

    rscale(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 rscale(X^2) specifies squaring, to show relative
        contribution to residual variance; rscale(abs(X)) specifies absolute
        value, to set aside sign; rscale(sqrt(abs(X))) specifies root of
        absolute value, a useful scale on which to check for
        heteroscedasticity.

    fscale(exp) specifies a transformed scale on which to show the fitted
        values using Stata syntax and X as a placeholder for the fitted
        variable name. Thus for example fscale(2 * ln(X)) specifies twice the
        natural logarithm, which is the constant information scale for a
        generalised linear model with gamma error. Similarly, arguments of 2
        * sqrt(X), 2 * asin(sqrt(X)), and -2 / sqrt(X) specify the constant
        information scale for Poisson, binomial and inverse Gaussian errors
        respectively. See McCullagh and Nelder (1989, p.398) for background.

    lowess[(lowess_options)] specifies that the residuals will be smoothed as
        a function of the fitted using twoway lowess (options may be
        specified).

    scatter_options are options of twoway scatter.

    plot(plot) provides a way to add other plots to the generated graph; see 
        plot_option.


Examples

    . gen forxmpg = foreign * mpg
    . regress price weight mpg forxmpg foreign
    . rvfplot2

    . anova price rep foreign rep*foreign weight, cont(weight)
    . rvfplot2, rscale(sqrt(abs(X)))

    . glm price weight, link(log)
    . rvfplot2, anscombe yli(0)

    . glm price weight, link(log)
    . rvfplot2, anscombe yli(0) lowess
    . rvfplot2, anscombe yli(0) lowess(bw(0.9))


Author

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


Acknowledgements 

    Phil Ender provided very helpful comments on a previous version.


References     

    McCullagh, P. and Nelder, J.A. 1989. Generalized linear models.  London:
    Chapman and Hall.


Also see