binreg_postestimation.hlp

是一个经济学管理应用软件 很难找的 但是经济学学生又必须用到

{smcl}
{* 25mar2005}{...}
{cmd:help binreg postestimation}{right:dialog:  {bf:{dialog glim_p:predict}}}
{right:also see:  {helpb binreg}{space 1}}
{hline}

{title:Title}

{p2colset 5 34 36 2}{...}
{p2col :{hi:[R] binreg postestimation} {hline 2}}Postestimation tools for binreg{p_end}
{p2colreset}{...}


{title:Description}

{pstd}
The following postestimation commands are available for {opt binreg}:

{synoptset 13 tabbed}{...}
{p2coldent :command}description{p_end}
{synoptline}
INCLUDE help post_adjust3star
INCLUDE help post_estat
INCLUDE help post_estimates
INCLUDE help post_lincom
INCLUDE help post_linktest
INCLUDE help post_mfx
INCLUDE help post_nlcom
{p2col :{helpb binreg postestimation##predict:predict}}predictions, residuals, influence statistics, and other diagnostic measures{p_end}
INCLUDE help post_predictnl
INCLUDE help post_test
INCLUDE help post_testnl
{synoptline}
{p2colreset}{...}
{p 4 6 2}* {cmd:adjust} does not work with time-series operators.


{marker predict}{...}
{title:Syntax for predict}

{p 8 16 2}
{cmd:predict}
{dtype}
{newvar}
{ifin}
[{cmd:,} {it:{help binreg postestimation##statistic:statistic}}
         {it:{help binreg postestimation##options:options}}]

{synoptset 16 tabbed}{...}
{marker statistic}{...}
{synopthdr :statistic}
{synoptline}
{syntab :Main}
{synopt :{opt m:u}}g_inverse(xb); the default{p_end}
{synopt :{opt xb}}linear prediction{p_end}
{synopt :{opt e:ta}}synonym for {opt xb}{p_end}
{synopt :{opt stdp}}standard error of the linear prediction{p_end}
{synopt :{opt a:nscombe}}Anscombe residuals{p_end}
{synopt :{opt c:ooksd}}Cook's distance{p_end}
{synopt :{opt d:eviance}}deviance residuals{p_end}
{synopt :{opt h:at}}diagonals of the "hat" matrix as an analog to simple linear
regression{p_end}
{synopt :{opt l:ikelihood}}weighted average of the standardized deviance and
standard Pearson residuals{p_end}
{synopt :{opt p:earson}}Pearson residuals{p_end}
{synopt :{opt r:esponse}}differences between the observed and fitted
outcomes{p_end}
{synopt :{opt sc:ore}}first derivative of the log likelihood with respect to
xb{p_end}
{synopt :{opt w:orking}}working residuals{p_end}
{synoptline}
{p2colreset}{...}

{synoptset 16 tabbed}{...}
{marker options}{...}
{synopthdr :options}
{synoptline}
{syntab :Options}
{synopt :{opt noff:set}}modify calculations to ignore the offset
variable{p_end}
{synopt :{opt sta:ndardized}}multiply residual by the factor  (1 - h)^[1/2] {p_end}
{synopt :{opt stu:dentized}}multiply residual by one over the square root of
the estimated scale parameter{p_end}
{synopt :{opt mod:ified}}modify denominator of residual to be a reasonable
estimate of the variance of {depvar}{p_end}
{synopt :{opt adj:usted}}adjust deviance residual to make the convergence to
the limiting normal distribution faster{p_end}
{synoptline}
{p2colreset}{...}
INCLUDE help esample


{title:Options for predict}

{phang}
{opt mu}, the default, specifies that {opt predict} calculate the
inverse link of the linear prediction.

{phang}
{opt xb} calculates the linear prediction.

{phang}
{opt eta} is a synonym for {opt xb}.

{phang}
{opt stdp} calculates the standard error of the linear prediction.

{phang}
{opt anscombe} calculates the Anscombe residuals
to produce residuals that closely follow a normal distribution.

{phang}
{opt cooksd} calculates Cook's distance, which measures the aggregate
change in the estimated coefficients when each observation is left out of the
estimation.

{phang}
{opt deviance} calculates the deviance residuals, which was recommended by
McCullagh and Nelder and others as having the best properties for examining
goodness of fit of a GLM.  They are approximately normally distributed if the
model is correct and may be plotted against the fitted values or against a
covariate to inspect the model's fit.  Also see the {opt pearson} option
below.

{phang}
{opt hat} calculates the diagonals of the "hat" matrix as an analog to
simple linear regression.

{phang}
{opt likelihood} calculates a weighted average of the standardized
deviance and standardized Pearson (described below) residuals.

{phang}
{opt pearson} calculates the Pearson residuals, which often have markedly
skewed distributions for non-normal family distributions.  Also see the
{opt deviance} option above.

{phang}
{opt response} calculates the differences between the observed and
fitted outcomes.

{phang}
{opt score} calculates the equation-level score; the derivative of the log
likelihood with respect to the linear prediction.

{phang}
{opt working} calculates the working residuals, which are response
residuals weighted according to the derivative of the link function.

{phang}
{opt nooffset} is relevant only if you specified {opth offset(varname)} for
{opt binreg}.  It modifies the calculations made by {opt predict} so that they
ignore the offset variable; the linear prediction is treated as xb rather
than as xb + offset.

{phang}
{opt standardized} requests that the residual be multiplied by the
factor (1 - h)^[-1/2], where h is the diagonal of the hat matrix.  This is done
to take into account the correlation between {depvar} and its predicted value.

{phang}
{opt studentized} requests that the residual be multiplied by one over
the square root of the estimated scale parameter.

{phang}
{opt modified} requests that the denominator of the residual be modified
to be a reasonable estimate of the variance of {depvar}.  The base residual
is multiplied by the factor (k/w)^[-1/2], where k is either one or the
user-specified dispersion parameter and w is the specified weight (or one if
left unspecified).

{phang}
{opt adjusted} adjusts the deviance residual to make the convergence to
the limiting normal distribution faster.  The adjustment deals with adding to
the deviance residual a higher-order term depending on the variance
function family.  This option is only allowed when {opt deviance} is
specified.


{title:Examples}

{phang}{cmd:. binreg low age lwt race2 race3 smoke ptl ht ui, or}{p_end}
{phang}{cmd:. predict rate, mu}{p_end}

{phang}{cmd:. binreg dead ln_dose, rr coefficients n(n)}{p_end}
{phang}{cmd:. predict devres, deviance adjusted}{p_end}


{title:Also see}

{psee}
Manual:  {bf:[R] binreg postestimation}

{psee}
Online:  {helpb binreg};{break}
{helpb adjust},
{helpb estimates},
{helpb lincom},
{helpb linktest},
{helpb mfx},
{helpb nlcom},
{helpb predictnl},
{helpb test},
{helpb testnl}
{p_end}