ztp_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}
{* 31mar2005}{...}
{cmd:help ztp postestimation}{right:dialog:  {bf:{dialog ztp_p:predict}}}
{right:also see:  {helpb ztp}    }
{hline}

{title:Title}

{p2colset 5 31 33 2}{...}
{p2col :{hi:[R] ztp postestimation} {hline 2}}Postestimation tools for ztp
{p_end}
{p2colreset}{...}


{title:Description}

{pstd}
The following postestimation commands are available for {cmd:ztp}:

{synoptset 11}{...}
{p2col :command}description{p_end}
{synoptline}
INCLUDE help post_adjust2
INCLUDE help post_estat
INCLUDE help post_estimates
INCLUDE help post_lincom
INCLUDE help post_lrtest
INCLUDE help post_mfx
INCLUDE help post_nlcom
{p2col :{helpb ztp postestimation##predict:predict}}predictions, residuals, influence statistics, and other diagnostic measures{p_end}
INCLUDE help post_predictnl
INCLUDE help post_suest
INCLUDE help post_test
INCLUDE help post_testnl
{synoptline}
{p2colreset}{...}


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

{p 8 16 2}
{cmd:predict} {dtype} {newvar} {ifin} [{cmd:,} 
   {it:statistic} {opt nooff:set}]

{synoptset 11 tabbed}{...}
{synopthdr:statistic}
{synoptline}
{synopt :{opt n}}predicted number of events (the default){p_end}
{synopt :{opt ir}}incidence rate (equivalent to {cmd:predict} ..., {cmd:n nooffset}){p_end}
{synopt :{opt cm}}estimate of conditional mean, E(n|n > 0){p_end}
{synopt :{opt xb}}linear prediction{p_end}
{synopt :{opt stdp}}standard error of the linear prediction{p_end}
{synopt :{opt sc:ore}}first derivative of the log likelihood with respect to xb{p_end}
{synoptline}
{p2colreset}{...}
INCLUDE help esample


{title:Options for predict}

{phang}
{opt n}, the default, calculates the predicted number of events, which
is exp(xb) if neither {opt offset()} nor {opt exposure()} was specified
when the model was fitted; {bind:exp(xb + offset)} if {opt offset()} was
specified; or {bind:exp(xb) x exposure} if {opt exposure()} was specified.

{phang}
{opt ir} calculates the incidence rate exp(xb), which is the predicted
number of events when exposure is 1.  This is equivalent to specifying both
{opt n} and {opt nooffset} options.

{phang}
{opt cm} calculates the estimate of conditional mean of n, given n>0, i.e.
E(n|n>0,x), which is exp(xb)/P(n > 0|x) if neither {opt offset()} nor
{opt exposure()} was specified when the zero-truncated negative binomial model
was fitted, or {bind:exp(xb + offset)/P(n > 0|x)} if {opt offset()} was
specified, or {bind:exp(xb)/P(n > 0|x)*exposure} if {opt exposure()} was
specified.

{phang}
{opt xb} calculates the linear prediction, which is xb if neither 
{opt offset()} nor {opt exposure()} was specified; 
{bind:xb + offset} if {opt offset()} was specified; or 
{bind:xb + ln(exposure)} if {opt exposure()} was specified; see 
{opt nooffset} below.

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

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

{phang}
{opt nooffset} is relevant only if you specified {opt offset()} or
{opt exposure()} when you fitted the model.  It modifies the calculations made
by {cmd:predict} so that they ignore the offset or exposure variable; the
linear prediction is treated as xb rather than as {bind:xb + offset}
or {bind:xb + ln(exposure)}.  Specifying {cmd:predict} ...{cmd:, nooffset} is
equivalent to specifying {cmd:predict} ...{cmd:, ir}.


{title:Examples}

{psee}{cmd:. ztp shoes distance, exposure(age)}{p_end}
{psee}{cmd:. predict shoehat, n}{p_end}
{psee}{cmd:. predict shoecm, cm}{p_end} 

{title:Also see}

{psee}
Manual:  {bf:[R] ztp postestimation}{p_end}

{psee}
Online:  {helpb ztp};{break}
{helpb adjust}, {helpb estimates},
{helpb lincom}, {helpb lrtest}, {helpb mfx}, {helpb nlcom},
{helpb predictnl}, {helpb suest}, {helpb test}, {helpb testnl}{p_end}