Stata 15 help for _pred_se

Title

[P] _pred_se -- Subroutine for programming single-equation extensions to predict

Example

program define estimator, eclass ... ereturn local predict "estimator_p" ... end

...

program define estimator_p local myopts "new_predict_options" _pred_se "`myopts'" `0' if `s(done)' { exit } local vtyp `s(typ)' local varn `s(varn)' local 0 `"`s(rest)'"' syntax [if] [in] [, `myopts' noOFFset] ... end

Description

_pred_se is a subroutine to assist programmers in implementing additions to predict following a single-equation estimation command or, more correctly, what appears to the user to be a single-equation estimation command even if, in the implementation, you used multiple equations to handle ancillary parameters.

This is necessary only if you want to add features to predict following your estimation command. If all you want are the standard predict features -- see [R] predict -- there is nothing you need to do; do not even define e(predict) in your estimation command.

If you do wish to add features, then your estimation command must set the name of your prediction routine in e(predict). We recommend that if your estimation command is X, you name your prediction routine X_p, truncating the X name if needed to fit within the program naming limit. For instance, if the estimation command were named ematreg, we recommend that the corresponding prediction routine be named ematreg_p. At the appropriate place in the code for ematreg.ado, include

ereturn local predict "ematreg_p"

Above we show the outline for ematreg_p.ado.

_pred_se will handle the standard cases. The "..." in your prediction program is responsible for handling the default case when no options are specified and the special case when one of the new options stored in `myopts' is specified.

Here is how we would fill in the dots if the predict options we were adding were pr, rratio, distance, and dfbeta(varname) so that, following estimation, the syntax of predict would be

predict [type] newvarname [if exp] [in range] [, pr rratio distance dfbeta(varname) index xb stdp nooffset ]

with pr the default. Note that index, xb, and stdp are the standard predict options; our program need only be concerned with providing code to handle the pr, rratio, distance, and dfbeta() options.

Our program is

program define ... version 15.1 local myopts "PR Rratio Distance DFBeta(varname)" _pred_se "`myopts'" `0' if `s(done)' { exit } local vtyp `s(typ)' local varn `s(varn)' local 0 `"`s(rest)'"' syntax [if] [in] [, `myopts' noOFFset]

/* concatenate switch options together */ local type "`pr'`rratio'`distanc'"

/* quickly process default case */ if ("`type'"=="" | "`type'"=="pr") & "`dfbeta'"=="" { if "`type'"=="" { di in gr "(option pr assumed)" } tempvar t qui _predict double `t' `if' `in', `offset' ... gen `vtyp' `varn' = ... `if' `in' label var `varn' "Probability of positive outcome" exit }

/* mark sample */ marksample touse

/* handle options that take argument, if any; we have one such option */ if "`dfbeta'" != "" { if "`type'" != "" { error 198 } ... exit }

/* handle switch options */ /* first do the ones that work both */ /* in and out-of-sample. */ if "`type'"=="rratio" { tempvar t ... qui _predict double `t' if `touse', stdp `offset' ... gen `vtyp' `varn' = ... if `touse' label var `varn' "R-metric ratio" exit }

/* then handle the options that only */ /* make sense when used with the */ /* estimation subsample */

qui replace `touse'=0 if ~e(sample)

if "`typ'"=="distance" { /* restricted to e(sample) */ tempvar r t ... qui predict double `r' if `touse', rratio `offset' qui _predict double `t' if `touse', stdp `offset' ... gen `vtyp' `varn' = ... if `touse' label var `varn' "Distance from centroids" exit }

error 198 end

In reviewing this program, please note the following:

1. All intermediate calculations are made using doubles.

2. _predict is used to assist in the calculations.

3. We call predict itself, and thus recursively call our program, in implementing option distance.

4. Caution is exercised to ensure that the nooffset option is handled correctly (or rather, that offsets, if they exist, are handled correctly).

5. Caution is exercised to ensure that the user sees the message "___ missing values generated" if any of the predictions are missing. This we accomplish by ending each calculation with a generate.

6. The new variable is labeled after creation.


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