.-
help for ^adjust^                                    (STB-46: sg89.2)
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Adjusted predictions and probabilities after estimation
-------------------------------------------------------

     ^adjust^ var[^=^ #] [var[^=^ #] ...] [^if^ exp] [^in^ range]^,^ ^by(^varlist^)^ [{^xb^|^p^r}
          {^stdp^|^stdf^|^noe^rror} ^g^enerate^(^newvar1 [newvar2]^)^ ^xblab^el^(^["]text["]^)^
          ^prlab^el^(^["]text["]^)^ ^stdplab^el^(^["]text["]^)^ ^stdflab^el^(^["]text["]^)^
          <tabdisp_options>]

Where allowed @tabdisp@ options are: ^f^ormat^(%^fmt^)^, ^cen^ter, ^l^eft, ^cellw^idth^(^#^)^,
^csep^width^(^#^)^, ^scsep^width^(^#^)^, and ^stubw^idth^(^#^)^.  See @tabdisp@ for help.


Description
-----------

After an estimation command ^adjust^ provides adjusted predictions of xbeta (the
means in a linear-regression setting) or probabilities (available after certain
estimation commands).  The estimate is computed for each level of the ^by^
variable(s) setting the variable(s) specified in var[= #] [var[= #] ...] to
their mean or to the specified number if the = # part is used.  Variables used
in the estimation command but not included in either the ^by^ variable list or
the ^adjust^ variable list are left at their current values, observation by
observation.  In this case ^adjust^ displays the average estimated prediction or
probability for each level of the ^by^ variables.


Options
-------

^by(^varlist^)^ is required and specifies the variable(s) whose levels determine
    the subsets of the data for which adjusted predictions are to be computed.
    The variables in the ^by^ option are not required to be involved in the
    original estimation command.

^xb^ indicates that the linear prediction from the estimation command is to be
    used.  This produces predicted values (means in the linear-regression
    setting) and is equivalent to the ^xb^ option of @predict@.  Realize that
    depending on the estimation command the ^xb^ values may not be in the
    original units of the dependent variable.

^pr^ is an alternative to ^xb^ and indicates that predicted probabilities are to be
    computed.  The ^pr^ option is only allowed after a few commands (@blogit@,
    @bprobit@, @dprobit@, @logistic@, @logit@, and @probit@).  The default is ^xb^ if ^pr^ is
    not specified.

^stdp^, ^stdf^, and ^noerror^ specify the kind of standard error reported for each
    mean.  ^stdp^, the default, uses the standard error of the linear prediction
    and is available only with the ^xb^ option.  ^stdf^ uses the standard error of
    the forecast which is the standard error of the linear prediction plus the
    residual.  ^stdf^ may be specified only with the ^xb^ option and after linear-
    regression estimation commands (@boxcox@, @corc@, @fit@, @hlu@, @prais@, @regdw@,
    @regress@, and @rreg@).  ^noerror^ specifies that no error term is to be used and
    hence no standard errors for the means reported in the table.

^generate(^newvar1 [newvar2]^)^ generates one or two new variables.  If one 
    variable is specified then the adjusted predictions for each observation
    are generated in ^newvar1^ (holding the appropriate variables to their means
    or other specified values).  If ^pr^ is specified then the predictions are 
    probabilities.  If ^newvar2^ is specified then the standard errors from
    either the ^stdp^ or ^stdf^ option are placed in the second variable.

^xblabel()^, ^prlabel()^, ^stdplabel()^, and ^stdflabel()^ allow you to change the
    labels for ^xb^, ^pr^, ^stdp^, and ^stdf^ in the output produced by ^adjust^.  This
    also changes the variable labels for the variables created by the ^generate^
    option.

Remarks
-------

If you have restricted your estimation command to a portion of the data using
^if^ or ^in^ then you will generally want to use the same conditions with ^adjust^.
However, there may be legitimate reasons for using different data to perform
the estimation and to obtain adjusted predictions.

An interesting way to use ^adjust^ is after using @xi@ with the estimation command.
Another interesting use of ^adjust^ is to specify a categorical variable both in
the variable list being set to a specific value and as a ^by^ variable.  This is
helpful in examining the predictions for several groups as if they were set at
a particular group's value (possibly also holding some other variables to
certain values also).

^adjust^ is implemented to follow the same estimation commands as listed in the
help for @predict@.  However, ^adjust^ currently is not allowed after @anova@
(because we can not obtain the variable names from the beta vector), @areg@
(because @predict@ doesn't handle the absorbed variable as expected), and
multiple-equation estimation commands.  ^adjust^ only uses @predict@.  It does
not use any specialized predict commands such as @fpredict@, @glmpred@, and @xtpred@.

^adjust^ is similar to ^adjmean^ and ^adjprop^ written by Joanne Garrett (see STB-43
sg33.1).  There are some differences between this command and those.  First,
^adjmean^ and ^adjprop^ internally use @regress@ and @logistic@ and are not available
for other estimation commands.  In comparison, ^adjust^ does not perform the
estimation command, but instead is a post estimation command and will work
after a large number of single equation estimation commands.  A second
difference is that ^adjust^ lets you specify up to seven variables in the ^by^
option.  ^adjust^ also allows ^by^ variables that were not used in the estimation
command.  Variables used in the estimation command are left at their individual
values if the variables are not specified in the ^adjust^ command.  Another
difference is that ^adjmean^ and ^adjprop^ compute confidence intervals and
optionally display them in a graph, while ^adjust^ does not.  ^adjust^ really is a
front end process for @predict@.  It sets up the values at which predictions are
desired and then displays them using @tabdisp@.  This means that ^adjust^ does not
provide confidence intervals directly, but will provide ^stdp^ or ^stdf^ estimates
of error under the same conditions that @predict@ would produce them.


Examples
--------

After a regression:

        . ^regress price mpg weight turn foreign^
        . ^adjust mpg weight turn, by(foreign)^
        . ^adjust mpg weight turn, by(foreign) stdf^

This time we set some variables to specific values instead of their mean.

        . ^adjust mpg=25 weight turn=35.2, by(foreign) noerror^

We can generate variables containing the predictions and errors.

        . ^adjust mpg weight, by(foreign) gen(pred err)^

We can use multiple ^by^ variables and they aren't required to have been used in
the estimation command.

        . ^adjust mpg weight, by(foreign rep78)^

After a @logit@ estimation command:

        . ^logit foreign weight mpg^
        . ^adjust mpg, by(rep78) pr^
        . ^adjust mpg, by(rep78)^

After a @probit@ estimation command:

        . ^probit foreign weight mpg^
        . ^adjust mpg, by(rep78) pr^
        . ^adjust mpg, by(rep78) gen(xb ee)^

Using @xi@ with the estimation command followed by ^adjust^:

        . ^xi : regress price I.rep78 mpg weight turn^
        . ^adjust mpg weight turn, by(rep78)^

Both set a variable and use it as a ^by^ variable:

        . ^regress price mpg weight turn foreign^
        . ^adjust weight foreign=0, by(foreign)^
Compare this to:
        . ^adjust weight, by(foreign)^
and this:
        . ^adjust weight foreign=1, by(foreign)^


Author
------

     Kenneth Higbee
     StataCorp
     email: tech-support@@stata.com


Also see
--------

    STB:  STB-46 sg89.2, STB-45 sg89.1, STB-44 sg89
          STB-43 sg33.1 (for ^adjmean^ and ^adjprop^)
 Manual:  ^[R] predict^
          ^[U] 26 Estimation and post-estimation commands^
          
