*! version 1.3.1 11aug2012

help stjm11 postestimationalso see: stjm11, stjmgraph

Title

stjm11 postestimation -- Post-estimation tools for stjm11

Description

The following standard post-estimation commands are available:

command description

predict predictions, residuals etc

Syntax for predict

Syntax for obtaining best linear unbiased predictions (BLUPs) of random effects, or the BLUPs' standard errors

predict {stub*|newvarlist} , {reffects | reses}

Syntax for obtaining other predictions

predict newvar [if] [in] [, statistic options]

statistic

Longitudinal
longitudinal longitudinal submodel

residuals longitudinal residuals, response minus fitted values

rstandard standardised residuals

Survival
hazard hazard

survival survival S(t)

cumhazard cumulative hazard

martingale martingale-like residuals

deviance deviance residuals

Random effects
reffects best linear unbiased predictions (BLUPS) of the random effects

reses standard errors of the best linear unbiased predictions (BLUPS) of the random effects

options

xb see description below

fitted fitted values, linear predictor of the fixed portion plus contributions based on predicted random effects

m(#) number of draws from the estimated random effects variance-covariance matrix in survival sub-model predictions

at(varname # [varname # ...]) predict at values of specified covariates

ci calculate confidence intervals

timevar(varname) time variable used for predictions (defaults: _t0 for longitudinal sub-model, _t for survival sub-model)

meastime evaluate predictions at measurements, _t0

survtime evaluate predictions at survival times, _t

zeros sets all covariates to zero (baseline prediction)

level(#) sets confidence level (default 95)

Statistics are available both in and out of sample; type
predict ... if e(sample) ... if wanted only for the
estimation sample.

Options for predict

Longitudinal

longitudinal predicts the fitted values for the longitudinal submodel. If xb is specified (the default) then
only contributions from the fixed portion of the model are included. If fitted is specified then estimates of the random effects are also included.

residuals calculates residuals, equal to the longitudinal response minus fitted values. By default, the fitted values take into
account the random effects.

rstandard calculates standardized residuals, equal to the residuals multiplied by the inverse square root of the estimated
error covariance matrix.

Survival

hazard calculates the predicted hazard.

survival calculates each observation's predicted survival probability.

cumhazard calculates the predicted cumulative hazard.

martingale calculates martingale-like residuals.

deviance calculates deviance residuals.

Random effects

reffects calculates best linear unbiased predictions (BLUPs) of the random effects. You must specify q new variables, where q is the number of random effects
terms in the model (or level). However, it is much easier to just specify stub* and let Stata name the variables stub1...stubq for you.

reffects calculates the standard errors of the best linear unbiased predictions (BLUPs) of the random effects. You must specify q new variables, where q is the number of random effects
terms in the model (or level). However, it is much easier to just specify stub* and let Stata name the variables stub1...stubq for you.

Subsidiary

xb specifies predictions based on the fixed portion of the model when a longitudinal option is chosen. When the prediction option is
hazard, survival or cumhazard, the predictions are based on the average of the fixed portion plus m draws from the estimated
random effects variance-covariance matrix.

fitted linear predictor of the fixed portion plus contributions based on predicted random effects.

m specifies the number of draws from the estimated random effects variance-covariance matrix in survival sub-model predictions when xb is chosen.

at(varname # [ varname # ...]) requests that the covariates specified by
the listed varname(s) be set to the listed # values. For example,
at(x1 1 x3 50) would evaluate predictions at x1 = 1 and
x3 = 50. This is a useful way to obtain
out of sample predictions. Note that if at() is used together
with zeros all covariates not listed in at()
are set to zero. If at() is used without zeros then
all covariates not listed in at() are set to their sample
values. See also zeros.

ci calculate a confidence interval for the requested statistic and
stores the confidence limits in newvar_lci and
newvar_uci.

timevar(varname) defines the variable used as time in the predictions.
This is useful for large datasets where for plotting purposes predictions are only needed for 200 observations for example.
Note that some caution should be taken when using this option as predictions may be
made at whatever covariate values are in the first 200 rows of data.
This can be avoided by using the at() option and/or the zeros option to
define the covariate patterns for which you require the predictions.

meastime evaluate predictions at measurement times i.e. _t0.

survtime evaluate predictions at survival times i.e. _t.

zeros sets all covariates to zero (baseline prediction). For
example, predict s0, survival zeros calculates the baseline
survival function. See also at().

level(#) sets the confidence level; default is level(95)
or as set by set level.

Example

Load simulated example dataset:

. use http://fmwww.bc.edu/repec/bocode/s/stjm_example

stset the data:

. stset stop, enter(start) f(event=1) id(id)

Joint model with a random intercept in the longitudinal submodel, and association based on the current value. No covariates in either submodel.

. stjm11 long_response, panel(id) survmodel(weibull) gh(5) ffracpoly(1)

Predict the marginal survival function.

. predict s1, survival

Predict the longitudinal fitted values including fixed effects and contributions from the random effects.

. predict longfitvals, longitudinal fitted

Also see

Online: stjm11; stjmgraph