help stgenreg postestimation                                also see:  stgenreg


stgenreg postestimation -- Postestimation tools for stgenreg


The following standard postestimation commands are also available:

command description ------------------------------------------------------------------------- INCLUDE help post_estat INCLUDE help post_estimates INCLUDE help post_lincom INCLUDE help post_lrtest INCLUDE help post_nlcom predict predictions INCLUDE help post_predictnl INCLUDE help post_test INCLUDE help post_testnl -------------------------------------------------------------------------

Syntax for predict

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

statistic Description ------------------------------------------------------------------------- Main hazard hazard function survival survival function cumhazard cumulative hazard function ci calculate confidence intervals stdp standard error of predicted function timevar(varname) time variable used for predictions (default _t) at(vn # [vn # ...]) predict at values of specified covariates zeros sets all covariates to zero (baseline prediction) mata the user-defined [log] hazard function contains a Mata only function, see details Subsidiary level sets confidence level (default 95) -------------------------------------------------------------------------

Options for predict

+------+ ----+ Main +-------------------------------------------------------------

hazard calculates the predicted hazard.

survival calculates the predicted survivor function

cumhazard calculates the predicted cumulative hazard.

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

stdp calculates standard error of prediction and stores it in newvar_se.

timevar(varname) defines the variable used as time in the predictions. Default varname is _t. 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.

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.

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

mata uses Mata to calculate the prediction, required when a Mata only function is included in the user-defined [log] hazard function used to fit the model. ci is not available when mata is used.

+------------+ ----+ Subsidiary +-------------------------------------------------------

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


Setup webuse brcancer stset rectime, failure(censrec = 1) scale(365.25)

Weibull proportional hazards model stgenreg, loghazard([ln_lambda] :+ [ln_gamma] :+ (exp([ln_gamma]):-1):*log(#t)) ln_lambda(hormon) predict h1, hazard ci predict s1, survival ci

Restricted cubic spline model on the log hazard scale stgenreg, loghazard([xb]) xb(hormon | #rcs(df(3))) predict h2, hazard ci predict s2, survival ci