The data set produced by the OUTEST= option in the PROC NLIN statement contains the parameter estimates on each iteration, including the grid search.
The variable _ITER_
contains the iteration number. The variable _TYPE_
denotes whether the observation contains iteration parameter estimates (“ITER”), final parameter estimates (“FINAL”), or covariance estimates (“COVB”). The variable _NAME_
contains the parameter name for covariances, and the variable _SSE_
contains the objective function value for the parameter estimates. The variable _STATUS_
indicates whether the estimates have converged.
The data set produced by the OUTPUT statement contains statistics calculated for each observation. In addition, the data set contains the variables from the input data set and any ID variables that are specified in the ID statement.
The data set that is produced by the PROFDATA= option in the PROFILE statement contains statistics that are calculated during parameter profiling. The variable _TYPE_
denotes whether the observation is from a profiling task (set as PROF) or jackknife resampling task (set as JACK). The variable
_PROF_PARM_
refers to the name of the profiled parameter for profiling tasks. The variable _OBS_INF_MEAS_
is the observation index of the deleted observation for jackknife resampling tasks, the variable _SSE_
stands for the sum of squared errors, and the variables _L_STAT_
and _W_STAT_
contain the likelihood ratio pivotal statistic and the Wald pivotal statistic, respectively. The rest of the variables are
the parameter estimates. Missing values for _SSE_
indicate that the least squares fit for that particular profiling or jackknife task does not converge.
The data set that is produced by the BOOTDATA= option in the BOOTSTRAP statement contains statistics that are calculated during bootstrap resampling and estimation. The variable _REPLICATE_
indexes the bootstrap sample data sets. The variable _SEED_
refers to the initial seed value that is used in obtaining each sample data set, the variable _SSE_
stands for the sum of squared errors, and the rest of the variables are the parameter estimates for the model that is fit
to the bootstrap sample data set. Missing values for _SSE_
indicate that the least squares fit for that particular bootstrap sample data set does not converge.