SPSS® 12.0 Command Syntax Reference

Example
MODEL PROGRAM A=1 B=1.
COMPUTE PRED=EXP(A+B*T)/(1+EXP(A+B*T)).
COMPUTE LOSS=-W*(Y*LN(PRED)+(1-Y)*LN(1-PRED)).
DERIVATIVES.
COMPUTE D.A=PRED/(1+EXP(A+B*T)).
COMPUTE D.B=T*PRED/(1+EXP(A+B*T)).
COMPUTE D.A=(-W*(Y/PRED - (1-Y)/(1-PRED)) * D.A).
COMPUTE D.B=(-W*(Y/PRED - (1-Y)/(1-PRED)) * D.B).
NLR 1059
CNLR Y /LOSS=LOSS.
• The second COMPUTE command in the model program computes the loss functions and
stores its values in the variable LOSS, which is then specified on the LOSS subcommand.
• Because derivatives are supplied in the derivatives program, the derivatives of all parameters
are computed with respect to the loss function, rather than the predicted value.
BOOTSTRAP Subcommand
BOOTSTRAP provides bootstrap estimates of the parameter standard errors, confidence intervals,
and correlations. BOOTSTRAP can be used only with CNLR; it cannot be used with NLR.
Bootstrapping is a way of estimating the standard error of a statistic, using repeated
samples from the original data set. This is done by sampling with replacement to get samples
of the same size as the original data set.
• The minimum specification is the subcommand keyword. Optionally, specify the number
of samples to use for generating bootstrap results.
• By default, BOOTSTRAP generates bootstrap results based on 10*p*(p+1)/2 samples,
where p is the number of parameters. That is, 10 samples are drawn for each statistic
(standard error or correlation) to be calculated.
• When BOOTSTRAP is used, the nonlinear equation is estimated for each sample. The standard
error of each parameter estimate is then calculated as the standard deviation of the
bootstrapped estimates. Parameter values from the original data are used as starting
values for each bootstrap sample. Even so, bootstrapping is computationally expensive.
• If the OUTFILE subcommand is specified, a case is written to the output file for each bootstrap
sample. The first case in the file will be the actual parameter estimates, followed by
the bootstrap samples. After the first case is eliminated (using SELECT IF), other SPSS
procedures (such as FREQUENCIES) can be used to examine the bootstrap distribution.
Example
MODEL PROGRAM A=.5 B=1.6.
COMPUTE PSTOP=A*SPEED**B.
CNLR STOP /BOOTSTRAP /OUTFILE=PARAM.
GET FILE=PARAM.
LIST.
COMPUTE ID=$CASENUM.
SELECT IF (ID > 1).
FREQUENCIES A B /FORMAT=NOTABLE /HISTOGRAM.