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regpred(vals [, silent:T]), vals a REAL vector or matrix.

Keywords: glm, regression
regpred(vals) computes the fitted (predicted) value, the standard error
of estimation, and the standard error of prediction for the current
regression model when the X variables have values given by the REAL
vector or matrix vals.

When there is only 1 variate in the model, vals is a scalar or a vector
and the estimates and standard errors are computed for each element of

When the number of variates in the model is nvars > 1, vals must either
be a vector of length nvars containing data for a single case, or a m by
nvars matrix containing data for m cases.  In the latter case, each
component of the result is a vector of length m.  For example, after
regress("y=x1+x2+x3"), regpred(hconcat(x1,x2,x3)) computes the predicted
values and standard errors for all cases in the data set.

The result is a structure with components 'estimate', 'SEest', and

When the error degrees of freedom are 0, all standard errors are set to

Caution: After anova(), manova() and regress(), standard errors are
computed using the final error mean square in the model.  This may not
be appropriate with mixed models, including split plot designs.

regpred(vals, silent:T) does the same except certain advisory messages
are suppressed.  The default value of 'silent' is False unless the value
of option' 'warnings' is False.

You can also use keyword phrases estimate:F, seest:F, sepred:F and n:N.
See glmpred() for details.

You can use regpred() after any GLM command as long as there are no
factors in the model.  The output has no SEpred component except after
regress(), anova() or manova() or their weighted versions.

After anova(), manova() and regress(), regpred(vals) is equivalent to
glmpred(vals,sepred:T).  After other GLM commands, regpred(vals) is
equivalent to glmpred(vals).

See also topics regress(), anova(), glmpred(), predtable(), glmtable(),
modelinfo(), popmodel(), pushmodel(), 'glm', yhat().

Gary Oehlert 2003-01-15