-@node REGRESSION, , ONEWAY, Statistics
+@node REGRESSION, ,ONEWAY, Statistics
+@comment node-name, next, previous, up
@section REGRESSION
The REGRESSION procedure fits linear models to data via least-squares
/VARIABLES=var_list
/DEPENDENT=var_list
/STATISTICS=@{ALL, DEFAULTS, R, COEFF, ANOVA, BCOV@}
- /EXPORT (filename)
+ /EXPORT ('file-name')
+ /SAVE=@{PRED, RESID@}
@end display
The @cmd{REGRESSION} procedure reads the active file and outputs
The VARIABLES subcommand, which is required, specifies the list of
variables to be analyzed. Keyword VARIABLES is required. The
DEPENDENT subcommand specifies the dependent variable of the linear
-model. The DEPENDENT subcommond is required. All variables listed in
+model. The DEPENDENT subcommand is required. All variables listed in
the VARIABLES subcommand, but not listed in the DEPENDENT subcommand,
are treated as explanatory variables in the linear model.
The covariance matrix for the estimated model coefficients.
@end table
+The SAVE subcommand causes PSPP to save the residuals or predicted
+values from the fitted
+model to the active file. PSPP will store the residuals in a variable
+called RES1 if no such variable exists, RES2 if RES1 already exists,
+RES3 if RES1 and RES2 already exist, etc. It will choose the name of
+the variable for the predicted values similarly, but with PRED as a
+prefix.
+
The EXPORT subcommand causes PSPP to write a C program containing
functions related to the model. One such function accepts values of
explanatory variables as arguments, and returns an estimate of the
value of the dependent variable. The generated program will also contain
functions that return prediction and confidence intervals related to
those new estimates. PSPP will write the program to the
-'filename' given by the user, and write declarations of functions
+'file-name' given by the user, and write declarations of functions
to a file called pspp_model_reg.h. The user can then compile the C
program and use it as part of another program. This subcommand is a
PSPP extension.
@node Examples, , Syntax, REGRESSION
@subsection Examples
-The following PSPP code will generate the default output, and save the
+The following PSPP syntax will generate the default output, save the
+predicted values and residuals to the active file, and save the
linear model in a program called ``model.c.''
@example
b 6.200189 -18.58219
end data.
list.
-regression /variables=v0 v1 v2 /statistics defaults /dependent=v2 /export (model.c) /method=enter.
+regression /variables=v0 v1 v2 /statistics defaults /dependent=v2
+ /export (model.c) /save pred resid /method=enter.
@end example
The file pspp_model_reg.h contains these declarations: