The GAM Procedure

OUTPUT Statement

  • OUTPUT OUT= SAS-data-set <keyword<=prefix> …keyword<=prefix>>;

The OUTPUT statement creates a new SAS data set that contains diagnostic measures calculated after fitting the model.

All the variables in the original data set are included in the new data set, along with new variables that are created with keywords specified in the OUTPUT statement. These new variables contain the values of a variety of statistics and diagnostic measures that are calculated for each observation in the data set. If no keywords are present, the OUT=  data set contains only the original data set and predicted values. The predicted values include the linear predictor for the response and the prediction for each smoothing term in the model. When you specify a distribution family with the DIST=  or LINK=  option in the MODEL statement, predicted response values after applying the inverse link function are also included. Predicted values are computed for observations that have missing response values, nonmissing values in the explanatory variables, and values of the smoothing variables that are within the smoothing ranges of the fitted model.

You can specify the following options in the OUTPUT statement:

OUT=SAS-data-set

specifies the name of the new data set to contain the diagnostic measures. This option is required.

keyword <=prefix>

specifies the statistics to include in the output data set. The keywords and the statistics they represent are as follows:

PREDICTED

predicted values for each smoothing component and overall predicted values on the response scale at design points. The prediction for each spline or loess term is only for the nonlinear component of each smoother.

LINP

linear prediction values on the link scale at design points

UCLM

upper confidence limits for each predicted smoothing component

LCLM

lower confidence limits for each predicted smoothing component

ADIAG

diagonal element of the hat matrix associated with the observation for each smoothing spline component

RESIDUAL

residual standardized by its weights

STD

standard deviation of the prediction for each smoothing component

ALL

all statistics in this list

The names of the new variables that contain the statistics are formed by concatenating a prefix and the corresponding variable names. If you do not specify a prefix, the names are formed by using default prefixes listed in the following table:

Keyword Prefix
PRED P_
LINP LINP_
UCLM UCLM_
LCLM LCLM_
ADIAG ADIAG_
RESID R_
STD STD_ (for spline)
STDP_ (for loess)

For example, suppose that you have a dependent variable y and an independent smoothing variable x. If you specify the keywords PRED=MyP_ and ADIAG=MyA_, the new variables in the output SAS data set are MyP_y, MyP_x, and MyA_x. If you specify the keywords PRED and ADIAG without prefixes, the new variables are P_y, P_x, and ADIAG_x.

Last updated: December 09, 2022