OUTPUT Statement
 OUTPUT < OUT=SASdataset > keyword=names
<
... keyword=names > < / option > ;
The OUTPUT statement creates a new SAS data set that saves
diagnostic measures calculated after fitting the model.
At least one specification of the form
keyword=names is required.
All the variables in the original data set
are included in the new data set, along with
variables created in the OUTPUT statement.
These new variables contain the values of a variety of diagnostic
measures that are calculated for each observation in the data set.
If you want to create a permanent SAS data set, you must specify
a twolevel
name
(refer to SAS Language Reference: Concepts for more
information on permanent SAS data sets).
Details on the specifications in the
OUTPUT statement follow.
 keyword=names

specifies the statistics to include in the output data set and
provides names to the new variables that contain the statistics.
Specify a keyword for each desired statistic (see
the following list of keywords), an equal sign, and
the variable or variables to contain the statistic.
In the output data set, the first variable listed after a
keyword in the OUTPUT statement contains that statistic for
the first dependent variable listed in the MODEL statement;
the second variable contains the statistic for the second
dependent variable in the MODEL statement, and so on.
The list of variables following the equal sign can be shorter
than the list of dependent variables in the MODEL statement.
In this case, the procedure creates the new names in
order of the dependent variables in the MODEL statement.
See the "Examples" section.
The keywords allowed and the statistics they represent are
as follows:

COOKD

Cook's D influence statistic

COVRATIO

standard influence of observation on covariance of parameter estimates

DFFITS

standard influence of observation on predicted value

H

leverage, h_{i} = x_{i} (X'X)^{1} x_{i}'

LCL

lower bound of a 100(1  p)% confidence
interval for an individual prediction. The plevel is equal to the value
of the ALPHA= option in the OUTPUT statement or, if this option is not
specified, to the ALPHA= option in the PROC GLM statement. If neither of
these options is set then p=0.05 by default, resulting in the lower
bound for a 95% confidence interval.
The interval also depends on the variance of the error, as
well as the variance of the parameter estimates.
For the corresponding upper bound, see the UCL keyword.

LCLM

lower bound of a 100(1  p)% confidence interval for the
expected value (mean) of the predicted value. The plevel is equal
to the value of the ALPHA= option in the OUTPUT statement or, if this
option is not specified, to the ALPHA= option in the PROC GLM statement.
If neither of these options is set then p=0.05 by default, resulting
in the lower bound for a 95% confidence interval.
For the corresponding upper bound, see the UCLM keyword.

PREDICTED  P

predicted values

PRESS

residual for the ith observation that results from
dropping it and predicting it on the basis of all other observations.
This is the residual divided by (1  h_{i}) where h_{i} is the
leverage, defined previously.

RESIDUAL  R

residuals, calculated as ACTUAL  PREDICTED

RSTUDENT

a studentized residual with the current observation deleted

STDI

standard error of the individual predicted value

STDP

standard error of the mean predicted value

STDR

standard error of the residual

STUDENT

studentized residuals, the residual divided by its
standard error

UCL

upper bound of a 100(1  p)% confidence
interval for an individual prediction.
The plevel is equal to the value
of the ALPHA= option in the OUTPUT statement or, if this option is not
specified, to the ALPHA= option in the PROC GLM statement. If neither of
these options is set then p=0.05 by default, resulting in the upper
bound for a 95% confidence interval.
The interval also depends on the variance of the error, as
well as the variance of the parameter estimates.
For the corresponding lower bound, see the LCL keyword.

UCLM

upper bound of a 100(1  p)% confidence interval for the
expected value (mean) of the predicted value.
The plevel is equal to the value
of the ALPHA= option in the OUTPUT statement or, if this option is not
specified, to the ALPHA= option in the PROC GLM statement. If neither of
these options is set then p=0.05 by default, resulting in the upper
bound for a 95% confidence interval.
For the corresponding lower bound, see the LCLM keyword.
 OUT=SASdataset

gives the name of the new data set.
By default, the procedure uses the DATAn
convention to name the new data set.
The following option is available in the OUTPUT
statement and is specified after a slash(/):
 ALPHA=p

specifies the level of significance p for 100(1p)% confidence intervals.
By default, p is equal to the value of the ALPHA= option in the PROC GLM
statement or 0.05 if that option is not specified. You may use
values between 0 and 1.
See Chapter 3, "Introduction to Regression Procedures,"
and the "Influence Diagnostics" section in Chapter 55, "The REG Procedure,"
for details on the calculation of these
statistics.
The following statements show the syntax for creating
an output data set with a single dependent variable.
proc glm;
class a b;
model y=a b a*b;
output out=new p=yhat r=resid stdr=eresid;
run;
These statements create an output data set named new.
In addition to all the variables from the original data set,
new contains the variable yhat, with values that are predicted
values of the dependent variable y; the variable resid, with
values that are the residual values of y; and the variable eresid,
with values that are the standard errors of the residuals.
The following statements show a situation
with five dependent variables.
proc glm;
by group;
class a;
model y1y5=a x(a);
output out=pout predicted=py1py5;
run;
Data set pout contains five new variables, py1 through py5.
The values of py1 are the predicted values of y1; the values of py2
are the predicted values of y2; and so on.
For more information on the data set produced by the OUTPUT
statement, see the section "Output Data Sets".
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.