PROC GLM Features
The following list summarizes the features in PROC GLM:
- PROC GLM enables you to specify any degree of interaction
(crossed effects) and nested effects.
It also provides for polynomial, continuous-by-class,
and continuous-nesting-class effects.
- Through the concept of estimability, the GLM procedure can provide tests
of hypotheses for the effects of a linear model regardless
of the number of missing cells or the extent of confounding.
PROC GLM displays the Sum of Squares (SS) associated with each
hypothesis tested and, upon request, the form of the
estimable functions employed in the test.
PROC GLM can produce the general form of all estimable functions.
- The REPEATED statement enables you to specify effects
in the model that represent repeated measurements on
the same experimental unit for the same response,
providing both univariate and multivariate
tests of hypotheses.
- The RANDOM statement enables you to specify random effects
in the model; expected mean squares are produced for each
Type I, Type II, Type III, Type IV, and contrast mean
square used in the analysis.
Upon request, F tests using appropriate mean
squares or linear combinations of mean squares as
error terms are performed.
- The ESTIMATE statement enables you to specify an L
vector for estimating a linear function of the parameters
- The CONTRAST statement enables you to specify a contrast
vector or matrix for testing the hypothesis that
. When specified, the contrasts are also incorporated into
analyses using the MANOVA and REPEATED statements.
- The MANOVA statement enables you to specify both the
hypothesis effects and the error effect to use for a
multivariate analysis of variance.
- PROC GLM can create an output data set containing the input
dataset in addition to predicted values, residuals, and
other diagnostic measures.
- PROC GLM can be used interactively.
After specifying and running a model, a variety of
statements can be executed without recomputing
the model parameters or sums of squares.
- For analysis involving multiple dependent variables but not the
MANOVA or REPEATED statements, a missing value in one dependent
variable does not eliminate the observation from the analysis
for other dependent variables.
PROC GLM automatically groups together those variables
that have the same pattern of missing values
within the data set or within a BY group.
This ensures that the analysis for each dependent
variable brings into use all possible observations.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.