HELP GLM in the command area on the main SAS
Display Manager Window.
For further information, you might look at
Linear Models in SAS An overview of regression and analysis of variance procedures.
(from Univ. Michigan).
In the statements below, uppercase is used for keywords, lowercase for things you fill in. Variable names are no more than 8 chars. in length.
PROC ANOVA DATA=datasetname;
CLASS factorvars;
MODEL responsevar = factorvars; /* See below */
MEANS factorvars / BON /* Bonferroni t-tests,
g=r(r-1)/2 */
T or LSD /* Unprotected t-tests */
TUKEY /* Tukey studentized range */
SCHEFFE /* Scheffe contrasts */
ALPHA=pvalue /* default: 5% */
CLDIFF /* Confidence limits */
LINES /* Non-significant subsets */
; /* use LINES or CLDIFF */
PROC GLM DATA=datasetname;
CLASS factorvars;
e.g. CLASS A B SEX;
MODEL responsevar = factorvars
/ options ; /* Not needed yet */
RANDOM factorvars / TEST; /* If any random factors, list
then here (after MODEL) */
TEST H=effects E=effect; /* To specify an error term other
than the residual MS */
eg, TEST H=A B E=AB; /* 2-way design with A,B random */
MEANS factorvars / options ; /* same as for ANOVA */
LSMEANS factorvars /* Least squares & adjusted
means for ANCOVA */
/ STDERR /* .. and std errors */
PDIFF ; /* ... and p-values for diff */
CONTRAST 'label' factor weights ;
eg, CONTRAST 'Linear' SUGAR -3 -1 1 3 ;
CONTRAST 'Quad ' SUGAR 1 -1 -1 3 ;
ESTIMATE 'name' effect values... / options; /* Only with GLM */
The ESTIMATE statement constructs and tests linear combinations
(predicted values and contrasts) of the parameters.
eg, ESTIMATE 'A1 vs A2' A 1 -1 0 0 / divisor=2;
ESTIMATE 'A2 vs A3,4' A 0 2 -1 -1 / divisor=2;
OUTPUT OUT=datasetname P=fitvar /* Predicted values */
R=residvar ; /* Residuals */
REPEATED factorname levels(levelvalue) contrast;
MODEL Y = X1; /* Simple linear regression */
MODEL Y = X1 X2 X3; /* Multiple regression */
MODEL Y = X1 X1*X1 X1*X1*X1; /* Polynomial regression */
MODEL Y = A; /* One way anova */
MODEL Y = A B; /* Two-way, main effects only */
MODEL Y = A B A*B; /* Two-way, factorial with
interaction */
MODEL Y = A | B; /* Two-way, same as above */
MODEL Y = A B C A*B A*C /* Three-way, complete */
B*C A*B*C; /* factorial */
MODEL Y = A | B | C; /* The same, using "|" notation */
CONTRAST 'linear' DELAY -1 0 1;
CONTRAST 'quad' DELAY 1 -2 1;
Coefficients, c(i)
r Trend X=1 2 3 4 5 6 7 sum c(i)**2
---------------------------------------------------------
3 Linear -1 0 1 2
Quad 1 -2 1 6
--------------------------------------------------
4 Linear -3 -1 1 3 20
Quad 1 -1 -1 1 4
Cubic -1 3 -3 1 20
--------------------------------------------------
5 Linear -2 -1 0 1 2 10
Quad 2 -1 -2 -1 2 14
Cubic -1 2 0 -2 1 10
Quartic 1 -4 6 -4 1 70
--------------------------------------------------
6 Linear -5 -3 -1 1 3 5 70
Quad 5 -1 -4 -4 1 5 84
Cubic -5 7 4 -4 -7 5 180
Quartic 1 -3 2 2 -3 1 28
--------------------------------------------------
7 Linear -3 -2 -1 0 1 2 3 28
Quad 5 0 -3 -4 -3 0 5 84
Cubic -1 1 1 0 -1 -1 1 6
Quartic 3 -7 1 6 1 -7 3 154
--------------------------------------------------
8 Linear -7 -5 -3 -1 1 3 5 7 168
Quad 7 1 -3 -5 -5 -3 1 7 168
Cubic -7 5 7 3 -3 -7 -5 7 264
Quartic 7 -13 -3 9 9 -3 -13 7 616
--------------------------------------------------
9 Linear -4 -3 -2 -1 0 1 2 3 4 60
Quad 28 7 -8 -17 -20 -17 -8 7 28 2772
Cubic -14 7 13 9 0 -9 -13 -7 14 990
Quartic 14 -21 -11 9 18 9 -11 -21 14 2002
--------------------------------------------------