
ADJDFE=SOURCE
ADJDFE=ROW

specifies how denominator degrees of freedom are determined when pvalues and confidence limits are adjusted for multiple comparisons with the ADJUST= option. When you do not specify the ADJDFE= option, or when you specify ADJDFE=SOURCE, the denominator degrees of freedom
for multiplicityadjusted results are the denominator degrees of freedom for the final effect listed in the ESTIMATE statement
from the “Type III Tests of Fixed Effects” table.
The ADJDFE=ROW setting is useful if you want multiplicity adjustments to take into account that denominator degrees of freedom
are not constant across estimates. This can be the case, for example, when the DDFM=SATTERTHWAITE or DDFM=KENWARDROGER degreesoffreedom method is in effect.

ADJUST=BON
ADJUST=SCHEFFE
ADJUST=SIDAK
ADJUST=SIMULATE<(simoptions)>
ADJUST=T

requests a multiple comparison adjustment for the pvalues and confidence limits for the estimates. The adjusted quantities are produced in addition to the unadjusted quantities. Adjusted
confidence limits are produced if the CL or ALPHA= option is in effect. For a description of the adjustments, see Chapter 42: The GLM Procedure, and Chapter 61: The MULTTEST Procedure, and the documentation for the ADJUST= option in the LSMEANS statement. The ADJUST= option is ignored for generalized logit models.
If the STEPDOWN option is in effect, the pvalues are further adjusted in a stepdown fashion.

ALPHA=number

requests that a ttype confidence interval be constructed with confidence level 1 – number. The value of number must be between 0 and 1; the default is 0.05. If DDFM=NONE and you do not specify degrees of freedom with the DF= option, PROC GLIMMIX uses infinite degrees of freedom, essentially computing a z interval.

BYCATEGORY
BYCAT

requests that in models for nominal data (generalized logit models) estimates be reported separately for each category. In contrast to the BYCATEGORY option in the CONTRAST statement, an ESTIMATE statement in a generalized logit model does not distribute coefficients by response category, because
ESTIMATE statements always correspond to single rows of the matrix.
For example, assume that the response variable Style
is multinomial with three (unordered) categories. The following GLIMMIX statements fit a generalized logit model relating
the preferred style of instruction to school and educational program effects:
proc glimmix data=school;
class School Program;
model Style(order=data) = School Program / s ddfm=none
dist=multinomial link=glogit;
freq Count;
estimate 'School 1 vs. 2' school 1 1 / bycat;
estimate 'School 1 vs. 2' school 1 1;
run;
The first ESTIMATE statement compares school effects separately for each nonredundant category. The second ESTIMATE statement
compares the school effects for the first nonreference category.
The BYCATEGORY option has no effect unless your model is a generalized (mixed) logit model.

CL

requests that ttype confidence limits be constructed. If DDFM=NONE and you do not specify degrees of freedom with the DF= option, PROC GLIMMIX uses infinite degrees of freedom, essentially computing a z interval. The confidence level is 0.95 by default. These intervals are adjusted for multiplicity when you specify the ADJUST=
option.

DF=number

specifies the degrees of freedom for the t test and confidence limits. The default is the denominator degrees of freedom taken from the “Type III Tests of Fixed Effects” table and corresponds to the final effect you list in the ESTIMATE statement.

DIVISOR=valuelist

specifies a list of values by which to divide the coefficients so that fractional coefficients can be entered as integer numerators. If you do not specify valuelist, a default value of 1.0 is assumed. Missing values in the valuelist are converted to 1.0.
If the number of elements in valuelist exceeds the number of rows of the estimate, the extra values are ignored. If the number of elements in valuelist is less than the number of rows of the estimate, the last value in valuelist is copied forward.
If you specify a rowspecific divisor as part of the specification of the estimate row, this value multiplies the corresponding
divisor implied by the valuelist. For example, the following statement divides the coefficients in the first row by 8, and the coefficients in the third and
fourth row by 3:
estimate 'One vs. two' A 2 2 (divisor=2),
'One vs. three' A 1 0 1 ,
'One vs. four' A 3 0 0 3 ,
'One vs. five' A 1 0 0 0 1 / divisor=4,.,3;
Coefficients in the second row are not altered.

E

requests that the matrix coefficients be displayed.

EXP

requests exponentiation of the estimate. When you model data with the logit, cumulative logit, or generalized logit link functions, and the estimate represents a log
odds ratio or log cumulative odds ratio, the EXP option produces an odds ratio. See Odds and Odds Ratio Estimation for important details about the computation and interpretation of odds and odds ratio results with the GLIMMIX procedure.
If you specify the CL or ALPHA= option, the (adjusted) confidence bounds are also exponentiated.

GROUP coeffs

sets up randomeffect contrasts between different groups when a GROUP= variable appears in the RANDOM statement. By default, ESTIMATE statement coefficients on random effects are distributed equally across groups. If you enter
a multirow estimate, you can also enter multiple rows for the GROUP coefficients. If the number of GROUP coefficients is less
than the number of contrasts in the ESTIMATE statement, the GLIMMIX procedure cycles through the GROUP coefficients. For example,
the following two statements are equivalent:
estimate 'Trt 1 vs 2 @ x=0.4' trt 1 1 0  x 0.4,
'Trt 1 vs 3 @ x=0.4' trt 1 0 1  x 0.4,
'Trt 1 vs 2 @ x=0.5' trt 1 1 0  x 0.5,
'Trt 1 vs 3 @ x=0.5' trt 1 0 1  x 0.5 /
group 1 1, 1 0 1, 1 1, 1 0 1;
estimate 'Trt 1 vs 2 @ x=0.4' trt 1 1 0  x 0.4,
'Trt 1 vs 3 @ x=0.4' trt 1 0 1  x 0.4,
'Trt 1 vs 2 @ x=0.5' trt 1 1 0  x 0.5,
'Trt 1 vs 3 @ x=0.5' trt 1 0 1  x 0.5 /
group 1 1, 1 0 1;

ILINK

requests that the estimate and its standard error are also reported on the scale of the mean (the inverse linked scale). PROC GLIMMIX computes the value on the mean scale by applying the inverse
link to the estimate. The interpretation of this quantity depends on the fixedeffect values and randomeffect values specified in your ESTIMATE statement and on the link function. In a model for binary data with logit link, for example, the
following statements compute
where and are the fixedeffects solutions associated with the first two levels of the classification effect A
:
proc glimmix;
class A;
model y = A / dist=binary link=logit;
estimate 'A one vs. two' A 1 1 / ilink;
run;
This quantity is not the difference of the probabilities associated with the two levels,
The standard error of the inversely linked estimate is based on the delta method. If you also specify the CL option, the GLIMMIX procedure computes confidence limits for the estimate on the mean scale. In multinomial models for nominal
data, the limits are obtained by the delta method. In other models they are obtained from the inverse link transformation
of the confidence limits for the estimate. The ILINK option is specific to an ESTIMATE statement.

LOWER
LOWERTAILED

requests that the pvalue for the t test be based only on values less than the test statistic. A twotailed test is the default. A lowertailed confidence limit is also produced
if you specify the CL or ALPHA= option.
Note that for ADJUST=SCHEFFE the onesided adjusted confidence intervals and onesided adjusted pvalues are the same as the corresponding twosided statistics, because this adjustment is based on only the right tail of
the F distribution.

SINGULAR=number

tunes the estimability checking as documented for the CONTRAST statement.

STEPDOWN<(stepdownoptions)>

requests that multiplicity adjustments for the pvalues of estimates be further adjusted in a stepdown fashion. Stepdown methods increase the power of multiple testing
procedures by taking advantage of the fact that a pvalue will never be declared significant unless all smaller pvalues are also declared significant. Note that the STEPDOWN adjustment combined with ADJUST=BON corresponds to the methods of Holm (1979) and “Method 2” of Shaffer (1986); this is the default. Using stepdownadjusted pvalues combined with ADJUST=SIMULATE corresponds to the method of Westfall (1997).
If the degreesoffreedom method is DDFM=KENWARDROGER or DDFM=SATTERTHWAITE, then stepdownadjusted pvalues are produced only if the ADJDFE=ROW option is in effect.
Also, the STEPDOWN option affects only pvalues, not confidence limits. For ADJUST=SIMULATE, the generalized least squares hybrid approach of Westfall (1997) is employed to increase Monte Carlo accuracy.
You can specify the following stepdownoptions in parentheses after the STEPDOWN option.

MAXTIME=n

specifies the time (in seconds) to spend computing the maximal logically consistent sequential subsets of equality hypotheses
for TYPE=LOGICAL. The default is MAXTIME=60. If the MAXTIME value is exceeded, the adjusted tests are not computed. When this
occurs, you can try increasing the MAXTIME value. However, note that there are common multiple comparisons problems for which
this computation requires a huge amount of time—for example, all pairwise comparisons between more than 10 groups. In such
cases, try to use TYPE=FREE (the default) or TYPE=LOGICAL(n) for small n.

ORDER=PVALUE
ORDER=ROWS

specifies the order in which the stepdown tests are performed. ORDER=PVALUE is the default, with estimates being declared
significant only if all estimates with smaller (unadjusted) pvalues are significant. If you specify ORDER=ROWS, then significances are evaluated in the order in which they are specified
in the syntax.

REPORT

specifies that a report on the stepdown adjustment be displayed, including a listing of the sequential subsets (Westfall,
1997) and, for ADJUST=SIMULATE, the stepdown simulation results.

TYPE=LOGICAL<(n)>
TYPE=FREE

If you specify TYPE=LOGICAL, the stepdown adjustments are computed by using maximal logically consistent sequential subsets
of equality hypotheses (Shaffer, 1986; Westfall, 1997). Alternatively, for TYPE=FREE, sequential subsets are computed ignoring logical constraints. The TYPE=FREE results are more
conservative than those for TYPE=LOGICAL, but they can be much more efficient to produce for many estimates. For example,
it is not feasible to take logical constraints between all pairwise comparisons of more than about 10 groups. For this reason,
TYPE=FREE is the default.
However, you can reduce the computational complexity of taking logical constraints into account by limiting the depth of the
search tree used to compute them, specifying the optional depth parameter as a number n in parentheses after TYPE=LOGICAL. As with TYPE=FREE, results for TYPE=LOGICAL(n) are conservative relative to the true TYPE=LOGICAL results, but even for TYPE=LOGICAL(0) they can be appreciably less conservative
than TYPE=FREE and they are computationally feasible for much larger numbers of estimates. If you do not specify n or if n = –1, the full search tree is used.

SUBJECT coeffs

sets up randomeffect contrasts between different subjects when a SUBJECT= variable appears in the RANDOM statement. By default, ESTIMATE statement coefficients on random effects are distributed equally across subjects. Listing
subject coefficients for an ESTIMATE statement with multiple rows follows the same rules as for GROUP coefficients.

UPPER
UPPERTAILED

requests that the pvalue for the t test be based only on values greater than the test statistic. A twotailed test is the default. An uppertailed confidence limit is also produced
if you specify the CL or ALPHA= option.
Note that for ADJUST=SCHEFFE the onesided adjusted confidence intervals and onesided adjusted pvalues are the same as the corresponding twosided statistics, because this adjustment is based on only the right tail of
the F distribution.