
ADJDFE=SOURCE  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 LSmean effect in 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 DDFM=SATTERTHWAITE or DDFM=KENWARDROGER is specified in the MODEL statement.

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

requests a multiple comparison adjustment for the pvalues and
confidence limits for the LSmean estimates. The adjusted quantities are produced in addition to the unadjusted pvalues and confidence limits. Adjusted confidence limits are produced if the CL or ALPHA= option is in effect. For a description of the adjustments, see Chapter 44: The GLM Procedure, and Chapter 65: The MULTTEST Procedure, as well as the documentation for the ADJUST= option in the LSMEANS statement.
Note that not all adjustment methods of the LSMEANS statement are available for the LSMESTIMATE statement. Multiplicity adjustments in the LSMEANS statement are designed specifically for differences of least squares means.
If you specify the STEPDOWN option, the pvalues are further adjusted in a stepdown fashion.

ALPHA=number

requests that a ttype confidence interval be constructed for
each of the LSmeans with confidence level 1 – number. The value of number must be between 0 and 1; the default is 0.05.

AT variable=value
AT (variablelist)=(valuelist)
AT MEANS

enables you to modify the values of the covariates used in computing
LSmeans. See the AT option in the LSMEANS statement for details.

BYLEVEL

requests that PROC GLIMMIX compute separate margins for each level of
the LSMEANS effect.
The standard LSmeans have equal coefficients across classification effects. The BYLEVEL option changes these coefficients
to be proportional to the observed margins. This adjustment is reasonable when you want your inferences to apply to a population
that is not necessarily balanced but has the margins observed in the input data set. In this case, the resulting LSmeans
are actually equal to raw means for fixedeffects models and certain balanced randomeffects models, but their estimated standard
errors account for the covariance structure that you have specified. If a WEIGHT statement is specified, PROC GLIMMIX uses weighted margins to construct the LSmeans coefficients.
If the AT option is specified, the BYLEVEL option disables it.

CHISQ

requests that chisquare tests be performed in addition to
F tests, when you request an F test with the FTEST option.

CL

requests that ttype confidence limits be constructed for each
of the LSmeans.
If DDFM=NONE, then PROC GLIMMIX uses infinite degrees of freedom for this test, essentially computing a z interval. The confidence level is 0.95 by default; this can be changed with the ALPHA= option.

CORR

displays the estimated correlation matrix of the linear combination of
the least squares means.

COV

displays the estimated covariance matrix of the linear combination of
the least squares means.

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 corresponding to the LSmeans effect.

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 carried forward.
If you specify a rowspecific divisor as part of the specification of the estimate row, this value multiplies the corresponding
value in 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:
lsmestimate A 'One vs. two' 8 8 divisor=2,
'One vs. three' 1 0 1 ,
'One vs. four' 3 0 0 3 ,
'One vs. five' 3 0 0 0 3 / divisor=4,.,3;
Coefficients in the second row are not altered.

E

requests that the coefficients of the estimable function be
displayed. These are the coefficients that apply to the fixedeffect parameter estimates. The E option displays the coefficients
that you would need to enter in an equivalent ESTIMATE statement.

ELSM

requests that the matrix coefficients be
displayed. These are the coefficients that apply to the LSmeans. This option is useful to ensure that you assigned the coefficients
correctly to the LSmeans.

EXP

requests exponentiation of the least squares means estimate.
When you model data with the logit link function and the estimate represents a log odds ratio, the EXP option produces an
odds ratio. See the section Odds and Odds Ratio Estimation for important details concerning 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 limits for the estimate are also exponentiated.

FTEST<(jointtestoptions)>
JOINT<(jointtestoptions)>

produces an F test that jointly tests the rows of the
LSMESTIMATE against zero. If the LOWER or UPPER options are in effect or if you specify boundary values with the BOUNDS= suboption,
the GLIMMIX procedure computes a simulationbased pvalue for the constrained joint test. For more information about these simulationbased pvalues, see the section Joint Hypothesis Tests with Complex Alternatives, the ChiBarSquare Statistic in Chapter 19: Shared Concepts and Topics. You can specify the following jointtestoptions in parentheses:

ACC=

specifies the accuracy radius for determining the necessary sample size in the simulationbased approach of Silvapulle and
Sen (2004) for tests with order restrictions. The value of must be strictly between 0 and 1; the default value is 0.005.

BOUNDS=valuelist

specifies boundary values for the estimable linear function. The null value of the hypothesis is always zero. If you specify
a positive boundary value z, the hypotheses are vs. with the added constraint that . The same is true for negative boundary values. The alternative hypothesis is then subject to the constraint . If you specify a missing value, the hypothesis is assumed to be twosided. The BOUNDS option enables you to specify sets
of one and twosided joint hypotheses. If all values in valuelist are set to missing, the procedure performs a simulationbased pvalue calculation for a twosided test.

EPS=

specifies the accuracy confidence level for determining the necessary sample size in the simulationbased approach of Silvapulle
and Sen (2004) for F tests with order restrictions. The value of must be strictly between 0 and 1; the default value is 0.01.

LABEL='label'

enables you to assign a label to the joint test that identifies the results in the “LSMFtest” table. If you do not specify a label, the first nondefault label for the LSMESTIMATE rows is used to label the joint test.

NSAMP=n

specifies the number of samples for the simulationbased method of Silvapulle and Sen (2004). If n is not specified, it is constructed from the values of the ALPHA=, the ACC=, and the EPS= options. With the default values for , , and (0.005, 0.01, and 0.05, respectively), NSAMP=12,604 by default.

ILINK

requests that the estimate and its standard error also be 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 coefficients that are specified in your LSMESTIMATE
statement and the link function. For example, in a model for binary data with a logit link, the following LSMESTIMATE statement
computes
where and are the least squares means associated with the first two levels of the classification effect A
:
proc glimmix;
class A;
model y = A / dist=binary link=logit;
lsmestimate A 1 1 / ilink;
run;
The quantity q 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 or ALPHA= option, the GLIMMIX procedure computes confidence intervals for the inversely linked estimate. These intervals are obtained
by applying the inverse link to the confidence intervals on the linked scale.

JOINT<(jointtestoptions)>

is an alias for the FTEST option.

LOWER
LOWERTAILED

requests that the pvalue for the t test be based
only on values that are 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.
If you request an F test with the FTEST option, then a onesided lefttailed order restriction is applied to all estimable functions, and the corresponding chibarsquare
statistic of Silvapulle and Sen (2004) is computed in addition to the twosided, standard F or chisquare statistic. See the description of the FTEST option for information about how to control the computation of the simulationbased chibarsquare statistic.

OBSMARGINS
OM

specifies a potentially different weighting scheme for the
computation of LSmeans coefficients. The standard LSmeans have equal coefficients across classification effects; however,
the OM option changes these coefficients to be proportional to those found in the input data set. See the OBSMARGINS option in the LSMEANS statement for further details.

SINGULAR=number

tunes the estimability checking as documented for the
CONTRAST statement.

STEPDOWN<(stepdownoptions)>

requests that multiplicity adjustments for the
pvalues of LSmean 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 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:

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  ROWS

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

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)>  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, logical constraints are ignored when sequential subsets are computed. 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.

UPPER
UPPERTAILED

requests that the pvalue for the t test be based only on
values that are 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.
If you request a joint test with the FTEST option, then a onesided righttailed order restriction is applied to all estimable functions, and the corresponding chibarsquare
statistic of Silvapulle and Sen (2004) is computed in addition to the twosided, standard F or chisquare statistic. See the FTEST option for information about how to control the computation of the simulationbased chibarsquare statistic.