
ADJDFE=ROW
ADJDFE=SOURCE

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. When you specify ADJDFE=ROW, the denominator degrees of freedom for multiplicityadjusted results correspond to the
degrees of freedom that are displayed in the DF
column of the “Differences of Least Squares Means” table.
The ADJDFE=ROW setting is particularly useful if you want multiplicity adjustments to take into account that denominator degrees
of freedom are not constant across LSmean differences.
In oneway models with heterogeneous variance, combining certain ADJUST= options with the ADJDFE=ROW option corresponds to particular methods of performing multiplicity adjustments in the presence
of heteroscedasticity. For example, the following statements fit a heteroscedastic oneway model and perform Dunnett’s T3
method (Dunnett, 1980), which is based on the studentized maximum modulus (ADJUST=SMM):
proc glimmix;
class A;
model y = A / ddfm=satterth;
random _residual_ / group=A;
lsmeans A / adjust=smm adjdfe=row;
run;
If you combine the ADJDFE=ROW option with ADJUST=SIDAK, the multiplicity adjustment corresponds to the T2 method of Tamhane (1979), and ADJUST=TUKEY corresponds to the method of GamesHowell (Games and Howell, 1976). Note that ADJUST=TUKEY gives the exact results for the case of fractional degrees of freedom in the oneway model, but it does not take into
account that the degrees of freedom are subject to variability. A more conservative method, such as ADJUST=SMM, might protect
the overall error rate better.
Unless the ADJUST= option is specified in the LSMEANS statement, the ADJDFE= option has no effect. The option is not supported by the procedures
that perform chisquarebased inference (GENMOD, LOGISTIC, PHREG, and SURVEYLOGISTIC).

ADJUST=BON
ADJUST=DUNNETT
ADJUST=NELSON
ADJUST=SCHEFFE
ADJUST=SIDAK
ADJUST=SIMULATE<(simoptions)>
ADJUST=SMM  GT2
ADJUST=TUKEY

requests a multiple comparison adjustment for the pvalues and confidence limits for the differences of LSmeans. The adjusted quantities are produced in addition to the unadjusted quantities.
By default, the procedure performs all pairwise differences. If you specify ADJUST=DUNNETT, the procedure analyzes all differences
with a control level. If you specify ADJUST=NELSON, ANOM differences are taken. The ADJUST= option implies the DIFF option.
The BON (Bonferroni) and SIDAK adjustments involve correction factors described in Chapter 42: The GLM Procedure, and Chapter 61: The MULTTEST Procedure,; also see Westfall and Young (1993) and Westfall et al. (1999). When you specify ADJUST=TUKEY and your data are unbalanced, the procedure uses the approximation described in Kramer (1956) and identifies the adjustment as “TukeyKramer” in the results. Similarly, when you specify ADJUST=DUNNETT or ADJUST=NELSON and the LSmeans are correlated, the procedure
uses the factoranalytic covariance approximation described in Hsu (1992) and identifies the adjustment in the results as “DunnettHsu” or “NelsonHsu,” respectively. The approximation derives an approximate “effective sample sizes” for which exact critical values are computed. Computing the exact adjusted pvalues and critical values for unbalanced designs can be computationally intensive, in particular for ADJUST=NELSON. A simulationbased
approach, as specified by the ADJUST=SIM option, while nondeterministic, can provide inferences that are sufficiently accurate
in much less time. The preceding references also describe the SCHEFFE and SMM adjustments.
Nelson’s adjustment applies only to the analysis of means (Ott, 1967; Nelson, 1982, 1991, 1993), where LSmeans are compared against an average LSmean. It does not apply to all pairwise differences of least squares
means. See the DIFF=ANOM option for more details regarding the analysis of means with the procedure.
The SIMULATE adjustment computes adjusted pvalues and confidence limits from the simulated distribution of the maximum or maximum absolute value of a multivariate t random vector. All covariance parameters, except the residual scale parameter, are fixed at their estimated values throughout
the simulation, potentially resulting in some underdispersion. The simulation estimates q, the true quantile, where is the confidence coefficient. The default is 0.05, and you can change this value with the ALPHA= option in the LSMEANS statement.
The number of samples is set so that the tail area for the simulated q is within of with % confidence. In equation form,
where is the simulated q and F is the true distribution function of the maximum; see Edwards and Berry (1987) for details. By default, = 0.005 and = 0.01, placing the tail area of within 0.005 of 0.95 with 99% confidence. You can specify the following simoptions in parentheses after the ADJUST=SIMULATE option:

ACC=value

specifies the target accuracy radius of a % confidence interval for the true probability content of the estimated quantile. The default value is ACC=0.005.

EPS=value

specifies the value for a % confidence interval for the true probability content of the estimated quantile. The default value for the accuracy confidence is 99%, which corresponds to EPS=0.01.

NSAMP=n

specifies the sample size for the simulation. By default, n is set based on the values of the target accuracy radius and accuracy confidence % for an interval for the true probability content of the estimated quantile. With the default values for , , and (0.005, 0.01, and 0.05, respectively), NSAMP=12,604 by default.

SEED=number

specifies an integer that is used to start the pseudorandom number generator for the simulation. If you do not specify a
seed, or specify a value less than or equal to zero, the seed is by default generated from reading the time of day from the
computer’s clock.

THREADS

specifies that the computational work for the simulation be divided into parallel threads, where the number of threads is
the value of the SAS system option CPUCOUNT=. For large simulations (as specified directly using the NSAMP= simoption or indirectly using the ACC= or EPS= simoptions), parallel processing can markedly speed up the computation of adjusted pvalues and confidence intervals. However, because the parallel processing has different pseudorandom number streams, the
precise results are different from the default ones, which are computed in sequence rather than in parallel. This option overrides
the SAS system option THREADS  NOTHREADS.

NOTHREADS

specifies that the computational work for the simulation be performed in sequence rather than in parallel. NOTHREADS is the
default. This option overrides the SAS system option THREADS  NOTHREADS.
If the STEPDOWN option is in effect, the pvalues are further adjusted in a stepdown fashion. For certain options and data, this adjustment is exact under an iid model for the dependent variable, in particular for the following:
The first case is a consequence of the nature of the successive stepdown hypotheses for comparisons with a control; the
second uses an extension of the maximum studentized range distribution appropriate for partition hypotheses (Royen, 1989). Finally, for STEPDOWN(TYPE=FREE), ADJUST=TUKEY employs the Royen (1989) extension in such a way that the resulting pvalues are conservative.

ALPHA=number

requests that a t type 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

modifies the values of the covariates that are used in computing LSmeans. By default, all covariate effects are set equal to their mean values for computation of standard LSmeans. The AT
option enables you to assign arbitrary values to the covariates. Additional columns in the output table indicate the values
of the covariates.
If there is an effect that contains two or more covariates, the AT option sets the effect equal to the product of the individual
means rather than the mean of the product (as with standard LSmeans calculations). The AT MEANS option sets covariates equal
to their mean values (as with standard LSmeans) and incorporates this adjustment to crossproducts of covariates.
As an example, consider the following statements:
class A;
model Y = A x1 x2 x1*x2;
lsmeans A;
lsmeans A / at means;
lsmeans A / at x1=1.2;
lsmeans A / at (x1 x2)=(1.2 0.3);
For the first two LSMEANS statements, the LSmeans coefficient for x1
is (the mean of x1
) and for x2
is (the mean of x2
). However, for the first LSMEANS statement, the coefficient for x1
*x2
is , but for the second LSMEANS statement, the coefficient is . The third LSMEANS statement sets the coefficient for x1
equal to 1.2 and leaves it at for x2
, and the final LSMEANS statement sets these values to 1.2 and 0.3, respectively.
Even if you specify a WEIGHT variable, the unweighted covariate means are used for the covariate coefficients if there is
no AT specification. If you specify the AT option, WEIGHT or FREQ variables are taken into account as follows. The weighted
covariate means are then used for the covariate coefficients for which no explicit AT values are given, or if you specify
AT MEANS. Observations that do not contribute to the analysis because of a missing dependent variable are included in computing
the covariate means. Use the E option in conjunction with the AT option to check that the modified LSmeans coefficients are the ones you want.
The AT option is disabled if you specify the BYLEVEL option.

BYLEVEL

requests that separate margins be computed 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, the procedure uses
weighted margins to construct the LSmeans coefficients.
If the AT option is specified, the BYLEVEL option disables it.

CL

requests that t type confidence limits be constructed for each of the LSmeans. The confidence level is 0.95 by default; this can be changed with the ALPHA= option. If you specify an ADJUST= option, then the confidence limits are adjusted for multiplicity. But if you also specify STEPDOWN, then only pvalues are stepdown adjusted, not the confidence limits.

CORR

displays the estimated correlation matrix of the least squares means as part of the “Least Squares Means” table.

COV

displays the estimated covariance matrix of the least squares means as part of the “Least Squares Means” table.

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” table that corresponds to the LSmeans effect. The option is not supported by the procedures that perform chisquarebased
inference (GENMOD, LOGISTIC, PHREG and SURVEYLOGISTIC).

DIFF<=difftype>
PDIFF<=difftype>

requests that differences of the LSmeans be displayed. You can use one of the following optional difftype values to specify which differences to produce:

ALL

requests all pairwise differences; this is the default.

ANOM

requests differences between each LSmean and the average LSmean, as in the analysis of means (Ott, 1967). The average is computed as a weighted mean of the LSmeans, the weights being inversely proportional to the diagonal entries
of the matrix. If LSmeans are nonestimable, this designbased weighted mean is replaced with an equally weighted mean. Note that
the ANOM procedure in SAS/QC software implements both tables and graphics for the analysis of means with a variety of response
types. For oneway designs and normal data with identity link, the DIFF=ANOM computations are equivalent to the results of
PROC ANOM. If the LSmeans being compared are uncorrelated, exact adjusted pvalues and critical values for confidence limits can be computed in the analysis of means; see Nelson (1982, 1991, 1993) and Guirguis and Tobias (2004) in addition to the documentation for the ADJUST=NELSON option.

CONTROL

requests differences with a control, which, by default, is the first valid level of each of the specified LSMEANS effects.
For example, suppose the effects A
and B
are classification variables, both of them have two levels 1 and 2, and the A
=1, B
=1 cell is missing. Unless the procedure supports a MISSING option in the CLASS statement and the option is in effect, the
following LSMEANS statement uses the level (1,2) of A
*B
as the control:
lsmeans A*B / diff=control;
Nevertheless, you can still specify a valid level as the control—for example, (2,1) of A
*B
. To specify which levels of the effects are the controls, list the quoted formatted values in parentheses after the CONTROL
keyword. For example, if the effects A
, B
, and C
are classification variables, each having two levels, 1 and 2, the following LSMEANS statement specifies the (1,2) level
of A
*B
and the (2,1) level of B
*C
as controls:
lsmeans A*B B*C / diff=control('1' '2' '2' '1');
For multiple effects, the results depend upon the order of the list, and so you should check the output to make sure that
the controls are correct.
Twotailed tests and confidence limits are associated with the CONTROL difftype. For onetailed results, use either the CONTROLL or CONTROLU difftype.

CONTROLL

tests whether the noncontrol levels are significantly smaller than the control; the upper confidence limits for the control
minus the noncontrol levels are considered to be infinity and are displayed as missing.

CONTROLU

tests whether the noncontrol levels are significantly larger than the control; the upper confidence limits for the noncontrol
levels minus the control are considered to be infinity and are displayed as missing.
If you want to perform multiple comparison adjustments on the differences of LSmeans, you must specify the ADJUST= option.
The differences of the LSmeans are displayed in a table titled “Differences of Least Squares Means.”

E

requests that the matrix coefficients for the LSMEANS effects be displayed.

EXP

requests exponentiation of the LSmeans or LSmean differences. 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. In proportional hazards model, the exponentiation
of the LSmean differences produces estimates of hazard ratios. If you specify the CL or ALPHA= option, the (adjusted) confidence bounds are also exponentiated.
The EXP option is supported only by PROC PHREG, PROC SURVEYPHREG, the procedures that support generalized linear modeling
(GENMOD, LOGISTIC, and SURVEYLOGISTIC), and PROC PLM when it is used to perform statistical analyses on item stores that are
created by these procedures.

ILINK

requests that estimates and their standard errors in the “Least Squares Means” table also be reported on the scale of the mean (the inverse linked scale). This enables you to obtain estimates of predicted probabilities and their standard errors in logistic
models, for example. The option is specific to an LSMEANS statement. If you also specify the CL option, the procedure computes confidence intervals for the predicted means by applying the inverse link transform to the
confidence limits on the linked (linear) scale. Standard errors on the inverse linked scale are computed by the delta method.
The ILINK option is supported only by the procedures that support generalized linear modeling (GENMOD, LOGISTIC and SURVEYLOGISTIC)
and by PROC PLM when it is used to perform statistical analyses on item stores that are created by these procedures.

LINES

presents results of comparisons between all pairs of least squares means by listing the means in descending order and indicating nonsignificant subsets by line segments beside the corresponding
LSmeans. When all differences have the same variance, these comparison lines are guaranteed to accurately reflect the inferences
that are based on the corresponding tests, which are made by comparing the respective pvalues to the value of the ALPHA= option (0.05 by default). However, equal variances might not be the case for differences between LSmeans. If the variances
are not all the same, then the comparison lines might be conservative, in the sense that if you base your inferences on the
lines alone, you will detect fewer significant differences than the tests indicate. If there are any such differences, the
procedure lists the pairs of means that are inferred to be significantly different by the tests but not by the comparison
lines. However, even though the variances in many cases are unequal, they are similar enough that the comparison lines accurately
reflect the test inferences.

MEANS  NOMEANS

determines whether to print the least squares means themselves. For most procedure, MEANS is the default behavior. For example, the NOMEANS option is the default for the PHREG procedure. You
can then use the MEANS option to produce the table of least squares means, if desired.

ODDSRATIO
OR

requests that LSmean differences (DIFF, ADJUST= options) are also reported in terms of odds ratios. The ODDSRATIO option is ignored unless you use either the logit, cumulative
logit, or generalized logit link function. If you specify the CL or ALPHA= option, confidence intervals for the odds ratios are also computed. These intervals are adjusted for multiplicity when you
specify the ADJUST= option.
The ODDSRATIO option is supported only by the procedures that support generalized linear modeling (GENMOD, LOGISTIC and SURVEYLOGISTIC)
and by PROC PLM when it is used to perform statistical analyses on item stores created by these procedures.

OBSMARGINS<=OMdataset>
OM<=OMdataset>

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 OMdataset. This adjustment is reasonable when you want your inferences to apply to a population that is not necessarily balanced but
has the margins that are observed in OMdataset.
By default, OMdataset is the same as the analysis data set. You can optionally specify another data set that describes the population for which
you want to make inferences. This data set must contain all model variables except for the dependent variable (which is ignored
if it is present). In addition, the levels of all CLASS variables must be the same as those that occur in the analysis data
set. If a level of a classification effect in the original data set is not present in the OMdataset, the LSmeans for that level are undefined. The corresponding rows of the LSMeans table are displayed as missing. Specifying
an OMdataset enables you to construct arbitrarily weighted LSmeans.
In computing the observed margins, the procedure uses all observations for which there are no missing or invalid independent
variables, including those for which there are missing dependent variables. Also, if you use a WEIGHT statement, the procedure
computes weighted margins to construct the LSmeans coefficients. If your data are balanced, the LSmeans are unchanged by
the OM option.
The BYLEVEL option modifies the observedmargins LSmeans. Instead of computing the margins across all of the OMdataset, the procedure computes separate margins for each level of the LSMEANS effect in question. 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.
You can use the E option in conjunction with either the OM or BYLEVEL option to verify that the modified LSmeans coefficients are the ones you want. It is possible that the modified LSmeans
are not estimable when the standard ones are estimable, or vice versa.

PDIFF

is the same as the DIFF option.

PLOT  PLOTS<=plotrequest<(options)>>
PLOT  PLOTS<=(plotrequest<(options)> <…plotrequest<(options)> >)>

requests that graphics related to least squares means be produced via ODS Graphics, provided that ODS Graphics is enabled and the plotrequest does not conflict with other options in the LSMEANS statement. For general information about ODS Graphics, see Chapter 21: Statistical Graphics Using ODS.
The available options and suboptions are as follows:

ALL

requests that the default plots that correspond to this LSMEANS statement be produced. The default plot depends on the options
in the statement.

ANOMPLOT
ANOM

requests an analysisofmeans display in which least squares means are compared to an average least squares mean. Least squares
mean ANOM plots are produced only for those model effects that are listed in LSMEANS statements and have options that do not
contradict with the display. For example, the following statements produce analysisofmean plots for effects A
and C
:
lsmeans A / diff=anom plot=anom;
lsmeans B / diff plot=anom;
lsmeans C / plot=anom;
The DIFF option in the second LSMEANS statement implies all pairwise differences.

BOXPLOT<boxplotoptions>

produces box plots of the distribution of the least squares mean or least squares mean differences across a posterior sample.
For example, this plot is available in procedures that support a Bayesian analysis through the BAYES statement.
A separate box is generated for each estimable function, and all boxes appear on a single graph by default. You can affect
the appearance of the box plot graph with the following options:

ORIENTATION=VERTICAL  HORIZONTAL
ORIENT=VERT  HORIZ

specifies the orientation of the boxes. The default is vertical orientation of the box plots.

NPANELPOS=number

specifies how to break the series of box plots across multiple panels. If the NPANELPOS option is not specified, or if number equals zero, then all box plots are displayed in a single graph; this is the default. If a negative number is specified,
then exactly up to of box plots are displayed per panel. If number is positive, then the number of boxes per panel is balanced to achieve small variation in the number of box plots per graph.

CONTROLPLOT


CONTROL

requests a display in which least squares means are visually compared against a reference level. These plots are produced
only for statements with options that are compatible with control differences. For example, the following statements produce
control plots for effects A
and C
:
lsmeans A / diff=control('1') plot=control;
lsmeans B / diff plot=control;
lsmeans C plot=control;
The DIFF option in the second LSMEANS statement implies all pairwise differences.

DIFFPLOT<(diffplotoptions)>
DIFFOGRAM<(diffplotoptions)>
DIFF<(diffplotoptions)>

requests a display of all pairwise least squares mean differences and their significance. The display is also known as a “meanmean scatter plot” when it is based on arithmetic means (Hsu, 1996; Hsu and Peruggia, 1994). For each comparison a line segment, centered at the LSmeans in the pair, is drawn. The length of the segment corresponds
to the projected width of a confidence interval for the least squares mean difference. Segments that fail to cross the 45degree
reference line correspond to significant least squares mean differences.
LSmean difference plots are produced only for statements with options that are compatible with the display. For example,
the following statements request differences against a control level for the A
effect, all pairwise differences for the B
effect, and the least squares means for the C
effect:
lsmeans A / diff=control('1') plot=diff;
lsmeans B / diff plot=diff;
lsmeans C plot=diff;
The DIFF= type in the first statement is incompatible with a display of all pairwise differences.
You can specify the following diffplotoptions:

ABS

determines the positioning of the line segments in the plot. This is the default diffplotoptions. When the ABS option is in effect, all line segments are shown on the same side of the reference line.

NOABS

determines the positioning of the line segments in the plot. The NOABS option separates comparisons according to the sign
of the difference.

CENTER

marks the center point for each comparison. This point corresponds to the intersection of two least squares means.

NOLINES

suppresses the display of the line segments that represent the confidence bounds for the differences of the least squares
means. The NOLINES option implies the CENTER option. The default is to draw line segments in the upper portion of the plot
area without marking the center point.

DISTPLOT<distplotoptions>
DIST<distplotoptions>

generates panels of histograms with a kernel density overlaid if the analysis has access to a set of posterior parameter estimates.
For example, this plot is available in procedures that support a Bayesian analysis through the BAYES statement. A separate
plot in each panel contains the results for each least squares mean or least squares mean differences. You can specify the
following distplotoptions in parentheses:

BOX  NOBOX

controls the display of a horizontal box plot of the estimable function’s distribution across the posterior sample below the
graph. The BOX option is enabled by default.

HIST  NOHIST

controls the display of the histogram of the estimable function’s distribution across the posterior sample. The HIST option
is enabled by default.

NORMAL  NONORMAL

controls the display of a normal density estimate on the graph. The NONORMAL option is enabled by default.

KERNEL  NOKERNEL

controls the display of a kernel density estimate on the graph. The KERNEL option is enabled by default.

NROWS=number

specifies the highest number of rows in a panel. The default is 3.

NCOLS=number

specifies the highest number of columns in a panel. The default is 3.

UNPACK

unpacks the panel into separate graphics.

MEANPLOT<(meanplotoptions)>

requests displays of the least squares means.
The following meanplotoptions control the display of the least squares means.

ASCENDING

displays the least squares means in ascending order. This option has no effect if means are displayed in separate plots.

CL

displays upper and lower confidence limits for the least squares means. By default, 95% limits are drawn. You can change the
confidence level with the ALPHA= option. Confidence limits are drawn by default if the CL option is specified in the LSMEANS statement.

CLBAND

displays confidence limits as bands. This option implies the JOIN option.

DESCENDING

displays the least squares means in descending order. This option has no effect if means are displayed in separate plots.

ILINK

requests that means (and confidence limits) be displayed on the inverse linked scale.

JOIN
CONNECT

connects the least squares means with lines. This option is implied by the CLBAND option. If the effect contains nested variables
and a SLICEBY= effect contains classification variables that appear as crossed effects, this option is ignored.

SLICEBY=fixedeffect

specifies an effect by which to group the means in a single plot. For example, the following statement requests a plot in
which the levels of A
are placed on the horizontal axis and the means that belong to the same level of B
are joined by lines:
lsmeans A*B / plot=meanplot(sliceby=b join);
Unless the LSmean effect contains at least two classification variables, the SLICEBY= option has no effect. The fixedeffect does not have to be an effect in your MODEL statement, but it must consist entirely of classification variables and it must
be contained in the LSmean effect.

PLOTBY=fixedeffect

specifies an effect by which to break interaction plots into separate displays. For example, the following statement requests
for each level of C
one plot of the A*B
cell means that are associated with that level of C
:
lsmeans A*B*C / plot=meanplot(sliceby=b plotby=c clband);
In each plot, levels of A
are displayed on the horizontal axis, and confidence bands are drawn around the means that share the same level of B
.
The PLOTBY= option has no effect unless the LSmean effect contains at least three classification variables. The fixedeffect does not have to be an effect in the MODEL statement, but it must consist entirely of classification variables and it must
be contained in the LSmean effect.

NONE

requests that no plots be produced.
When LSmean calculations are adjusted for multiplicity by using the ADJUST= option, the plots are adjusted accordingly.

SEED=number

specifies the seed for the samplingbased components of the computations for the LSMEANS statement (for example, chibarsquare statistics and simulated pvalues). The value of number must be an integer. The seed is used to start the pseudorandomnumber generator for the simulation. If you do not specify
a seed, or if you specify a value less than or equal to zero, the seed is generated from reading the time of day from the
computer clock. Note that there could be multiple LSMEANS statements with SEED= specifications and there could be other statements
that can supply a random number seed. Since the procedure has only one random number stream, the initial seed is shown in
the SAS log.

SINGULAR=number

tunes the estimability checking. If is a vector, define ABS() to be the largest absolute value of the elements of . If ABS() is greater than c*number for any row of in the contrast, then is declared nonestimable. Here, is the Hermite form matrix , and c is ABS(), except when it equals 0, and then c is 1. The value for number must be between 0 and 1; the default is 1E–4.

STEPDOWN<(stepdownoptions)>

requests that multiple comparison adjustments for the pvalues of LSmean differences be further adjusted in a stepdown fashion. Stepdown methods increase the power of multiple
comparisons by taking advantage of the fact that a pvalue is never declared significant unless all smaller pvalues are also declared significant. The STEPDOWN adjustment combined with ADJUST=BON corresponds to the methods of Holm (1979) “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 denominator degrees of freedom are computed by the KenwardRoger (Kenward and Roger, 1997) or Satterthwaite method in a mixed model, then stepdownadjusted pvalues are produced only if the ADJDFE=ROW option is in effect.
Also, STEPDOWN affects only pvalues, not confidence limits. For ADJUST=SIMULATE, the generalized least squares hybrid approach of Westfall (1997) is used to increase Monte Carlo accuracy.
You can specify the following stepdownoptions in parentheses:

MAXTIME=n

specifies the time (in seconds) to be spent 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.

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

specifies how stepdown adjustment are made. 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 comparisons. For example,
it is not feasible to take logical constraints between all pairwise comparisons of more than 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 comparisons. If you do not specify n or if n = –1, the full search tree is used.