
ADJUST=BON


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

requests a multiple comparison adjustment for the pvalues and confidence limits for the differences of LSmeans. The ADJUST= option modifies the results of the TDIFF
and PDIFF
options; thus, if you omit the TDIFF
or PDIFF
option then the ADJUST= option implies PDIFF. By default, PROC GLM analyzes all pairwise differences. If you specify ADJUST=DUNNETT,
PROC GLM analyzes all differences with a control level. If you specify the ADJUST=NELSON option, PROC GLM analyzes all differences
with the average LSmean. The default is ADJUST=T, which really signifies no adjustment for multiple comparisons.
The BON (Bonferroni) and SIDAK adjustments involve correction factors described in the section Multiple Comparisons and in Chapter 67: The MULTTEST Procedure. When you specify ADJUST=TUKEY and your data are unbalanced, PROC GLM uses the approximation described in Kramer (1956) and identifies the adjustment as "TukeyKramer" in the results. Similarly, when you specify either ADJUST=DUNNETT or the
ADJUST=NELSON option and the LSmeans are correlated, PROC GLM uses the factoranalytic covariance approximation described
in Hsu (1992) and identifies the adjustment in the results as "DunnettHsu" or "NelsonHsu," respectively. The preceding references also
describe the SCHEFFE and SMM adjustments.
The SIMULATE adjustment computes the adjusted pvalues from the simulated distribution of the maximum or maximum absolute value of a multivariate t random vector. The simulation estimates q, the true quantile, where is the confidence coefficient. The default is the value of the ALPHA=
option in the PROC GLM
statement or 0.05 if that option is not specified. You can change this value with the ALPHA=
option in the LSMEANS statement.
The number of samples for the SIMULATE adjustment is set so that the tail area for the simulated q is within a certain accuracy radius of with an accuracy confidence of %. 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, so that the tail area of is 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. Note that, if you also specify the CVADJUST simoption, then the actual accuracy radius will probably be substantially less than this target.

CVADJUST

specifies that the quantile should be estimated by the control variate adjustment method of Hsu and Nelson (1998) instead of simply as the quantile of the simulated sample. Specifying the CVADJUST option typically has the effect of significantly
reducing the accuracy radius of a % confidence interval for the true probability content of the estimated quantile. The controlvariateadjusted quantile estimate takes roughly twice as long to compute, but it is typically much
more accurate than the sample quantile.

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%, corresponding 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=12604 by default.

REPORT

specifies that a report on the simulation should be displayed, including a listing of the parameters, such as , , and , as well as an analysis of various methods for estimating or approximating the quantile.

SEED=number

specifies an integer 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.

ALPHA=p

specifies the level of significance p for % confidence intervals. This option is useful only if you also specify the CL
option, and, optionally, the PDIFF
option. By default, p is equal to the value of the ALPHA=
option in the PROC GLM
statement or 0.05 if that option is not specified, This value is used to set the endpoints for confidence intervals for the
individual means as well as for differences between means.

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

enables you to modify the values of the covariates 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 set the covariates to whatever values
you consider interesting. For more information, see the section Setting Covariate Values.

BYLEVEL

requests that PROC GLM process the OM data set by each level of the LSmean effect in question. For more details, see the
entry for the OM
option in this section.

CL

requests confidence limits for the individual LSmeans. If you specify the PDIFF
option, confidence limits for differences between means are produced as well. You can control the confidence level with the
ALPHA=
option. Note that, if you specify an ADJUST=
option, the confidence limits for the differences are adjusted for multiple inference but the confidence intervals for individual
means are not adjusted.

COV

includes variances and covariances of the LSmeans in the output data set specified in the OUT=
option in the LSMEANS statement. Note that this is the covariance matrix for the LSmeans themselves, not the covariance
matrix for the differences between the LSmeans, which is used in the PDIFF
computations. If you omit the OUT=
option, the COV
option has no effect. When you specify the COV
option, you can specify only one effect in the LSMEANS statement.

E

displays the coefficients of the linear functions used to compute the LSmeans.

E=effect

specifies an effect in the model to use as an error term. The procedure uses the mean square for the effect as the error mean square when calculating estimated standard errors (requested with the STDERR
option) and probabilities (requested with the STDERR
, PDIFF
, or TDIFF
option). Unless you specify STDERR
, PDIFF
or TDIFF
, the E= option is ignored. By default, if you specify the STDERR
, PDIFF
, or TDIFF
option and do not specify the E= option, the procedure uses the error mean square for calculating standard errors and probabilities.

ETYPE=n

specifies the type (1, 2, 3, or 4, corresponding to a Type I, II, III, or IV test, respectively) of the E=
effect. If you specify the E=
option but not the ETYPE= option, the highest type computed in the analysis is used. If you omit the E=
option, the ETYPE= option has no effect.

LINES

presents results of comparisons between all pairs of means (specified by the PDIFF
=ALL option) by listing the means in descending order and indicating nonsignificant subsets by line segments beside the corresponding
means. When all differences have the same variance, these comparison lines are guaranteed to accurately reflect the inferences
based on the corresponding tests, made by comparing the respective pvalues to the value of the ALPHA=
option (0.05 by default). However, equal variances are rarely 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, a note
is appended to the table that lists the pairs of means that are inferred to be significantly different by the tests but not
by the comparison lines. Note, however, that in many cases, even though the variances are unbalanced, they are near enough
that the comparison lines in fact accurately reflect the test inferences.

NOPRINT

suppresses the normal display of results from the LSMEANS statement. This option is useful when an output data set is created
with the OUT=
option in the LSMEANS statement.

OBSMARGINS
OM

specifies a potentially different weighting scheme for computing 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. For more information, see the section Changing the Weighting Scheme.
The BYLEVEL
option modifies the observedmargins LSmeans. Instead of computing the margins across the entire data set, the procedure
computes separate margins for each level of the LSmean effect in question. The resulting LSmeans are actually equal to raw
means in this case. If you specify the BYLEVEL
option, it disables the AT
option.

OUT=SASdataset

creates an output data set that contains the values, standard errors, and, optionally, the covariances (see the COV
option) of the LSmeans.
For more information, see the section Output Data Sets.

PDIFF<=difftype>

requests that pvalues for differences of the LSmeans be produced. The optional difftype specifies which differences to display. Possible values for difftype are ALL, CONTROL, CONTROLL, CONTROLU, and ANOM. The ALL value requests all pairwise differences, and it is the default. The
CONTROL value requests the differences with a control that, by default, is the first level of each of the specified LSmean
effects. The ANOM value 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 variances.
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, the PDIFF=ANOM computations are equivalent to the results of PROC ANOM. See the section
Analysis of Means: Comparing Each Treatments to the Average for more details.
To specify which levels of the effects are the controls, list the quoted formatted values in parentheses after the keyword
CONTROL. For example, if the effects A
, B
, and C
are CLASS
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 / pdiff=control('1' '2', '2' '1');
For multipleeffect situations such as this one, the ordering of the list is significant, and 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 less than the control; you declare a noncontrol level to be significantly less than
the control if the associated upper confidence limit for the noncontrol level minus the control is less than zero, and you
ignore the associated lower confidence limits (which are set to minus infinity).

CONTROLU tests whether the noncontrol levels are greater than the control; you declare a noncontrol level to be significantly greater
than the control if the associated lower confidence limit for the noncontrol level minus the control is greater than zero,
and you ignore the associated upper confidence limits (which are set to infinity).
The default multiple comparisons adjustment for each difftype is shown in the following table.
difftype

Default ADJUST=

Not specified

T

ALL

TUKEY

CONTROL


CONTROLL

DUNNETT

CONTROLU


ANOM

NELSON

If no difftype is specified, the default for the ADJUST=
option is T (that is, no adjustment); for PDIFF=ALL, ADJUST=TUKEY
is the default; for PDIFF=CONTROL, PDIFF=CONTROLL, or PDIFF=CONTROLU, the default value for the ADJUST=
option is DUNNETT
. For PDIFF=ANOM, ADJUST=NELSON
is the default. If there is a conflict between the PDIFF= and ADJUST=
options, the ADJUST=
option takes precedence.
For example, in order to compute onesided confidence limits for differences with a control, adjusted according to Dunnett’s
procedure, the following statements are equivalent:
lsmeans Treatment / pdiff=controll cl;
lsmeans Treatment / pdiff=controll cl adjust=dunnett;

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 PDIFF
option in the second LSMEANS statement implies all pairwise differences.

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 PDIFF
option in the second LSMEANS statement implies all pairwise differences.

DIFFPLOT<(diffplotoptions)>
DIFFOGRAM<(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=diffplot;
lsmeans B / diff plot=diffplot;
lsmeans C plot=diffplot;
The PDIFF=
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.

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.

NONE

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

SLICE=fixedeffect  (fixedeffects)

specifies effects within which to test for differences between interaction LSmean effects. This can produce what are known
as tests of simple effects (Winer, 1971). For example, suppose that A
*B
is significant and you want to test for the effect of A
within each level of B
. The appropriate LSMEANS statement is
lsmeans A*B / slice=B;
This statement tests for the simple main effects of A
for B
, which are calculated by extracting the appropriate rows from the coefficient matrix for the A
*B
LSmeans and using them to form an F test as performed by the CONTRAST
statement.

SINGULAR=number

tunes the estimability checking. If ABSnumber for any row, then is declared nonestimable. is the matrix, and C is ABS except for rows where is zero, and then it is 1. The default value for the SINGULAR= option is . Values for the SINGULAR= option must be between 0 and 1.

STDERR

produces the standard error of the LSmeans and the probability level for the hypothesis .

TDIFF

produces the t values for all hypotheses and the corresponding probabilities.