The GLM Procedure 
LSMEANS Statement 
Least squares means (LSmeans) are computed for each effect listed in the LSMEANS statement. You can specify only classification effects in the LSMEANS statement—that is, effects that contain only classification variables. You can also specify options to perform multiple comparisons. In contrast to the MEANS statement, the LSMEANS statement performs multiple comparisons on interactions as well as main effects.
LSmeans are predicted population margins; that is, they estimate the marginal means over a balanced population. In a sense, LSmeans are to unbalanced designs as class and subclass arithmetic means are to balanced designs. Each LSmean is computed as for a certain column vector , where is the vector of parameter estimates—that is, the solution of the normal equations. For further information, see the section Construction of Least Squares Means.
Multiple effects can be specified in one LSMEANS statement, or multiple LSMEANS statements can be used, but they must all appear after the MODEL statement. For example:
proc glm; class A B; model Y=A B A*B; lsmeans A B A*B; run;
LSmeans are displayed for each level of the A, B, and A*B effects.
You can specify the following options in the LSMEANS statement after a slash (/):
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 has no effect. 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 58, 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 , the true th 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 is within a certain accuracy radius of with an accuracy confidence of %. In equation form,
where is the simulated and 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.
specifies the target accuracy radius of a % confidence interval for the true probability content of the estimated th 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.
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 th quantile. The controlvariateadjusted quantile estimate takes roughly twice as long to compute, but it is typically much more accurate than the sample quantile.
specifies the value for a % confidence interval for the true probability content of the estimated th quantile. The default value for the accuracy confidence is 99%, corresponding to EPS=0.01.
specifies the sample size for the simulation. By default, 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 th quantile. With the default values for , , and (0.005, 0.01, and 0.05, respectively), NSAMP=12604 by default.
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.
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.
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.
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.
specifies the level of significance for % confidence intervals. This option is useful only if you also specify the CL option, and, optionally, the PDIFF option. By default, 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.
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.
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.
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.
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.
displays the coefficients of the linear functions used to compute the LSmeans.
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.
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.
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 values 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.
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.
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.
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.
requests that values 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.
PDIFF=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).
PDIFF=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 

ALL 

CONTROL 

CONTROLL 

CONTROLU 

ANOM 
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;
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.
tunes the estimability checking. If ABSnumber for any row, then is declared nonestimable. is the matrix, and 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.
produces the standard error of the LSmeans and the probability level for the hypothesis .
produces the values for all hypotheses and the corresponding probabilities.
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