Modeling and Multivariate Methods - SAS

444 Correlations and Multivariate Techniques Chapter 17
Launch the Multivariate Platform
Default
REML
ML
Robust
Row-wise
Pairwise
The Default option uses either the Row-wise, Pairwise, or REML methods:
• Row-wise is used for data tables with no missing values.
• Pairwise is used for data tables that have more than 10 columns or more than 5000 rows, and that have
no missing values.
• REML is used otherwise.
REML (restricted maximum likelihood) estimates are less biased than the ML (maximum likelihood)
estimation method. The REML method maximizes marginal likelihoods based upon error contrasts. The
REML method is often used for estimating variances and covariances.The REML method in the
Multivariate platform is the same as the REML estimation of mixed models for repeated measures data with
an unstructured covariance matrix. See the documentation for SAS PROC MIXED about REML
estimation of mixed models. REML uses all of your data, even if missing cells are present, and is most useful
for smaller datasets. Because of the bias-correction factor, this method is slow if your dataset is large and
there are many missing data values. If there are no missing cells in the data, then the REML estimate is
equivalent to the sample covariance matrix.
The maximum likelihood estimation method (ML) is useful for large data tables with missing cells. The ML
estimates are similar to the REML estimates, but the ML estimates are generated faster. Observations with
missing values are not excluded. For small data tables, REML is preferred over ML because REML’s variance
and covariance estimates are less biased.
Robust estimation is useful for data tables that might have outliers. For statistical details, see “Robust” on
page 454.
Rowwise estimation does not use observations containing missing cells. This method is useful in the
following situations:
• checking compatibility with JMP versions earlier than JMP 8. Rowwise estimation was the only
estimation method available before JMP 8.
• excluding any observations that have missing data.
Pairwise estimation performs correlations for all rows for each pair of columns with nonmissing values.