Multivariate Methods • Correlations and Multivariate Techniques • Launch the Multivariate Platform
Previous
 • 
Next
Launch the Multivariate Platform
Launch the Multivariate platform by selecting Analyze > Multivariate Methods > Multivariate.
The Multivariate Launch Window
Description of the Multivariate Launch Window 
Y, Columns
Defines one or more response columns.
Weight
(Optional) Identifies one column whose numeric values assign a weight to each row in the analysis.
Freq
(Optional) Identifies one column whose numeric values assign a frequency to each row in the analysis.
By
(Optional) Performs a separate multivariate analysis for each level of the By variable.
Estimation Method
Select from one of several estimation methods for the correlations. With the Default option, 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 missing values. Otherwise, the default estimation method is REML. For details, see Estimation Methods.
Matrix Format
Select a format option for the Scatterplot Matrix. The Square option displays plots for all ordered combinations of columns. Lower Triangular displays plots on and below the diagonal, with the first n - 1 columns on the horizontal axis. Upper Triangular displays plots on and above the diagonal, with the first n - 1 columns on the vertical axis.
Estimation Methods
Several estimation methods for the correlations options are available to provide flexibility and to accommodate personal preferences. REML and Pairwise are the methods used most frequently. You can also estimate missing values by using the estimated covariance matrix, and then using the Impute Missing Data command. See Impute Missing Data.
Default
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 in these circumstances:
‒
the data table has more than 10 columns or more than 5000 rows and has missing values
‒
the data table has more columns than rows and has missing values
•
REML is used otherwise.
REML
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.
ML
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
Note: If you select Robust, and your data table contains more columns than rows, JMP switches the Estimation Method to Row-wise.
Robust estimation is useful for data tables that might have outliers. For statistical details, see Robust.
Row-wise
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
Pairwise estimation performs correlations for all rows for each pair of columns with nonmissing values.