Several platforms allow the estimation of a predictive model despite the presence of missing values. For more information, see the Informative Missing section in the Introduction to Fit Model chapter of the Fitting Linear Models book. However, many multivariate analyses cannot be completed if missing values are in the data set. This tool offers several ways to identify and understand the missing values in your continuous data and to conduct imputation for missing values.
The Arrhythmia.jmp sample data table contains information from 452 patient electrocardiograms (ECGs). The data was originally collected to classify different patterns of ECGs as cardiac arrhythmia. However, there are missing values in this data table. You are primarily interested in exploring these missing values and imputing them when necessary.
1.
Select Help > Sample Data Library and open Arrhythmia.jmp.
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Select Cols > Modeling Utilities > Explore Missing Values.
4.
Select the Show only columns with missing checkbox.
Missing Value Report
The Missing Value Report shown in Missing Value Report indicates that only five columns have missing data. Column J contains mostly missing data and is not useful for data analysis unless used in an Informative Missing platform such as Fit Model.
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6.
Click Multivariate Normal Imputation.
7.
Click Yes Shrinkage.
A JMP Alert appears, informing you that you should use the Save As command to preserve your original data.
8.
Imputation Report
The Imputation Report in Imputation Report indicates how many missing values were imputed and the specific imputation details. No missing data remain in the four columns that Multivariate Normal Imputation was used on.
Click Undo to undo the imputation and replace the imputed data with missing values.
Click Undo to undo the imputation and replace the imputed data with missing values.