Missing Value Imputation

One of the problems complicating the analysis of clinical data sets is the prevalence of missing values .

The Missing Value Imputation process replaces missing values in a data matrix with values computed from nonmissing values in the same row. Imputation is performed rowwise. That is, new imputation statistics are computed for each row in the input data set. You can also define groups of columns so that imputation is performed groupwise within each row.

What do I need?

One Input Data Set , containing all of the numeric data to be analyzed, is required for this process. Missing data should be represented by a dot ( . ).

An optional Experimental Design Data Set (EDDS) can be specified. This data set tells how the experiment was performed, providing information about the columns in the input data set. Note that one column in the EDDS must be named ColumnName and the values contained in this column must exactly match the column names in the input data set.

For detailed information about the files and data sets used or created by JMP Life Sciences software, see Files and Data Sets .

Output/Results

The output of the Missing Value Imputation process includes one data set containing all of the data from the input data set.

Missing data have been imputed.