Wide Data Variable Selector - Chi Square / Anova

From a very large data set (that is, one whose variables number in the thousands or millions), produces a new data set with correlations and significance statistics for each predictor (X) variable against a user-specified dependent (Y) variable.

Information at a Glance

Category Transform
Data source type HD
Sends output to other operators Yes
Data processing tool Spark SQL

Algorithm

For each predictor (X) variable, the operator computes the correlation against the dependent (Y) variable. If categorical predictors exist, they are converted to continuous predictors using impact coding before the correlations are calculated. The algorithm does two passes through the data, one to collect the dependent values and another to calculate the correlations.
Note: For this operator, the dependent variable must be categorical. If your dependent variable is continuous, then use the operator Wide Data Variable Selector - Correlations

The t statistic and corresponding p value calculations use the following formula.



Scalability should not be limited by anything other than available cluster resources. The algorithm makes two passes through the data: one to collect the dependent values, and another to calculate the correlations.

Input

A single tabular data set that contains key-value pairs of variables and values in stacked format, with variable_names, continuous_values, and categorical_values, and row_id columns.

Bad or Missing Data
Missing data is not present in the input table. There is a minimum of two values for each predictor and dependent variable. Missing data is casewise deleted.
Error and Exception Handling
The operation checks for validity of the dependent variable specification. See the Algorithm section for more information.
  • If the dependent variable is categorical, then it should be in a categorical values column and have discrete values (string, long, int).
  • If the dependent variable is continuous, then use the operator Wide Data Variable Selector - Correlations.

If there are not enough cases to calculate correlation for a variable (at least 2), then the operation returns NaN.

If there are not enough cases to calculate t statistic and p value (at least 3), then the operation returns 0 and 1, respectively.

Configuration

Parameter Description
Notes Any notes or helpful information about this operator's parameter settings. When you enter content in the Notes field, a yellow asterisk is displayed on the operator.
Dependent Variable Name The name of the dependent variable against which the correlation is computed. The dependent variable must be categorical. If is continuous, then use the operator Wide Data Variable Selector - Correlations.

Required.

Variables Column The name of the column containing variable names. The column should contain the Dependent Variable Name.
Continuous Values Column The name of the column that contains the continuous predictor values. If the Dependent Variable Name is specified as continuous, then this value is required.
Categorical Values Column The name of the column that contains the categorical predictor values. If the Dependent Variable Name is specified as categorical, then this value is required.
Row ID Column The name of the column that contains the row ID numbers. Required.
Number of Bins The number of bins used for the correlation. The default is 10.
Chi Square Output Can be one of the following:
  • Anova
  • Chi-Square
  • Chi-Square and p values
Output Directory The location to store the output files.
Output Name The name to contain the results.
Overwrite Output Specifies whether to delete existing data at that path.
  • Yes - if the path exists, delete that file and save the results.
  • No - fail if the path already exists.
Storage Format Select the format in which to store the results. The storage format is determined by your type of operator.

Typical formats are Avro, CSV, TSV, or Parquet.

Compression Select the type of compression for the output.
Available Parquet compression options.
  • GZIP
  • Deflate
  • Snappy
  • no compression

Available Avro compression options.

  • Deflate
  • Snappy
  • no compression
Advanced Spark Settings Automatic Optimization
  • Yes specifies using the default Spark optimization settings.
  • No enables providing customized Spark optimization. Click Edit Settings to customize Spark optimization. See Advanced Settings Dialog Box for more information.

Output

Visual Output
A tabular preview of the output data set, which includes Output and Summary tabs.
Output
A single tabular data set containing correlations for each predictor along with significance statistics.
Summary
The default summary, which includes parameters selected, input data size, and output location.
Data Output
A single tabular data set that contains s for each predictor, along with significance statistics.

Example

The following example shows the relationship between a wide table and the stacked table input the operator requires.



Related reference