best_regression_subsets.py $input1 $response_col $predictor_cols $out_file1 $out_file2 1>/dev/null 2>/dev/null rpy .. class:: infomark **TIP:** If your data is not TAB delimited, use *Edit Queries->Convert characters* ----- .. class:: infomark **What it does** This tool uses the 'regsubsets' function from R statistical package for regression subset selection. It outputs two files, one containing a table with the best subsets and the corresponding summary statistics, and the other containing the graphical representation of the results. ----- .. class:: warningmark **Note** - This tool currently treats all predictor and response variables as continuous variables. - Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis. - The 6 columns in the output are described below: - Column 1 (Vars): denotes the number of variables in the model - Column 2 ([c2 c3 c4...]): represents a list of the user-selected predictor variables (full model). An asterix denotes the presence of the corresponding predictor variable in the selected model. - Column 3 (R-sq): the fraction of variance explained by the model - Column 4 (Adj. R-sq): the above R-squared statistic adjusted, penalizing for higher number of predictors (p) - Column 5 (Cp): Mallow's Cp statistics - Column 6 (bic): Bayesian Information Criterion.