rcve.py $input1 $response_col $predictor_cols $out_file1 1>/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 computes the RCVE (Relative Contribution to Variance) for all possible variable subsets using the following formula: **RCVE(i) = [R-sq (full: 1,2,..,i..,p-1) - R-sq(without i: 1,2,...,p-1)] / R-sq (full: 1,2,..,i..,p-1)**, which denotes the case where the 'i'th predictor is dropped. In general, **RCVE(X+) = [R-sq (full: {X,X+}) - R-sq(reduced: {X})] / R-sq (full: {X,X+})**, where, - {X,X+} denotes the set of all predictors, - X+ is the set of predictors for which we compute RCVE (and therefore drop from the full model to obtain a reduced one), - {X} is the set of the predictors that are left in the reduced model after excluding {X+} The 4 columns in the output are described below: - Column 1 (Model): denotes the variables present in the model ({X}) - Column 2 (R-sq): denotes the R-squared value corresponding to the model in Column 1 - Column 3 (RCVE_Terms): denotes the variable/s for which RCVE is computed ({X+}). These are the variables that are absent in the reduced model in Column 1. A '-' in this column indicates that the model in Column 1 is the Full model. - Column 4 (RCVE): denotes the RCVE value corresponding to the variable/s in Column 3. A '-' in this column indicates that the model in Column 1 is the Full model.