rcve.py
$input1
$response_col
$predictor_cols
$out_file1
1>/dev/null
rpy
.. class:: infomark
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.. 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.