diff logistic_regression_vif.xml @ 1:2e7bc1bb2dbe draft default tip

Uploaded

--- a/logistic_regression_vif.xml	Tue Jul 29 06:30:45 2014 -0400
+++ b/logistic_regression_vif.xml	Fri Jan 09 12:56:07 2015 -0500
@@ -5,12 +5,14 @@
         <import>statistic_tools_macros.xml</import>
     </macros>
   <command interpreter="python">
-    logistic_regression_vif.py 
+<![CDATA[
+    logistic_regression_vif.py
       $input1
       $response_col
       $predictor_cols
       $out_file1
       1>/dev/null
+]]>
   </command>
   <inputs>
     <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/>
@@ -33,11 +35,12 @@
     </test>
   </tests>
   <help>
+<![CDATA[
 
 
 .. class:: infomark
 
-**TIP:** If your data is not TAB delimited, use *Edit Datasets-&gt;Convert characters*
+**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters*
 
 -----
 
@@ -67,9 +70,10 @@
 - Coefficient indicates log ratio of (probability to be class 1 / probability to be class 0)
 
 - This tool also provides **Variance Inflation Factor or VIF** which quantifies the level of multicollinearity. The tool will automatic generate VIF if the model has more than one predictor. The higher the VIF, the higher is the multicollinearity. Multicollinearity will inflate  standard error and reduce level of significance of the predictor. In the worst case, it can reverse direction of slope for highly correlated predictors if one of them is significant. A general thumb-rule is to use those predictors having VIF lower than 10 or 5.
-- **vif** is calculated by 
+- **vif** is calculated by
     - First, regressing each predictor over all other predictors, and recording R-squared for each regression.
     - Second, computing vif as 1/(1- R_squared)
 
+]]>
   </help>
 </tool>