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**Chi-Square as variable selection / reduction technique**

**The Pearson / Wald / Score Chi-Square Test**can be used to test the association between the independent variables and the dependent variable.

A

**Wald/Score chi-square**test can be used for continuous and categorical variables. Whereas,

**Pearson chi-square**is used for categorical variables. The p-value indicates whether a coefficient is significantly different from zero.

**Note :**Wald and Score Chi-Square tests are asymptotically equivalent.**For Continuous variables**

Run

**UNIVARIATE Score / Wald Chi-Square analysis**on each of your continuous independent variable with dependent variable. It means putting one variable in independent variable list and setting your dependent variable and run regression analysis. Score and Wald Chi-Square are asymptotically equivalent. In

**PROC LOGISTIC**, use options:

**selection=stepwise maxstep=1 details**

**MAXSTEP=1**means that the maximum number of times any of the independent variables can be added or removed is

**1 time**.

Look at table -It helps to know the univariate predictive power of each continuous variable."Analysis of Effects Eligible for Entry"

**Rule :**

- Keep all the variables with a probability of chi-square of less than 0.25.
- Select the 50 variables with the highest chi-square score.

ODS OUTPUT EFFECTNOTINMODEL = TEST;

PROC LOGISTIC DATA = BHALLA.MODELING DESC;

MODEL ATTRITION = DDA DDABAL DEP DEPAMT

/ SELECTION = S MAXSTEP = 1 DETAILS;

RUN;

**For Character variables**

Run UNIVARIATE

**Pearson Chi-Square**analysis on each of your continuous independent variable with dependent variable. In

**PROC FREQ**, use options:

**missing chisq**

proc freq data= newdata;

tables attrition * balance/

**missing ChiSq**;

run;

Keep character variables where each category has more than about 25 observations and a p-value <=0.25.

**For Transformed Variables (Log, sqrt, exp, cube root, inverse etc.)**

In PROC LOGISTIC, use options :

**selection=stepwise maxstep=2 details**

Look at"Summary of Stepwise Procedure"table. It will give you two best variables.

*Use command*

**ODS OUTPUT ModelBuildingSummary = TEST;**

*to store the*

**'Summary of Stepwise Procedure'**table in SAS**Note :**Use of conservative sig level (0.05) often leads to important variables excluded from analysis. It is recommended to use higher level based on the work by Mickey and Greenland (1989) on logistic regression. It is important to CHECK the IMPORTANCE of these variables in multivariable model stage before deciding your final variables.

Why 0.25 and not 0.05?

ReplyDeleteUse of conservative sig level (0.05) often leads to important variables excluded from analysis. It is recommended to use higher level based on the work by Mickey and Greenland (1989) on logistic regression. It is important to CHECK the IMPORTANCE of these variables in multivariable model stage before deciding your final variables.

ReplyDeleteok u mean running the logistic regression with no selection options and viewing p-values of each variable before seeting p-value...but not more than 0.25?

ReplyDeleteStep 1 : Run logistic regression on each of the independent variable and selecting all the variables having p-value less than 0.25. For example, you have 10 independent variables, so run UNIVARIATE logistic regression 10 times for each of the variable and recording their p-values.

DeleteStep 2 : Run multivariate logistic regression on all the variables selecting at step 1 and now set p-value 0.05.

Pls i posted questions under the following topics: model validation, scoring in logistic, bootstrapping.

ReplyDeletetnx

I would request you to post your question as an identified user. I don't want to encourage readers post comments as "Anonymous". Thanks!

DeleteOk. Changd now. Thanks for that. I guess the same process goes for categorical variables using proc freq?

Delete