Building models with SAS Enterprise Miner, SAS Factory Miner, SAS Visual Data Mining and Machine Learning or just with programming

Regression Model and Missing Values

Accepted Solution Solved
Reply
Occasional Contributor
Posts: 14
Accepted Solution

Regression Model and Missing Values

Hi,

 

I plan to create a logistic regression model in SAS, but I am working with categorical data (independent variables) with missing values.

Should I eliminate the missing values before fitting the model? I'm leaning towards this because the goal is not to predict using missing categorical values.

OR

Should I create a new category for missing variables? If I do that, then I'd have to create an extra dummy variable.

 

Thank you in advance for your help!

SMajid


Accepted Solutions
Solution
‎10-12-2016 01:39 PM
Super User
Posts: 10,028

Re: Regression Model and Missing Values

If you have a big table, you could remove them.
OR using PROC MI to populate these missing value.


View solution in original post


All Replies
Solution
‎10-12-2016 01:39 PM
Super User
Posts: 10,028

Re: Regression Model and Missing Values

If you have a big table, you could remove them.
OR using PROC MI to populate these missing value.


Super User
Posts: 19,814

Re: Regression Model and Missing Values

Do you have random missing or systematic missing?

Super User
Posts: 11,343

Re: Regression Model and Missing Values

By default the regression procedures will eliminate any record from analysis with missing values for any variable on the model statement.

☑ This topic is solved.

Need further help from the community? Please ask a new question.

Discussion stats
  • 3 replies
  • 392 views
  • 2 likes
  • 4 in conversation