BookmarkSubscribeRSS Feed
deleted_user
Not applicable
Is there anyway I could get standardized estimates of the regression analysis output we get in Proc MIANALYZE ?

or is there any formula that I could use in a datastep with which I can indirectly get standardized coefficients?
3 REPLIES 3
Dale
Pyrite | Level 9
I don't believe that there is any direct way to convert parameter estimates obtained from the MIANALYZE procedure into standardized parameter estimates. However, you could use a process as described below to obtain standardized parameter estimates:

1) impute data using PROC MI

2) standardize the response and predictors to have mean 0 and variance 1 using PROC STANDARD

3) fit the regression model to each of the standardized imputation data sets outputting parameter estimates and the covariance matrix of the parameter estimates. Any of a number of procedures could be used for this step.

4) Combine standardized regression coefficients from each imputation set into a single estimate of the standardized regression coefficients using PROC MI
deleted_user
Not applicable
Does combining standardized regression coefficients mean I should take average of all the standardized estimates from the regression model of each imputation?


Thanks,

L
Dale
Pyrite | Level 9
Yes, that would be appropriate.

sas-innovate-2024.png

Don't miss out on SAS Innovate - Register now for the FREE Livestream!

Can't make it to Vegas? No problem! Watch our general sessions LIVE or on-demand starting April 17th. Hear from SAS execs, best-selling author Adam Grant, Hot Ones host Sean Evans, top tech journalist Kara Swisher, AI expert Cassie Kozyrkov, and the mind-blowing dance crew iLuminate! Plus, get access to over 20 breakout sessions.

 

Register now!

What is ANOVA?

ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.

Find more tutorials on the SAS Users YouTube channel.

Discussion stats
  • 3 replies
  • 1619 views
  • 0 likes
  • 2 in conversation