BookmarkSubscribeRSS Feed
🔒 This topic is solved and locked. Need further help from the community? Please sign in and ask a new question.
MetinBulus
Quartz | Level 8

Hi everyone, 

 

I need your help to analyze a GLIMMIX parameter estimates output using PROC MIANALYZE. The output is created by group, and for each group treatment effect is estimated with its standard error. Basically I want to pool estimates from multiple simulated datasets. Unlike PROC MI procedure imputed values are not variables but they are estimates. Thanks. 

1 ACCEPTED SOLUTION

Accepted Solutions
PGStats
Opal | Level 21

Usually, simulations are used to assess the effect of sources of variation/uncertainty that are not well accounted for by available models. The variation of parameter estimates from simulation runs is assumed to reflect the variability built into the simulation process. Thus, the estimated standard errors are usually ignored, unless you are interested in the distribution of the standard error itself. The properties (location, variance, covariance, etc.) of your set of parameter estimates can normally be investigated and summarized with SAS base procedures (univariate, corr, sgplot).

I think however that simulations should involve more than 10 repetitions. I usually go for 100 to 2000.

PG

View solution in original post

3 REPLIES 3
PGStats
Opal | Level 21

Why not look at the distribution of your parameter estimates with standard tools like PROC UNIVARIATE and PROC CORR?

PG
MetinBulus
Quartz | Level 8

I have the regression coefficient estimated ten times for example, each estimation has a standard error associated with it. Using Proc means or Univariate won't take into account standard errors, as far as I know. I don't know how to come up with a single value for ten coefficients, taking into account their standard errors. 

PGStats
Opal | Level 21

Usually, simulations are used to assess the effect of sources of variation/uncertainty that are not well accounted for by available models. The variation of parameter estimates from simulation runs is assumed to reflect the variability built into the simulation process. Thus, the estimated standard errors are usually ignored, unless you are interested in the distribution of the standard error itself. The properties (location, variance, covariance, etc.) of your set of parameter estimates can normally be investigated and summarized with SAS base procedures (univariate, corr, sgplot).

I think however that simulations should involve more than 10 repetitions. I usually go for 100 to 2000.

PG

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 Bayesian Analysis?

Learn the difference between classical and Bayesian statistical approaches and see a few PROC examples to perform Bayesian analysis in this video.

Find more tutorials on the SAS Users YouTube channel.

Click image to register for webinarClick image to register for webinar

Classroom Training Available!

Select SAS Training centers are offering in-person courses. View upcoming courses for:

View all other training opportunities.

Discussion stats
  • 3 replies
  • 948 views
  • 2 likes
  • 2 in conversation