Bob at r4stats.com claimed that a linear mixed model with over 5 million observations and 2 million levels of random effects was fit using lme4 package in R:
I am always interested in large scale mixed model like this and would appreciate anyone w…
Tag: PROC HPMIXED
Benchmark Regression Procedures using OLS Regression
Rick Wicklin discussed in his blog the performance in solving a linear system using SOLVE() function and INV() function from IML.
Since regression analysis is an integral part of SAS applications and there are many SAS procedures in SAS/STAT that a…
Benchmark Regression Procedures using OLS Regression
Rick Wicklin discussed in his blog the performance in solving a linear system using SOLVE() function and INV() function from IML.
Since regression analysis is an integral part of SAS applications and there are many SAS procedures in SAS/STAT that a…
Benchmark Regression Procedures using OLS Regression
Rick Wicklin discussed in his blog the performance in solving a linear system using SOLVE() function and INV() function from IML.
Since regression analysis is an integral part of SAS applications and there are many SAS procedures in SAS/STAT that a…
%HPGLIMMIX macro on large scale HMM
PROC GLIMMIX is good tool for generalized linear mixed model (GLMM), when the scale is small to medium. When facing a large scale GLMM, such as modeling all ZIPs nested in Counties nested in all 51 States in US, a 64-bit machine with extremely large …
%HPGLIMMIX macro on large scale HMM
PROC GLIMMIX is good tool for generalized linear mixed model (GLMM), when the scale is small to medium. When facing a large scale GLMM, such as modeling all ZIPs nested in Counties nested in all 51 States in US, a 64-bit machine with extremely large …
%HPGLIMMIX macro on large scale HMM
PROC GLIMMIX is good tool for generalized linear mixed model (GLMM), when the scale is small to medium. When facing a large scale GLMM, such as modeling all ZIPs nested in Counties nested in all 51 States in US, a 64-bit machine with extremely large …