Dear List,
As I understand, GLMM (in experimental lme4) and glmmPQL (MASS) do
similar things using somewhat different methods. Trying both,
I get the same coefficients, but markedly different std. errors and
p-values.
Any help in understanding the models tested by both procedures?
Dieter Menne
UseMASS<-T # must restart R after changing because of nlme/lme4 clash
if (UseMASS){
library(MASS)
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria))
} else
{
library(lme4)
summary(GLMM(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria,method="PQL"))
}
(MASS output)
Fixed effects: y ~ trt + I(week > 2)
Value Std.Error DF t-value p-value
(Intercept) 3.412012 0.5185028 169 6.580509 0.0000
trtdrug -1.247355 0.6440627 47 -1.936698 0.0588
trtdrug+ -0.754327 0.6453971 47 -1.168780 0.2484
I(week > 2)TRUE -1.607256 0.3583378 169 -4.485310 0.0000
(lme4 output)
Fixed effects: y ~ trt + I(week > 2)
Estimate Std. Error DF z value Pr(>|z|)
(Intercept) 3.41202 3.93293 169 0.8676 0.3856
trtdrug -1.24736 1.52156 47 -0.8198 0.4123
trtdrug+ -0.75433 1.21963 47 -0.6185 0.5363
I(week > 2)TRUE -1.60726 2.19660 169 -0.7317 0.4644
GLMM (lme4) vs. glmmPQL output
3 messages · Dieter Menne, Douglas Bates, Göran Broström
1 day later
I believe the distinction is explained in the lme4 documentation but, in any case, the standard errors and the approximate log-likelihood for glmmPQL are from the lme model that is the last step in the optimization. The corresponding quantities from GLMM are from another approximation that should be more reliable. "Dieter Menne" <dieter.menne at menne-biomed.de> writes:
Dear List,
As I understand, GLMM (in experimental lme4) and glmmPQL (MASS) do
similar things using somewhat different methods. Trying both,
I get the same coefficients, but markedly different std. errors and
p-values.
Any help in understanding the models tested by both procedures?
Dieter Menne
UseMASS<-T # must restart R after changing because of nlme/lme4 clash
if (UseMASS){
library(MASS)
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria))
} else
{
library(lme4)
summary(GLMM(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria,method="PQL"))
}
(MASS output)
Fixed effects: y ~ trt + I(week > 2)
Value Std.Error DF t-value p-value
(Intercept) 3.412012 0.5185028 169 6.580509 0.0000
trtdrug -1.247355 0.6440627 47 -1.936698 0.0588
trtdrug+ -0.754327 0.6453971 47 -1.168780 0.2484
I(week > 2)TRUE -1.607256 0.3583378 169 -4.485310 0.0000
(lme4 output)
Fixed effects: y ~ trt + I(week > 2)
Estimate Std. Error DF z value Pr(>|z|)
(Intercept) 3.41202 3.93293 169 0.8676 0.3856
trtdrug -1.24736 1.52156 47 -0.8198 0.4123
trtdrug+ -0.75433 1.21963 47 -0.6185 0.5363
I(week > 2)TRUE -1.60726 2.19660 169 -0.7317 0.4644
______________________________________________ R-help at stat.math.ethz.ch mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Douglas Bates bates at stat.wisc.edu Statistics Department 608/262-2598 University of Wisconsin - Madison http://www.stat.wisc.edu/~bates/
On Fri, Jan 09, 2004 at 12:26:21PM -0600, Douglas Bates wrote:
I believe the distinction is explained in the lme4 documentation but, in any case, the standard errors and the approximate log-likelihood for glmmPQL are from the lme model that is the last step in the optimization. The corresponding quantities from GLMM are from another approximation that should be more reliable.
It would be interesting to see what glmmML, which uses yet another approximation, gives on this particular data set. Could you (Dieter) try it, and perhaps also share your data with us?
"Dieter Menne" <dieter.menne at menne-biomed.de> writes:
Dear List,
As I understand, GLMM (in experimental lme4) and glmmPQL (MASS) do
similar things using somewhat different methods. Trying both,
I get the same coefficients, but markedly different std. errors and
p-values.
Any help in understanding the models tested by both procedures?
Dieter Menne
UseMASS<-T # must restart R after changing because of nlme/lme4 clash
if (UseMASS){
library(MASS)
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria))
} else
{
library(lme4)
summary(GLMM(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria,method="PQL"))
}
(MASS output)
Fixed effects: y ~ trt + I(week > 2)
Value Std.Error DF t-value p-value
(Intercept) 3.412012 0.5185028 169 6.580509 0.0000
trtdrug -1.247355 0.6440627 47 -1.936698 0.0588
trtdrug+ -0.754327 0.6453971 47 -1.168780 0.2484
I(week > 2)TRUE -1.607256 0.3583378 169 -4.485310 0.0000
(lme4 output)
Fixed effects: y ~ trt + I(week > 2)
Estimate Std. Error DF z value Pr(>|z|)
(Intercept) 3.41202 3.93293 169 0.8676 0.3856
trtdrug -1.24736 1.52156 47 -0.8198 0.4123
trtdrug+ -0.75433 1.21963 47 -0.6185 0.5363
I(week > 2)TRUE -1.60726 2.19660 169 -0.7317 0.4644
______________________________________________ R-help at stat.math.ethz.ch mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
-- Douglas Bates bates at stat.wisc.edu Statistics Department 608/262-2598 University of Wisconsin - Madison http://www.stat.wisc.edu/~bates/
______________________________________________ R-help at stat.math.ethz.ch mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
G?ran Brostr?m tel: +46 90 786 5223 Department of Statistics fax: +46 90 786 6614 Ume? University http://www.stat.umu.se/egna/gb/ SE-90187 Ume?, Sweden e-mail: gb at stat.umu.se