At 06:28 26/11/2006, you wrote:
Below is the output for p5.random.p,p5.random.p1 and m0
My question is
in p5.random.p, variance for P is 5e-10.
But in p5.random.p1,variance for P is 0.039293.
Why they are so different?
Please do as the posting guide asks and reply to the list, not just
the individual.
a - I might not know the answer
b - the discussion might help others
You give very brief details of the underlying problem so it is hard
to know what information will help you most.
Remember, if a computer estimates a non-negative quantity as very
small perhaps it is really zero.
I think you might benefit from reading Pinheiro and Bates, again see
the posting list.
Is that wrong to write Y~P+(1|P) if I consider P as random effect?
I suppose terminology differs but I would have said the model with
Y~1+(1|P) was a random intercept model
Also in p5.random.p and m0, it seems that there are little
difference in estimate for P. Why?
thanks,
Aimin Yan
p5.random.p -
lmer(Y~P+(1|P),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1))
summary(p5.random.p)
Generalized linear mixed model fit using Laplace
Formula: Y ~ P + (1 | P)
Data: p5
Family: binomial(logit link)
AIC BIC logLik deviance
1423 1452 -705.4 1411
Random effects:
Groups NameVariance Std.Dev.
P (Intercept) 5e-102.2361e-05
number of obs: 1030, groups: P, 5
Estimated scale (compare to 1 ) 0.938
Fixed effects:
Estimate Std. Error z value Pr(|z|)
(Intercept) 0.153404 0.160599 0.9552 0.3395
P8ABP -0.422636 0.202181 -2.0904 0.0366 *
P8adh0.009634 0.194826 0.0495 0.9606
P9pap0.108536 0.218875 0.4959 0.6200
P9RNT0.376122 0.271957 1.3830 0.1667
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) P8ABP P8adh P9pap
P8ABP -0.794
P8adh -0.824 0.655
P9pap -0.734 0.583 0.605
P9RNT -0.591 0.469 0.487 0.433
p5.random.p1 -
lmer(Y~1+(1|P),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1))
summary(p5.random.p1)
Generalized linear mixed model fit using Laplace
Formula: Y ~ 1 + (1 | P)
Data: p5
Family: binomial(logit link)
AIC BIC logLik deviance
1425 1435 -710.6 1421
Random effects:
Groups NameVariance Std.Dev.
P (Intercept) 0.039293 0.19823
number of obs: 1030, groups: P, 5
Estimated scale (compare to 1 ) 0.9984311
Fixed effects:
Estimate Std. Error z value Pr(|z|)
(Intercept) 0.1362 0.1109 1.2280.219
m0-glm(Y~P,data=p5,family=binomial(logit))
summary(m0)
Call:
glm(formula = Y ~ P, family = binomial(logit), data = p5)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.4086 -1.2476 0.9626 1.1088 1.2933
Coefficients:
Estimate Std. Error z value Pr(|z|)
(Intercept) 0.154151 0.160604 0.960 0.3371
P8ABP -0.422415 0.202180 -2.089 0.0367 *
P8adh0.009355 0.194831 0.048 0.9617
P9pap0.108214 0.218881 0.494 0.6210
P9RNT0.374693 0.271945 1.378 0.1683
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1425.5 on 1029 degrees of freedom
Residual deviance: 1410.8 on 1025 degrees of freedom
AIC: 1420.8
Number of Fisher Scoring iterations: 4
At 06:13 AM 11/24/2006, you wrote:
At 21:52 23/11/2006, Aimin Yan wrote:
consider p as random effect with 5 levels, what is difference between these
two models?
p5.random.p - lmer(Y
~p+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1))
p5.random.p1 - lmer(Y
~1+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1))
Well, try them and see. Then if you cannot understand the output tell us
a) what you found
b) how it differed from what you expected
in addtion, I try these two models, it seems they are same.
what is the difference between these two model. Is this a cell means model?
m00 - glm(sc ~aa-1,data = p5)
m000 - glm(sc ~1+aa-1,data = p5)
See above
thanks,
Aimin Yan
Michael Dewey
http://www.aghmed.fsnet.co.uk
--
No virus found in this incoming message.
Checked by AVG Free Edition.
Version: 7.5.431 / Virus Database: 268.14.16/551 - Release Date:
25/11/2006 10:55
Michael Dewey
http://www.aghmed.fsnet.co.uk
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
