f*********e
发帖数: 1144 | 1 Backgroud:
GLMM model: y = a + bX + cZ + e
bX: fixed effect (between-group variations: control vs treatment)
cZ: random effect (within-group variations)
Dist: poisson
===========================================================================
Quesetion 1): I have two levels of within-group variations for random
effects, specifically: biological variations and technicl variations. How do
I incorporate these two levels into one single random effect term? I am
using "glmer" from R-programming
Question 2): Does GLMM assume the data points are NOT independently
distributed?
I keep hearing something like "nested" But have no idea how does GLMM handle
the dependency.
I checked a couple literatures, but couldn't understand it.......sorry about
this dumb questions | s*********e
发帖数: 1051 | 2 1) although i don't know much about "glmer", 2-level random effect is common
and very doable in sas glimmix procedure. Do take a look at the "random"
statement in this procedure.
2) GLMM "assume the data points are NOT independently distributed" is not a
correct statement. Instead, it ought to be "GLMM does NOT have to assume the
data points are independently distributed". GLMM can take care of this
dependency by various co-variance structure in either G-side matrix or R-
side matrix. | f*********e
发帖数: 1144 | 3 Thanks a lot for the reply!!!!! esp in the holidays!
But my background is in bio/chem, I have marginally stat background.....
I only know R-programming a little bit....
As for 1), thanks for the explanatioin, at least I know it is doable,
although I don't know how to implement it.
As for 2), when u say "GLMM can take care of the dependency by......", do
you mean those two matrix are already embeded in the functions/packages so
that I don't have to modify anything?
common
a
the
【在 s*********e 的大作中提到】
: 1) although i don't know much about "glmer", 2-level random effect is common
: and very doable in sas glimmix procedure. Do take a look at the "random"
: statement in this procedure.
: 2) GLMM "assume the data points are NOT independently distributed" is not a
: correct statement. Instead, it ought to be "GLMM does NOT have to assume the
: data points are independently distributed". GLMM can take care of this
: dependency by various co-variance structure in either G-side matrix or R-
: side matrix.
| a****m
发帖数: 693 | 4 GLM are extension of LM to cases where data are independent and standard
linear model assumptions are violated, and GLMM just incorporate another
extra random effect.
for Q1, you can not separate those biological and technical variation in the
random effect
For Q2, for independent assumption, you can easily solve those parameter
analytically using ML, however this is not doable in GLMM, you may use some
numerical method to get optimal value of parameter, like pseudo-likelihood
approach. | f*********e
发帖数: 1144 | 5 谢谢回复！
我好好去看看
the
some
【在 a****m 的大作中提到】
: GLM are extension of LM to cases where data are independent and standard
: linear model assumptions are violated, and GLMM just incorporate another
: extra random effect.
: for Q1, you can not separate those biological and technical variation in the
: random effect
: For Q2, for independent assumption, you can easily solve those parameter
: analytically using ML, however this is not doable in GLMM, you may use some
: numerical method to get optimal value of parameter, like pseudo-likelihood
: approach.
| f*********e
发帖数: 1144 | 6 Does REML [built-in with glmer()]handle the dependency to some degree? It
does not have to be optimal estimations.
Thanks!
the
some
【在 a****m 的大作中提到】
: GLM are extension of LM to cases where data are independent and standard
: linear model assumptions are violated, and GLMM just incorporate another
: extra random effect.
: for Q1, you can not separate those biological and technical variation in the
: random effect
: For Q2, for independent assumption, you can easily solve those parameter
: analytically using ML, however this is not doable in GLMM, you may use some
: numerical method to get optimal value of parameter, like pseudo-likelihood
: approach.
| a****m
发帖数: 693 | 7 the dependency here refer to the correlation within group observation?
that is how LMM handle the covariance and variance in the modeling.
It
【在 f*********e 的大作中提到】
: Does REML [built-in with glmer()]handle the dependency to some degree? It
: does not have to be optimal estimations.
: Thanks!
:
: the
: some
| r*****l
发帖数: 457 | 8 哪位老大来通俗的解释一下
fixed effect 和 random effect的区别啊。半路出家，老被别人问，却讲不清楚
do
【在 f*********e 的大作中提到】
: Backgroud:
: GLMM model: y = a + bX + cZ + e
: bX: fixed effect (between-group variations: control vs treatment)
: cZ: random effect (within-group variations)
: Dist: poisson
: ===========================================================================
: Quesetion 1): I have two levels of within-group variations for random
: effects, specifically: biological variations and technicl variations. How do
: I incorporate these two levels into one single random effect term? I am
: using "glmer" from R-programming
|
|
