Gary Dong gmail.com> writes:
> I'm estimating a two-level regresion model but having difficuly in finding
> a good R syntax example.
>
> Let me use an example to explain what I'm doing. The dependent variable is
> math score (mathscore). Predictors are at two levels. At the student level,
> they
Dear R users,
I'm estimating a two-level regresion model but having difficuly in finding
a good R syntax example.
Let me use an example to explain what I'm doing. The dependent variable is
math score (mathscore). Predictors are at two levels. At the student level,
they are household income (hinc)
Hello,
When I make certain values greater with the lmer function, variance
components that I expect to give a certain value are switched from how I
expect them to be. Has anyone else had this problem?
Thanks
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Dear R users
I have two questions, I have been on this problem for last 3 months, please
help
First question:
*How can I use the lmer function for a three level probit ( ie please help
me with the command syntax)?*
The second question is,
*how can I then subsequently calculate the Inverse Mills
Ali Mahani wrote:
>
> I'm trying to estimate a two-tier model with varying intercepts and slopes
> across 20 groups, with each group having about 50 observations and with no
> group predictor. I use the command lmer(y~x+(1+x | group)). But the result
> is a constant intercept (zero standard dev
I'm trying to estimate a two-tier model with varying intercepts and slopes
across 20 groups, with each group having about 50 observations and with no
group predictor. I use the command lmer(y~x+(1+x | group)). But the result
is a constant intercept (zero standard deviation, all 20 intercept values
The freely available R package glmmADMB can do Adaptive
Gaussian Quadrature for this type of model,
since it is built using AD Model Builder's random effects
module which incorporates this feature.
There is now a beta version of the software for
people using R on the Mac intel platform.
http://ot
The freely available R package glmmADMB can do Adaptive
Gaussian Quadrature for this type of model,
since it is built using AD Model Builder's random effects
module which incorporates this feature.
There is now a beta version of the software for
people using R on the Mac intel platform.
http://ot
On Tue, Apr 1, 2008 at 7:45 AM, Stefan Grosse <[EMAIL PROTECTED]> wrote:
> On Tuesday 01 April 2008 02:20:39 pm Boikanyo Makubate wrote:
> BM> I am using the lmer function from the lme4 package. I wrote the
> BM> following statement, specifying the method to be adaptive Gaussian
> BM> quadratur
On Tuesday 01 April 2008 02:20:39 pm Boikanyo Makubate wrote:
BM> I am using the lmer function from the lme4 package. I wrote the
BM> following statement, specifying the method to be adaptive Gaussian
BM> quadrature. I am getting an error saying "method = "AGQ" not yet
BM> implemented for supernod
I am using the lmer function from the lme4 package. I wrote the
following statement, specifying the method to be adaptive Gaussian
quadrature. I am getting an error saying "method = "AGQ" not yet
implemented for supernodal representation". Please help. How can i implement
AGQ.
fit<-lmer(respons
On Mon, Mar 31, 2008 at 7:13 AM, Boikanyo Makubate
<[EMAIL PROTECTED]> wrote:
> I am using the lmer function from the lme4 package. I wrote the
> following statement, specifying the method to be adaptive Gaussian
> quadrature. I am getting an error saying "method = "AGQ" not yet
> implemented f
I am using the lmer function from the lme4 package. I wrote the
following statement, specifying the method to be adaptive Gaussian
quadrature. I am getting an error saying "method = "AGQ" not yet
implemented for supernodal representation". Please help.
> fit <-
lmer(response~beta1+(1|patien
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