Displaying 20 results from an estimated 800 matches similar to: "Repeatability and lme"
2011 Jun 08
1
using stimulate(model) for parametric bootstrapping in lmer repeatabilities
Hi all,
I am currently doing a consistency analysis using an lmer model and
trying to use parametric bootstrapping for the confidence intervals.
My model is like this:
model<-lmer(y~A+B+(1|C/D)+(1|E),binomial)
where E is the individual level for consistency analysis, A-D are
other fixed and random effects that I have to control for.
Following Nakagawa and Scheilzeth I can work out the
2008 Jan 03
2
confidence interval too small in nlme?
Hello,
I am interested in using nlme to model repeated measurements, but I don't seem
to get good CIs.
With the code below I tried to generate data sets according to the model given
by equations (1.4) and (1.5) on pages 7 and 8 of Pinheiro and Bates 2000 (having
chosen values for beta, sigma.b and sigma similar to those estimated in the
text).
For each data set I used lme() to fit a model,
2012 May 01
1
VarCorr procedure from lme4
Folks
In trying to use lmer for a hierarchical model, I encountered the
following message:
Error in UseMethod("VarCorr") :
no applicable method for 'VarCorr' applied to an object of class "mer"
foo.mer <- lmer(y ~ TP + (TP|M),data=joe.q)
> head(joe.q[,1:5])
TP M AB Trt y
1 1 Jan A NN 19.20002
2 1 Jan A NN 19.06378
3 1 Jan A NN
2004 Nov 30
1
lme in R-2.0.0: Problem with lmeControl
Hello!
One note/question hier about specification of control-parameters in the
lme(...,control=list(...)) function call:
i tried to specify tne number of iteration needed via
lme(....,control=list(maxIter=..., niterEM=...,msVerbose=TRUE))
but every time i change the defualt values maxIter (e.g. maxIter=1,
niterEM=0) on ones specified by me, the call returns all the iterations
needed until
2008 Feb 05
1
Extracting level-1 variance from lmer()
All,
How does one extract the level-1 variance from a model fit via lmer()?
In the code below the level-2 variance component may be obtained via
subscripting, but what about the level-1 variance, viz., the 3.215072 term?
(actually this term squared) Didn't see anything in the archives on this.
Cheers,
David
> fm <- lmer( dv ~ time.num*drug + (1 | Patient.new), data=dat.new )
2003 Jul 14
1
methods help and glmmPQL
Dear All,
I would like to ask you to help me with my memeory. I remember using some
function that would list all the possible methods I could apply to an
object. Say, if I had an object of
class=lme,
it would tell me that that I could do stuff like
qqnorm(myobjct), or VarCorr(myobject). In general, a very complete list.
I though this list of all possible methods would pop out by typing
2011 Jan 19
2
VarCorr
I have a loop that I would like to use to extract the "stddev" for
each itteration so I can average the "stddev" for all the runs. It
would be helpful to know how to extract the "stddev" for each run from
the VarCorr. Thanks
MCruns<-1000
sighatlvec<-rep(NA,MCruns)
sighatbvec<-rep(NA,MCruns)
sighatevec<-rep(NA,MCruns)
for(mc in 1:MCruns)
{
2004 Sep 21
2
Bootstrap ICC estimate with nested data
I would appreciate some thoughts on using the bootstrap functions in the
library "bootstrap" to estimate confidence intervals of ICC values
calculated in lme.
In lme, the ICC is calculated as tau/(tau+sigma-squared). So, for instance
the ICC in the following example is 0.116:
> tmod<-lme(CINISMO~1,random=~1|IDGRUP,data=TDAT)
> VarCorr(tmod)
IDGRUP = pdLogChol(1)
2005 Sep 01
2
VarCorr function for assigning random effects: was Question
If you are indeed using lme and not lmer then the needed function is
VarCorr(). However, 2 recommendations. First, this is a busy list and
better emails subject headers get better attention. Second, I would
recommend using lmer as it is much faster. However, VarCorr seems to be
incompatible with lmer and I do not know of another function to work
with lmer.
Hence, a better email subject header
2006 Sep 04
1
Problem with Variance Components (and general glmm confusion)
Dear list,
I am having some problems with extracting Variance Components from a random-effects model:
I am running a simple random-effects model using lme:
model<-lme(y~1,random=~1|groupA/groupB)
which returns the output for the StdDev of the Random effects, and model AIC etc as expected.
Until yesterday I was using R v. 2.0, and had no problem in calling the variance components of the
2007 Nov 09
1
Confidence Intervals for Random Effect BLUP's
I want to compute confidence intervals for the random effect estimates
for each subject. From checking on postings, this is what I cobbled
together using Orthodont data.frame as an example. There was some
discussion of how to properly access lmer slots and bVar, but I'm not
sure I understood. Is the approach shown below correct?
Rick B.
# Orthodont is from nlme (can't have both nlme and
2013 Sep 12
1
Importing packages in Depend
Hi,
I am currently preparing a new version of my package papeR. When I run R CMD
check using the development version of R I get the following note:
Package in Depends field not imported from: ?nlme?, ?lme4?, ?survival?
These packages needs to imported from for the case when
this namespace is loaded but not attached.
I now have problems to fix this issue. It is easy to get rid of two of the
2007 Jan 02
1
How to extract the variance componets from lme
Here is a piece of code fitting a model to a (part) of a dataset, just
for
illustration. I can extract the random interaction and the residual
variance
in group meth==1 using VarCorr, but how do I get the other residual
variance?
Is there any way to get the other variances in numerical form directly -
it
seems a litte contraintuitive to use "as.numeric" when extracting
estimates,
2005 Oct 10
1
lmer / variance-covariance matrix random effects
Hello,
has someone written by chance a function to extract the
variance-covariance matrix from a lmer-object? I've noticed the VarCorr
function, but it gives unhandy output.
Regards,
Roel de Jong
2006 Feb 20
1
Extracting variance components from lmer
Hi All.
I need a bit of help extracting the residual error variance from the VarCorr
structure from lmer.
#Here's a 2-way random effects model
lmer.1 <- lmer(rating ~ (1|person)+(1|rater), data = dat)
#Get the structure
vc.fit <- VarCorr(lmer.1)
#results in.....
$person
1 x 1 Matrix of class "dpoMatrix"
(Intercept)
(Intercept) 0.7755392
$rater
1 x 1 Matrix
2003 Feb 13
1
fixed and random effects in lme
Hi All,
I would like to ask a question on fixed and random effecti in lme. I am
fiddlying around Mick Crawley dataset "rats" :
http://www.bio.ic.ac.uk/research/mjcraw/statcomp/data/
The advantage is that most work is already done in Crawley's book (page 361
onwards) so I can check what I am doing.
I am tryg to reproduce the nested analysis on page 368:
2005 Jan 11
1
lme4 print and summary errror
Hi all - (this is posted to r-help and R-SIG-MAC)
OSX 10.3.7, R 2.0.1, lme4/Matrix/latticeExtra latest, fresh install of
R. MASS loaded (or not).
I am getting an error message for the print() and summary() commands
with all lme models I try and run in lme4 (GLMM's work fine). Using
the example from the lme help, summary and print produce the following
errors, despite the model being
2012 May 14
1
Extract Variance Components
Hi,
I'm still having problems putting the variance components of my model in to
a data frame, it is a continuation of this discussion,
http://r.789695.n4.nabble.com/ANOVA-problem-td4609062.html, but now focussed
on the problem of extracting variance components. I have got my mixed
effects model now
/narrow$line<-as.factor(narrow$line)
rg.lmer <- lapply(split(narrow,
2004 Dec 31
1
lme: Confusion about Variances
Dear R users!
I used lme to fit a mixed model with random intercept and spatial Gaussian
correlation i.e. I fitted a model of the following form:
Y = X*beta + error
and
error = U + W(t) + Z
where U is the random intercept (normally distributed), W(t) the stationary
Gaussian process and Z also a normally distributed (the residual) rv. Each of
these three random variables have a variance which
2006 Apr 25
1
lme: how to compare random effects in two subsets of data
Dear R-gurus,
I have an interpretation problem regarding lme models.
I am currently working on dog locomotion, particularly on some variation
factors.
I try to figure out which limb out of 2 generated more dispersed data.
I record a value called Peak, around 20 times for each limb with a record.
I repeat the records during a single day, and on several days.
I tried to build two models, one