Displaying 20 results from an estimated 900 matches similar to: "zero random effect sizes with binomial lmer [sorry, ignore previous]"
2006 Dec 31
0
(no subject)
> > If one compares the random effect estimates, in fact, one sees that
> > they are in the correct proportion, with the expected signs. They are
> > just approximately eight orders of magnitude too small. Is this a bug?
>
> BLUPs are essentially shrinkage estimates, where shrinkage is
> determined with magnitude of variance. Lower variance more
> shrinkage towards
2006 Dec 31
7
zero random effect sizes with binomial lmer
I am fitting models to the responses to a questionnaire that has
seven yes/no questions (Item). For each combination of Subject and
Item, the variable Response is coded as 0 or 1.
I want to include random effects for both Subject and Item. While I
understand that the datasets are fairly small, and there are a lot of
invariant subjects, I do not understand something that is happening
2011 Jul 19
2
strang behaviour of mice package
I am using mice package for multiple imputation. For one data
(attached), mice doesn't impute all missing values. Specifically, some
variables were not imputed at all.
the reproducible code
library(mice)
test.df<-read.table(c:\\test.txt',header=T,sep=',')
mi<-mice(test.df,maxit=10,m=5)
sum(is.na(complete(mi,1)))
>129
and x41, x50... were not imputed at all.
Any
2011 Mar 23
1
import question
I have been struggling all day to import a particular function/method
combination (ranef(), which extracts the random effects from a mixed
model fit) from the nlme package into another package ... so far without
success.
The NAMESPACE for nlme contains the following lines:
export(..., ranef, ...)
S3method(ranef, lme)
ranef is defined as a standard S3 generic,
function (object, ...)
2010 Mar 05
2
Defining a method in two packages
The coxme package has a ranef() method, as does lme4. I'm having
trouble getting them to play together, as shown below. (The particular
model in the example isn't defensible, but uses a standard data set.)
The problem is that most of the time only one of lme4 or coxme will be
loaded, so each needs to define the basic ranef function as well as a
method for it. But when loaded together
2015 Mar 02
1
clarification on import/depends for a method
User of the coxme library (mixed effects Cox models) are instructed to use ranef(),
fixed(), VarCorr(), etc to retrieve bits out of a fitted model; it purposely uses the same
methods as nlme and/or lmer.
The current behavior is to "depend" on nlme. If I defined the methods myself in coxme,
then someone who had both nlme and coxme loaded will suffer from "last loaded wins",
2003 May 12
1
plot.ranef.lme (PR#2986)
library(nlme)
data(Phenobarb)
na.include <- function(x)x
phe1 <- nlme(conc~phenoModel(Subject, time, dose, lCl, lV),
data = Phenobarb,
fixed = lCl+lV~1,
random= pdDiag(lCl+lV~1),
start = c(-5,0),
na.action = na.include,
naPattern = ~!is.na(conc))
phe.ranef <- ranef(phe1,augFrame=TRUE)
plot(phe.ranef, form=lCl~Wt+ApgarInd)
[Error in max(length(x0),
2011 Dec 30
0
New version of coxme / lmekin
Version 2.2 of coxme has been posted to CRAN, Windows versions and
mirrors should appear in due course. This is a major update with three
features of note:
1. A non-upwardly compatable change:
Extractor functions: beta= fixed effects, b=random effects
nlme lme4 coxme <2.2 coxme 2.2 lmekin 2.2
------------------------------------------------------
beta
2006 Aug 02
2
lme4 and lmeSplines
I'm trying to use the lmeSplines package together with lme4.
Below is (1) an example of lmeSplines together with nlme (2) an
attempt to use lmeSplines with lme4 (3) then a comparison of the
random effects from the two different methods.
(1)
require(lmeSplines)
data(smSplineEx1)
dat <- smSplineEx1
dat.lo <- loess(y~time, data=dat)
plot(dat.lo)
dat$all <- rep(1,nrow(dat))
times20
2015 Feb 16
0
Imports problem
>>>>> Therneau, Terry M , Ph D <therneau at mayo.edu>
>>>>> on Sun, 15 Feb 2015 17:31:00 -0600 writes:
> I'm testing out a new version of coxme and R CMD check fails with "could not find function
> ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the
> line below
>
2011 Feb 19
0
lmer, MCMCsamp and ranef samples?
I really hope sombody could help me with the following,
I'm having problems accessing the random effect samples following the
example on MCMCsamp:
(fm1 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy))
set.seed(101); samp0 <- mcmcsamp(fm1, n = 1000, saveb=TRUE)
str(samp0)
Formal class 'merMCMC' [package "lme4"] with 9 slots
..@ Gp :
2006 Oct 04
1
extracting nested variances from lme4 model
I have a model:
mod1<-lmer( x ~ (1|rtr)+ trth/(1|cs) , data=dtf) #
Here, cs and rtr are crossed random effects.
cs 1-5 are of type TRUE, cs 6-10 are of type FALSE,
so cs is nested in trth, which is fixed.
So for cs I should get a fit for 1-5 and 6-10.
This appears to be the case from the random effects:
> mean( ranef(mod1)$cs[[1]][1:5] )
[1] -2.498002e-16
> var(
2015 Feb 15
2
Imports problem
I'm testing out a new version of coxme and R CMD check fails with "could not find function
ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the
line below
importFrom(nlme, ranef, random.effects, fixef, fixed.effects, VarCorr)
and nlme is declared in the DESCRIPTION file as an import. I feel that I must be staring
at some obvious (but
2015 Feb 16
0
Imports problem
> > I'm testing out a new version of coxme and R CMD check fails with "could not find function
> > ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the
> > line below
>
> > importFrom(nlme, ranef, random.effects, fixef, fixed.effects, VarCorr)
>
> > and nlme is declared in the DESCRIPTION
2006 Dec 10
0
lmer, gamma family, log link: interpreting random effects
Dear all,
I'm curious about how to interpret the results of the following code.
The first model is directly from the help page of lmer; the second is
the same model but using the Gamma family with log link. The fixed
effects make sense, because
y = 251.40510 + 10.46729 * Days
is about the same as
log(y) = 5.53613298 + 0.03502057 * Days
but the random effects seem quite
2006 Jul 24
3
standardized random effects with ranef.lme()
Using ranef() (package nlme, version 3.1-75) with an 'lme' object I can
obtain random effects for intercept and slope of a certain level (say:
1) - this corresponds to (say level 1) "residuals" in MLWin. Maybe I'm
mistaken here, but the results are identical.
However, if I try to get the standardized random effects adding the
paramter "standard=T" to the
2007 Jun 28
0
mixed-effects model using lmer
Hello R-users,
I have been trying to fit what I think is a simple mixed-effects model using lmer (from lme4), but I've run into some difficulty that I have not been able to resolve using the existing archives or Pinheiro and Bates (2000).
I am measuring populations (of birds) which change with time at a number of different sites. These sites are grouped into regions. Sites are not measured
2010 Apr 25
1
How to assign scores to rows based on column values
Hi,
I'm trying to assign a score to each row which allow me to identify which
rows differ. In the example file below, I've used "," to indicate column
separators. In this example, I'd like to identify that row 1 and row 5 are
the same, and row 2 and row 4 are teh same.
Any help much appreciated. Also, any comments on what the command lines do
would be fantastic.
Thanks!!
2015 Feb 16
2
Imports problem
On 16/02/2015 8:20 AM, Therneau, Terry M., Ph.D. wrote:
>
>> > I'm testing out a new version of coxme and R CMD check fails with "could not find function
>> > ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the
>> > line below
>>
>> > importFrom(nlme, ranef, random.effects, fixef,
2011 Nov 03
0
Help in ranef Function
Hi
I'm getting the intercepts of the Random effects as 0. Please help me to
understand why this is coming Zero
This is my R code
Data<- read.csv("C:/FE and RE.csv")
Formula="Y~X2+X3+X4 + (1|State) + (0+X5|State)"
fit=lmer(formula=Formula,data=Data)
ranef(fit).
My sample Data
State Year Y X2 X3 X4 X5 X6
S2 1960 27.8 397.5 42.2 50.7 78.3 65.8
S1 1960 29.9 413.3 38.1