Displaying 9 results from an estimated 9 matches for "pdclass".
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2010 Apr 14
3
pdMat
Alguien tiene experiencia en escribir una pdMat. Para aquellos que no lo
recuerden son las matrices de covarianzas de los efectos aleatorios que
ajusta la función lme de la librería nlme
Estas matrices tiene especial importancia en aplicaciones de genética de
poblaciones y en particular en mapeo de asociación. Pinheiro y Bates dicen
que el usuario puede crear sus propias pdMat y sugiere como
2006 Mar 22
1
An lme model that works in old R.2.1.1 but not always in R.2.2.0 - why?
Following lme model runs fine in general under R.2.1.1 but only for 9 out
of my 11 response variables under R.2.2.0.
model for one of my response variables:
lme(Yresp~F1fix,random=list(const=pdBlocked(list(~F2mix-1,~Ass:F1fix-1,~F3mix-1,~F1fix:F3mix-1,~F2mix:F3mix-1),pdClass="pdIdent")))
Yresp is my response variable, F1fix is a fixed effect factor whereas
F2mix and F3mix are random effect factors.
const is set to rep(1,dim(Ycont)[1]).
The strange thing is that if an intercept is omitted (F1fix-1) the R.2.2.0
also runs a 100 %. It's the same model, just...
2013 Oct 26
2
Problems with lme random slope+intercept model
...omit))
#Error in chol.default((value + t(value))/2) :
# the leading minor of order 2 is not positive definite
I also tried fitting the same model with a diagonal covariance structure,
which resulted in convergence failure.
m3.lme<-with(data,lme(y~x1+x2+x3+factor,random=reStruct(object=~1+y|id,pdClass="pdDiag"),na.action=na.omit))
#Error in lme.formula(y ~ x1 + x2 + x3, random = reStruct(object = ~y | :
#nlminb problem, convergence error code = 1
#message = false convergence (8)
However, changing lmeControl gets this model to run, but I can't make sense
of the estimates for fix...
2006 Jun 30
1
lme and SAS Proc mixed
...elements of the matrix.
I have tried to reproduce this using lme, but it is unclear from the
documentation (and Pinheiro & Bates text) how the pdBlocked and
compound symmetric structure can be combined.
fit.lme<-lme(expression~End+logpgc,random=list(RefSeqID=pdBlocked(list
(~1,~ProbeID-1),pdClass="pdSymm")),data=dataset,correlation=corCompSym
m(form=~1|RefSeqID/ProbeID/ProbeNo))
The point estimates are essentially the same comparing R and SAS for
the fixed effects, but the 95% confidence intervals are much shorter
using lme(). In order to find the difference in the algorithms us...
2011 Aug 08
1
mixed model fitting between R and SAS
Hi al,
I have a dataset (see attached), which basically involves 4 treatments for a chemotherapy drug. Samples were taken from 2 biopsy locations, and biopsy were taken at 2 time points. So each subject has 4 data points (from 2 biopsy locations and 2 time points). The objective is to study treatment difference.?
I used lme to fit a mixed model that uses "biopsy.site nested within pid"
2003 Dec 24
0
Solution to "Can anyone help me reproduce this SAS Mixed output??"
...2
39 30.3 4 5 1
40 29.9 4 5 2
> attach(hw7)
> driver <- factor(driver)
> car <- factor(car)
> require(nlme)
> const <- factor(rep(1,40))
> hw7.lme <- lme(mpg~1, random=list(const=pdBlocked(list(~car-1,~driver-1,~car:driver-1),
pdClass="pdIdent")))
> VarCorr(hw7.lme)
> intervals(hw7.lme)
Phil Turk
Department of ____________
Mathematical Sciences | | phone: (406)994-5357
2-235 Wilson Hall \ BZN, MT | FAX: (406)994-1789
Montana State Uni...
2006 Jun 30
0
SAS Proc Mixed and lme
...elements of the matrix.
I have tried to reproduce this using lme, but it is unclear from the
documentation (and Pinheiro & Bates text) how the pdBlocked and
compound symmetric structure can be combined.
fit.lme<-lme(expression~End+logpgc,random=list(RefSeqID=pdBlocked(list
(~1,~ProbeID-1),pdClass="pdSymm")),data=dataset,correlation=corCompSym
m(form=~1|RefSeqID/ProbeID/ProbeNo))
The point estimates are essentially the same comparing R and SAS for
the fixed effects, but the 95% confidence intervals are much shorter
using lme(). In order to find the difference in the algorithms us...
2012 Jun 21
1
lme random effects in additive models with interaction
...))
Z.block[[i]] <-
as.formula(paste("~Z[,c(",paste(block.ind[[i]],collapse=","),")]-1"))
group <- rep(1, length(Time))
model.data <- groupedData(y~X|group, data=data.frame(X, y))
fit <- lme(y~-1+X, data=model.data, random=pdBlocked(list(
pdBlocked(Z.block,pdClass="pdIdent"), pdIdent(~-1+ Replication) ))
,control=list(maxIter=1000, msMaxIter=1000, niterEM=1000))
It gives errror: "Error: getResponseFormula(el) : "Form" must be a two
sided formula"
Does anybody help how can I write random part?
Thanks..
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2010 Feb 04
1
random slope models with lme --> failured to converge
Dear all,
I am working on a data set in which I have sequentially measured egg
temperatures ("eggtemp") in birds incubating in different ambient
temperatures ("treat", sample data set below), "id" is not replicated within
treatment.
id treat eggtemp
1 79 3 30.90166
2 42 3 34.94044
3 10 3 32.69945
4 206 3 36.64127
5 23 3 31.80055
6