Displaying 20 results from an estimated 2000 matches similar to: "Extracting intercept and residual std dev from lme results"
2003 May 20
1
Extracting elements from an reStruct
Sorry if this is obvious, but my S skills aren't great and I haven't been
able to find it documented anywhere.
I want to write a new function for use with lme objects; the function will
simply calculate an ICC (aka "rho") for each level of a mixed-effects
model. What I need for this is pretty simple:
(c(var1..varn, residual)) / sum(c(var1..varn, residual))
where var1..varn
2003 Mar 04
2
How to extract R{i} from lme object?
Hi, lme() users,
Can some one tell me how to do this.
I model Orthodont with the same G for random
variables, but different R{i}'s for boys and girls, so
that I can get sigma1_square_hat for boys and
sigma2_square_hat for girls.
The model is Y{i}=X{i}beta + Z{i}b + e{i}
b ~ iid N(0,G) and e{i} ~ iid N(0,R{i}) i=1,2
orth.lme <- lme(distance ~ Sex * age, data=Orthodont,
random=~age|Subject,
2006 Mar 07
1
lme and gls : accessing values from correlation structure and variance functions
Dear R-users
I am relatively new to R, i hope my many novice questions are welcome.
I have problems accessing some objects (specifically the random effects, correlation structure and variance function) from an object of class gls and lme.
I used the following models:
yah <- gls (outcome~ -1 + as.factor(Trial):as.factor(endpoint)+
2007 Jun 25
3
Bug in getVarCov.gls method (PR#9752)
Hello,
I am using R2.5 under Windows.
Looks like the following statement
vars <- (obj$sigma^2)*vw
in getVarCov.gls method (nlme package) needs to be replaced with:
vars <- (obj$sigma*vw)^2
With best regards
Andrzej Galecki
Douglas Bates wrote:
>I'm not sure when the getVarCov.gls method was written or by whom. To
>tell the truth I'm not really sure what
2004 Aug 03
2
lme fitted correlation of random effects: where is it?
The print method for lme *prints out* the fitted correlation matrix for
the random effects. Is there any way to get these values as an object in
R? I have examined the components of the lme object (called "junk" in the
example below) and the components of summary(junk) without finding these
numbers.
(How I did this: I dumped the entire lme object to a text file and then
used egrep to
2001 Nov 14
2
lme: how to extract the variance components?
Dear all,
Here is the question:
For example, using the "petrol" data offered with R.
pet3.lme<-lme(Y~SG+VP+V10+EP,random=~1|No,data=petrol)
pet3.lme$sigma gives the residual StdDev.
But I can't figure out how to extract the "(intercept) StdDev",
although it is in the print out if I do "summary(pet3.lme)".
In
2009 Mar 23
1
Extracting SD of random effects from lme object
Hello,
How do I get the standard deviations for the random effects out of the
lme object? I feel like there's probably a simple way of doing this,
but I can't see it. Using the first example from the documentation:
> fm1 <- lme(distance ~ age, data = Orthodont) # random is ~ age
> fm1
Linear mixed-effects model fit by REML
Data: Orthodont
Log-restricted-likelihood:
2007 May 03
2
Package contrast error
Trying to use contrast to look at differences within an lme
lme.fnl.REML <- lme(Max ~ S + Tr + Yr + Tr:Yr, random = ~1 |TID,
method = "REML")
I have three levels of Tr I'm trying to contrast among different
years (R, T97, T98), years = 1997-1999, so I'm interested in
contrasts of the interaction term.
> anova(lme.fnl.REML)
numDF denDF F-value
2008 Aug 29
3
extract variance components
HI,
I would like to extract the variance components estimation in lme function
like
a.fit<-lme(distance~age, data=aaa, random=~day/subject)
There should be three variances \sigma_day, \sigma_{day %in% subject } and
\sigma_e.
I can extract the \sigma_e using something like a.fit$var. However, I cannot
manage to extract the first two variance components. I can only see the
results in
2001 Oct 09
1
PROC MIXED user trying to use (n)lme...
Dear R-users
Coming from a proc mixed (SAS) background I am trying to get into
the use of (n)lme.
In this connection, I have some (presumably stupid) questions
which I am sure someone out there can answer:
1) With proc mixed it is easy to get a hold on the estimated
variance parameters as they can be put out into a SAS data set.
How do I do the same with lme-objects? For example, I can see the
2007 Jan 06
1
help with gls
Hello R-users,
I am using gls function in R to fit a model with certain correlation
structure.
The medol as:
fit.a<-gls(y~1,data=test.data,correlation=corAR1(form=~1|aa),method="ML")
mu<-summary(fit.a)$coefficient
With the toy data I made to test, the estimate of mu is exactly equal to
the overall mean of y which can not be true.
But, if I make a toy data with y more than two
2010 Jan 15
1
'nlme' library - lme function results
Dear R-helpers
I am running a simple mixed effects model using lme(). The call looks
like this:
fit <- lme(Analyte~Sample, data=Data, random=~1 | Run)
I am particularly interested in the estimated random effects. When I
print the 'fit' object, it looks something like example below:
(...)
Random effects:
Formula: ~1 | Run
(Intercept) Residual
StdDev: 3.483794 3.637523
2004 Apr 05
3
2 lme questions
Greetings,
1) Is there a nice way of extracting the variance estimates from an lme fit? They don't seem to be part of the lme object.
2) In a series of simulations, I am finding that with ML fitting one of my random effect variances is sometimes being estimated as essentially zero with massive CI instead of the finite value it should have, whilst using REML I get the expected value. I guess
2007 May 18
0
gls() error
Hi All
How can I fit a repeated measures analysis using gls? I want to start with a
unstructured correlation structure, as if the the measures at the occations are
not longitudinal (no AR) but plainly multivariate (corSymm).
My data (ignore the prox_pup and gender, occ means occasion):
> head(dta,12)
teacher occ prox_self prox_pup gender
1 1 0 0.76 0.41 1
2
2003 May 30
1
Error using glmmPQL
Can anyone shed any light on this?
> doubt.demographic.pql<-glmmPQL(random = ~ 1 | groupid/participantid,
+ fixed = r.info.doubt ~
+ realage + minority + female + education + income + scenario,
+ data = fgdata.df[coded.resource,],
+ na.action=na.omit,
+
2005 Jun 28
1
How to extract the within group correlation structure matrix in "lme"
Dear R users,
I fitted a repeated measure model without random effects by using lme. I will use the estimates from that model as an initial estimates to do multiple imputation for missing values of the response variable in the model. I am trying to extract the within group correlation matrix or covariance matrix.
here is my code:
f = lme(y ~x0+x1+trt+tim+x1:tim +tim:trt,random=~-1|subj,
2013 Oct 26
2
Problems with lme random slope+intercept model
Dear all,
I'm trying to fit a model on ecological data in which I have measured a few
biotic and abiotic factors over the course of a few days in several
individuals. Specifically, I'm interested in modelling y ~ x1, with x2, x3,
and 'factor' as independent variables. Because data suggests both slope and
intercept (for y ~x1) might differ between individuals, I'd want to
2002 May 31
0
Convergence and singularity in glmmPQL
Greetings-
Using R 1.5.0 under linux and the latest MASS and nlme, I am trying to
develop a three-level (two levels of nesting) model with a dichotomous
oucome variable. The unconditional model is thus:
> doubt1.pql<-glmmPQL(fixed = r.info.doubt ~ 1, random = ~1 |
groupid/participantid,
+ family = binomial, data = fgdata.10statements.df)
iteration 1
iteration 2
iteration 3
iteration 4
2007 Nov 12
1
R - lme
Dear R gurus,
I am trying to work out the problem given in Nested design - Montgomery - Design of Experiments p.561
I have attached a pdf of the data as well the anova table. It is a mixed model with Supplier as fixed effect and batches within the supplier as random effects.
I am able to work out the error stratums as below using aov. Which agrees perfectly with the book example
2007 Jun 20
2
Linear Mixed Models with nlme, more than one random effect
Hi, I' trying to learn how to use lme for Linear Mixed Models and I have a
problem when I have to include more than one random effect in my model. I
know that this could be a stupid question to ask, but I'm not able to solve
it by myself... One example: if my model is
response = operator + block + day
with operator and block as fixed effects and day as random effect, I use
res.lme