Displaying 20 results from an estimated 2000 matches similar to: "Can't run code from "Mixed Effects Models in S and S-plus""
2012 May 02
3
Consulta gráfica
Hola,
Por favor, ¿podríais indicarme qué recursos (librerías o ideas) pueden resultar de utilidad para crear un gráfico del estilo del de la figura 3.8 del siguiente link?
http://www.tsc.uvigo.es/BIO/Bioing/ChrLDoc3.html#3.5
Actualmente estoy utilizando funciones muy básicas y la verdad es que no me encuentro muy satisfecha con el resultado.
Muchas gracias.
Eva
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2009 Nov 08
2
reference on contr.helmert and typo on its help page.
I'm wondering which textbook discussed the various contrast matrices
mentioned in the help page of 'contr.helmert'. Could somebody let me
know?
BTW, in R version 2.9.1, there is a typo on the help page of
'contr.helmert' ('cont.helmert' should be 'contr.helmert').
2008 Jul 18
2
column wise paste of data.frames
Hi everybody!
I'm sure that I overlook something and feel quite stupid to ask, but I
have not found an easy solution to the following problem: Take e.g. the
Orthodont data from the nlme package:
> head(Orthodont)
Grouped Data: distance ~ age | Subject
distance age Subject Sex
1 26.0 8 M01 Male
2 25.0 10 M01 Male
3 29.0 12 M01 Male
4 31.0 14 M01 Male
2006 Aug 22
1
summary(lm ... conrasts=...)
Hi Folks,
I've encountered something I hadn't been consciously
aware of previously, and I'm wondering what the
explanation might be.
In (on another list) using R to demonstrate the difference
between different contrasts in 'lm' I set up an example
where Y is sampled from three different normal distributions
according to the levels ("A","B","C")
2004 Mar 03
1
Confusion about coxph and Helmert contrasts
Hi,
perhaps this is a stupid question, but i need some help about
Helmert contrasts in the Cox model.
I have a survival data frame with an unordered factor `group'
with levels 0 ... 5.
Calculating the Cox model with Helmert contrasts, i expected that
the first coefficient would be the same as if i had used treatment
contrasts, but this is not true.
I this a error in reasoning, or is it
2010 Jun 22
2
xyplot: adding pooled regression lines to a paneled type="r" plot
Consider the following plot that shows separate regression lines ~ age
for each subject in the Pothoff-Roy Orthodont data,
with separate panels by Sex:
library(nlme)
#plot(Orthodont)
xyplot(distance ~ age|Sex, data=Orthodont, type='r', groups=Subject,
col=gray(.50),
main="Individual linear regressions ~ age")
I'd like to also show in each panel the pooled OLS
2019 Jan 17
3
long-standing documentation bug in ?anova.lme
tl;dr anova.lme() claims to provide sums of squares, but it doesn't. And
some names are misspelled in ?lme. I can submit all this stuff as a bug
report if that's preferred.
?anova.lme says:
When only one fitted model object is present, a data frame with
the sums of squares, numerator degrees of freedom, denominator
degrees of freedom, F-values, and P-values
The output of
fm1
2009 Mar 16
1
Please help! How do I change the class of a numeric variable in a grouped data object to a factor?
Hi all
I’m in desperate need of help. I’m working with a grouped data object, called Orthodont in the nlme package in R, and am trying to fit various models (learning methods for my thesis), but one of the variables in the object is numeric, (age) and I need it to be a factor. I’ve tried: as.factor(Orthodont$age)
as.factor(as.character(Orthodont$age))
and various other things, but when I then
2023 Mar 04
1
nlme varFixed
Dear R-project team,
I have a problem with the function varFixed() of the nlme-package.
I used it with the squid-data of Zuur et. al 2009 (chapter 4), to fix
increasing residuals (heterogenetiy) (see graph in the email)
I get the message '
Variance function structure of class varFixed with no parameters, or
uninitialized
Could you help me please?
Kind regards
Franz
2005 Apr 13
2
multinom and contrasts
Hi,
I found that using different contrasts (e.g.
contr.helmert vs. contr.treatment) will generate
different fitted probabilities from multinomial
logistic regression using multinom(); while the fitted
probabilities from binary logistic regression seem to
be the same. Why is that? and for multinomial logisitc
regression, what contrast should be used? I guess it's
helmert?
here is an example
2008 May 09
1
Using lme() inside a function
Dear R-help
I'm working on a large dataset which I have divided into 20 subsets
based on similar features. Each subset consists of observations from
different locations and I wish to use the location as a random effect.
For each group I want to select regressors by a stepwise procedure and
include a random effect on the intercept. I use stepAIC() and lme().
(The lmer()-function doesn't
2011 Feb 28
3
Measuring correlations in repeated measures data
R-helpers:
I would like to measure the correlation coefficient between the repeated measures of a single variable that is measured over time and is unbalanced. As an example, consider the Orthodont dataset from package nlme, where the model is:
fit <- lmer(distance ~ age + (1 | Subject), data=Orthodont)
I would like to measure the correlation b/t the variable "distance" at
2001 Jun 15
1
contrasts in lm and lme
I am using RW 1.2.3. on an IBM PC 300GL.
Using the data bp.dat which accompanies
Helen Brown and Robin Prescott
1999 Applied Mixed Models in Medicine. Statistics in Practice.
John Wiley & Sons, Inc., New York, NY, USA
which is also found at www.med.ed.ac.uk/phs/mixed. The data file was opened
and initialized with
> dat <- read.table("bp.dat")
>
1999 Jul 01
1
lme
I am using rw0641.
In my continuing quest to understand repeated measures analysis, I again
return to lme. I exported the Potthoff and Roy data Orthodont.dat from
S-PLUS 4.5 to avoid capture errors and ran the examples in the R help. I
imported the data.frame with
data <- read.table("Orthodont.dat",header=T)
attach(data)
and created the objects
Orthodont.fit1 <-
2008 May 09
2
How can one make stepAIC and lme
Dear R-help
I'm working on a large dataset which I have divided into 20 subsets based on similar features. Each subset consists of observations from different locations and I wish to use the location as a random effect.
For each group I want to select regressors by a stepwise procedure and include a random effect on the intercept. I use stepAIC() and lme(). (The lmer()-function doesn't
2010 Jul 15
4
Sweave: infelicities with lattice graphics
In a paper I'm writing using Sweave, I make use of lattice graphics, but
don't want to explicitly show (or explain)
in the article text the print() wrapper I need in code chunks for the
graphs to appear.
I can solve this by including each chunk twice, with different options,
as in
<<ortho-xyplot1-code, keep.source=TRUE, eval=FALSE>>=
library(nlme)
library(lattice)
2004 Aug 27
2
degrees of freedom (lme4 and nlme)
Hi, I'm having some problems regarding the packages
lme4 and nlme, more specifically in the denominator
degrees of freedom. I used data Orthodont for the two
packages. The commands used are below.
require(nlme)
data(Orthodont)
fm1<-lme(distance~age+ Sex,
data=Orthodont,random=~1|Subject, method="REML")
anova(fm1)
numDF DenDF F-value p-value
(Intercept) 1
2005 Jun 23
4
contrats hardcoded in aov()?
On 6/23/05, RenE J.V. Bertin <rjvbertin at gmail.com> wrote:
> Hello,
>
> I was just having a look at the aov function source code, and see that when the model used does not have an Error term, Helmert contrasts are imposed:
>
> if (is.null(indError)) {
> ...
> }
> else {
> opcons <- options("contrasts")
>
2005 Jul 12
1
nlme plot
Hello,
I am running this script from Pinheiro & Bates book in R Version 2.1.1 (WinXP).
But, I can't plot Figure 2.3.
What's wrong?
TIA.
Rod.
---------------------------------------------------------
>library(nlme)
> names( Orthodont )
[1] "distance" "age" "Subject" "Sex"
> levels( Orthodont$Sex )
[1] "Male"
2002 Dec 17
1
lme invocation
Hi Folks,
I'm trying to understand the model specification formalities
for 'lme', and the documentation is leaving me a bit confused.
Specifically, using the example dataset 'Orthodont' in the
'nlme' package, first I use the invocation given in the example
shown by "?lme":
> fm1 <- lme(distance ~ age, data = Orthodont) # random is ~ age
Despite the