Displaying 20 results from an estimated 2000 matches similar to: "DF and intercept term meaning for mixed (lme) models"
2007 Jan 28
1
extra panel arguments to plot.nmGroupedData {nlme}
Greetings,
I have a groupedData (nmGroupedData) object created with the following syntax:
Soil <- groupedData(
ksat ~ conc | soil_id/sar/rep,
data=soil.data,
labels=list(x='Solution Concentration', y='Saturated Hydraulic Conductivity'),
units=list(x='(cmol_c)', y='(cm/s)')
)
the original data represents longitudinal observations in the form of:
2005 Jan 03
1
different DF in package nlme and lme4
Hi all
I tried to reproduce an example with lme and used the Orthodont
dataset.
library(nlme)
fm2a.1 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1 | Subject)
anova(fm2a.1)
> numDF denDF F-value p-value
> (Intercept) 1 80 4123.156 <.0001
> age 1 80 114.838 <.0001
> Sex 1 25 9.292 0.0054
or alternatively
2003 Oct 02
0
lme vs. aov with Error term
Hi,
I have a question about using "lme" and "aov" for the
following dataset. If I understand correctly, using
"aov" with an Error term in the formula is equivalent
to using "lme" with default settings, i.e. both assume
compound symmetry correlation structure. And I have
found that equivalency in the past. However, with the
follwing dataset, I got different
2003 Oct 01
0
lme vs. aov with Error term again
Hi all,
Sent the following question yesterday, but haven't got
any suggestions yet. So just trying again, can anyone
comment on the problem that I have? Thank you!
-------------
Hi,
I have a question about using "lme" and "aov" for the
following dataset. If I understand correctly, using
"aov" with an Error term in the formula is equivalent
to using
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 Mar 09
1
multiple comparisons for lme using multcomp
Dear R-help list,
I would like to perform multiple comparisons for lme. Can you report to me
if my way to is correct or not? Please, note that I am not nor a
statistician nor a mathematician, so, some understandings are sometimes
quite hard for me. According to the previous helps on the topic in R-help
list May 2003 (please, see Torsten Hothorn advices) and books such as
Venables &
2003 Oct 02
0
RE: [S] lme vs. aov with Error term
Hi Bert,
Thanks for the suggestions. I tried lme with different
control parameters, and also tried using "ML", instaed
of "REML", but still got the same answers.
Yes, I hope some gurus on this list could give me some
hints.
Thanks
--- "Gunter, Bert" <bert_gunter at merck.com> wrote:
> But they are close. This is almost certainly a
> numeric issue --
2004 Nov 26
1
help with glmmPQL
Hello:
Will someone PLEASE help me with this problem. This is the third time
I've posted it.
When I appply anova() to two equations estimated using glmmPQL, I get a
complaint,
> anova(fm1, fm2)
Error in anova.lme(fm1, fm2) : Objects must inherit from classes "gls",
"gnls" "lm","lmList", "lme","nlme","nlsList", or
2007 Oct 31
0
set initial parameter values for GLMM estimation
Dear list members,
I look for a way (or alternative) to specify initial values when estimating
linear mixed models in R, and to avoid iterative estimation.
This is a way to control specific parameter values (eg. variance parameter
values) such that the result (F-value) is based on them. This result can
then be used for power analyses that uses the non-central F-distribution, as
is done with SAS
2004 Nov 25
1
Error in anova(): objects must inherit from classes
Hello:
Let me rephrase my question to attract interest in the problem I'm having. When I appply anova() to two equations
estimated using glmmPQL, I get a complaint,
> anova(fm1, fm2)
Error in anova.lme(fm1, fm2) : Objects must inherit from classes "gls",
"gnls" "lm","lmList", "lme","nlme","nlsList", or "nls"
2012 Feb 14
2
how to test the random factor effect in lme
Hi
I am working on a Nested one-way ANOVA. I don't know how to implement
R code to test the significance of the random factor
My R code so far can only test the fixed factor :
anova(lme(PCB~Area,random=~1|Sites, data = PCBdata))
numDF denDF F-value p-value
(Intercept) 1 12 1841.7845 <.0001
Area 1 4 4.9846 0.0894
Here is my data and my hand
2004 Aug 19
2
glmmPQL in R and S-PLUS 6 - differing results
Greetings R-ers,
A colleague and I have been exploring the behaviour of glmmPQL in R
and S-PLUS 6 and we appear to get different results using the same
code and the same data set, which worries us. I have checked the
behaviour in R 1.7.1 (MacOS 9.2) and R. 1.9.0 (Windows 2000) and the
results are the same, but differ from S-PLUS 6 with the latest Mass
and nlme libraries (Windows XP).
Here
2004 Dec 22
0
Random intercept model with time-dependent covariates, results different from SAS
Answering on a mail from
>From Keith Wong <keithw_at_med.usyd.edu.au>
Date Sun 04 Jul 2004 - 17:21:36 EST
Subject [R] Random intercept model with time-dependent
covariates, results different from SAS
Hi all
I've got a question about the degrees of freedom in a mixed model,
calculated with lme from the lme4 package.
Since I've no access to the original data
2003 Apr 09
1
[OFF] Nested or not nested, this is the question.
Hi,
sorry by this off.
I'm still try to understand nested design.
I have the follow example (fiction):
I have 12 plots in 4 sizes in 3 replicates (4*3 = 12)
In each plot I put 2 species (A and B) to reproduce.
After a period I make samples in each board and count the number of
individuals total (tot) and individuals A and B (nsp). Others individuals
excepts A and B are in total of
2006 Feb 23
2
Strange p-level for the fixed effect with lme function
Hello,
I ran two lme analyses and got expected results. However, I saw
something suspicious regarding p-level for fixed effect. Models are the
same, only experimental designs differ and, of course, subjects. I am
aware that I could done nesting Subjects within Experiments, but it is
expected to have much slower RT (reaction time) in the second
experiment, since the task is more complex, so it
2004 Jul 04
2
Random intercept model with time-dependent covariates, results different from SAS
Dear list-members
I am new to R and a statistics beginner. I really like the ease with which I can
extract and manipulate data in R, and would like to use it primarily. I've
been learning by checking analyses that have already been run in SAS.
In an experiment with Y being a response variable, and group a 2-level
between-subject factor, and time a 5-level within-subject factor. 2
2004 Nov 25
0
MASS problem -- glmmPQL and anova
Hello:
I am really stuck on this problem. Why do I get an error message with
anova() when I compare these two equations?
Hope someone can help.
ANDREW
____________________________
> fm1 <- glmmPQL(choice ~ day + stereotypy,
+ random = ~ 1 | bear, data = learning, family = binomial)
> fm2 <- glmmPQL(choice ~ day + envir + stereotypy,
+ random = ~ 1 |
2017 Nov 27
0
How to extract coefficients from sequential (type 1) ANOVAs using lmer and lme
I wantto run sequential ANOVAs (i.e. type I sums of squares), and trying to getresults including ANOVA tables and associated coefficients for predictive variables(I am using the R 3.4.2 version). I think ANOVA tables look right, but believecoefficients are wrong. Specifically, it looks like that the coefficients arefrom ANOVA with ?marginal? (type III sums of squares). I have tried both lme
2004 Nov 24
0
problem with anova and glmmPQL
Hello:
I am getting an error message when appplying anova() to two equations
estimated using glmmPQL. I did look through the archives but didn't
finding anything relevant to my problem. The R-code and results follow.
Hope someone can help.
ANDREW
____________________________
> fm1 <- glmmPQL(choice ~ day + stereotypy,
+ random = ~ 1 | bear, data = learning, family =
2007 Jun 25
1
degrees of freedom in lme
Dear all,
I am starting to use the lme package (and plan to teach a course based on it
next semester...). To understand what lme is doing precisely, I used balanced
datasets described in Pinheiro and Bates and tried to compare the lme outputs
to that of aov. Here is what I obtained:
> data(Machines)
> summary(aov(score~Machine+Error(Worker/Machine),data=Machines))
Error: Worker