Displaying 20 results from an estimated 8000 matches similar to: "Query on linear mixed model"
2011 Mar 01
1
glht() used with coxph()
Hi, I am experimenting with using glht() from multcomp package together with
coxph(), and glad to find that glht() can work on coph object, for example:
> (fit<-coxph(Surv(stop, status>0)~treatment,bladder1))
coxph(formula = Surv(stop, status > 0) ~ treatment, data = bladder1)
coef exp(coef) se(coef) z p
treatmentpyridoxine -0.063 0.939 0.161
2011 Mar 04
2
glht: Problem with symbolic contrast for factors with number-levels
Using a factor with 'number' levels the straightforward
symbolic formulation of a contrast in 'glht' of
the 'multcomp' package fails.
How can this problem be resolved without having to redefine the factor levels?
Example:
#A is a factor with 'number' levels
#B similar factor with 'letter' levels
dat<-data.frame(y=1:4,A=factor(c(1,1,2,2)),
2013 Jul 25
1
lme (weights) and glht
Dear R members,
I tried to fit an lme model and to use the glht function of multcomp.
However, the glht function gives me some errors when using
weights=varPower().
The glht error makes sense as glht needs factor levels and the model
works fine without weights=. Does anyone know a solution so I do not
have to change the lme model?
Thanks
Sibylle
--> works fine
2010 Jul 21
1
post hoc test for lme using glht ?
Hi,
I have a fairly simple repeated measures-type data set I've been attempting
to analyze using the lme function in the nlme package. Repeated searches
here and other places lead me to believe I have specified my model
correctly.
However, I am having trouble with post-hoc tests. From what I gather, other
people are successfully using the glht function from the multcomp package to
2009 Mar 22
1
Multiple Comparisons for (multicomp - glht) for glm negative binomial (glm.nb)
Hi
I have some experimental data where I have counts of the number of
insects collected to different trap types rotated through 5 different
location (variable -location), 4 different chemical attractants [A, B,
C, D] were applied to the traps (variable - semio) and all were
trialled at two different CO2 release rates [1, 2] (variable CO2) I also
have a selection of continuous variables
2009 Apr 21
3
broken example: lme() + multcomp() Tukey on repeated measures design
I am trying to do Tukey HSD comparisons on a repeated measures expt.
I found the following example on r-help and quoted approvingly elsewhere.
It is broken. Can anyone please tell me how to get it to work?
I am using R 2.4.1.
> require(MASS) ## for oats data set
> require(nlme) ## for lme()
> require(multcomp) ## for multiple comparison stuff
> Aov.mod <- aov(Y ~ N + V +
2007 Feb 09
1
Help in using multcomp.
Hi All,
I am trying use 'multcomp' for multiple comparisons
after my ANOVA analysis. I have used the following
code to do ANOVA:
dat <- matrix(rnorm(45), nrow=5, ncol=9)
f <- gl(3,3,9, label=c("C", "Tl", "T2"))
aof <- function(x) {
m <- data.frame(f, x);
aov(x ~ f, m)
}
amod <- apply(dat,1,aof)
Now, how can I use
2011 Jul 18
1
Multiple comparison test on selected contrasts
Dear Help-list, How can I do a multiple comparison test (mct) on selected contrasts from a linear model while using packages lme4 and multcomp? I am running R 2.13.0 under Windows 7. The following linear model and mct produces a global mct of 15 paired contrasts of the combined (Site, Position) factor SitePos of which only 9 are of interest. Model.G = lmer(log10(SrCa) ~ SitePos + (1 | Eel),
2011 Jul 26
3
a question about glht function
Hi all:
There's a question about glht function.
My data:data_ori,which inclue CD4, GROUP,time.
f_GROUP<-factor(data_ori$GROUP)
f_GROUP is a factor of 3 levels(0,1,2,3)
result <- lme(sqrt(CD4) ~ f_GROUP*time ,random = ~time|ID,data=data_ori)
glht(result, linfct = mcp(f_GROUP="Tukey") )
Error in `[.data.frame`(mf, nhypo[checknm]) : undefined columns selected
I can't
2012 Jan 11
2
problems with glht for ancova
I've run an ancova, edadysexo is a factor with 3 levels,and log(lcc) is the
covariate (continous variable)
I get this results
> ancova<-aov(log(peso)~edadysexo*log(lcc))
> summary(ancova)
Df Sum Sq Mean Sq F value Pr(>F)
edadysexo 2 31.859 15.9294 803.9843 <2e-16 ***
log(lcc) 1 11.389 11.3887 574.8081 <2e-16 ***
2012 Mar 28
1
discrepancy between paired t test and glht on lme models
Hi folks,
I am working with repeated measures data and I ran into issues where the
paired t-test results did not match those obtained by employing glht()
contrasts on a lme model. While the lme model itself appears to be fine,
there seems to be some discrepancy with using glht() on the lme model
(unless I am missing something here). I was wondering if someone could
help identify the issue. On
2008 Jan 18
1
how to specify a particular contrast
Hi, I am running a simple one-way ANOVA with an
independent factot variable "treat" (3 levels: a, b
and c) and a response variable "y". I want to test a
linear relationship of the response among the 3 levels
of the variable "treat" (ordered a->b->c). I used
glht() from multcomp package. Later I found out I need
to exclude the situation where the response at the
2007 Nov 07
1
bug in multcomp?
I am running a linear model with achiev as the outcome and major as my
iv (5 levels). The lm statement runs fine, but for the glht command I
get the following error. I noted that someone else asked the same
question a while back but received no reply. I am hoping someone might
know what is happening.
anovaf2<-lm(achiev ~ major, data=data_mcp)
> pairwise<- glht(anovaf2,linfct =
2011 Jan 19
3
lme-post hoc
Hi all,
I analysed my data with lme and after that I spent a lot of time for
mean separation of treatments (post hoc). But still I couldn’t make
through it. This is my data set and R scripts I tried.
replication fertilizer variety plot height
1 level1 var1 1504 52
1 level1 var3 1506 59
1 level1 var4 1509 54
1 level1 var2 1510 48
2 level1 var1 2604 47
2 level1 var4 2606 51
2 level1 var3
2012 Feb 06
2
glht (multicomparisons) with a binomial response variable
Hi,
I,ve a run a model like this
mcrm<-glm(catroj~month,binomial)
being catroj a binary response variable with two levels (infected and
non infected)
> anova(mcrm3,test="Chisq")
Df Deviance Resid. Df Resid. Dev P(>|Chi|)
NULL 520 149.81
mes 3 16.86 517 132.94 0.0007551 ***
When I?m trying to do a post
2013 Jan 14
1
Tukey HSD plot with lines indicating (non-)significance
Dear list members,
I'm running some tests looking at differences between means for various
levels of a factor, using Tukey's HSD method.
I would like to plot the data as boxplots or dotplots, with horizontal
significance lines indicating which groups are statistically
significantly different, according to Tukey HSD. Here's a nice image
showing an example of such a graphical
2008 Dec 08
2
How to display y-axis labels in Multcomp plot
Dear R-users,
I'm currently using the multcomp package to produce plots of means with 95%
confidence intervals
i.e.
mult<-glht(lm(response~treatment, data=statdata),
linfct=mcp(treatment="Means"))
plot(confint(mult,calpha = sig))
Unfortunately the y-axis on the plot appears to be fixed and hence if the
labels on the y-axis (treatment levels) are too long, then they are not
2008 Jan 10
1
general linear hypothesis glht() to work with lme()
Hi,
I am trying to test some contrasts, using glht() in
multcomp package on fixed effects in a linear mixed
model fitted with lme() in nlme package. The command I
used is:
## a simple randomized block design,
## type is fixed effect
## batch is random effect
## model with interaction
dat.lme<-lme(info.index~type, random=~1|batch/type,
data=dat)
glht(dat.lme, linfct = mcp(type
2007 Oct 07
1
multcomp and lme4
Dear list members,
Can anyone please point to an example of how to use glht(multcomp) with lmer
objects?
I am trying:
summary(glht(lmerObject, linfct = mcp(x = "Tukey")))
as I would for a glm object, but with no luck.
Thank you,
Andy.
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2008 Nov 18
1
Tukey HSD following lme
Hi everyone
I'm using Tukey HSD as post-hoc test following a lme analysis. I'm
measuring hemicelluloses in different species treated with three
different CO2 concentrations (l=low, m=medium, h=high). The whole
experiment is a split-plot design and the Tukey-function from the
package multcomp is suitable for lme-analysis with random factors.
The analysis works fine but I get a non