Displaying 20 results from an estimated 3000 matches similar to: "Pairwise comparison, TukeyHSD, glht, ANCOVA"
2018 Apr 24
0
TukeyHSD and glht differ for models with a covariate
I have a question about TukeyHSD and the glht function because I'm
getting different answers when a covariate is included in model for
ANCOVA.? I'm using the cabbages dataset in the 'MASS' package for
repeatability.? If I include HeadWt as a covariate, then I get different
answers when performing multiple comparisons using TukeyHSD and the glht
function. The difference appears
2010 Mar 15
1
Multiple comparisons for a two-factor ANCOVA
I'm trying to do an ANCOVA with two factors (clipping treatment with two
levels, and plot with 4 levels) and a covariate (stem diameter). The
response variable is fruit number. The minimal adequate model looks like
this:
model3<-lm(fruit~clip + plot + st.dia + clip:plot)
I'd like to get some multiple comparisons like the ones from TukeyHSD, but
TukeyHSD doesn't work with the
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 Jan 02
1
Is using glht with "Tukey" for lme post-hoc comparisons an appropriate substitute to TukeyHSD?
Hello,
I am trying to determine the most appropriate way to run post-hoc
comparisons on my lme model. I had originally planned to use Tukey
HSD method as I am interested in all possible comparisons between my
treatment levels. TukeyHSD, however, does not work with lme. The
only other code that I was able to find, and which also seems to be
widely used, is glht specified with Tukey:
2011 Dec 11
2
multiple comparison of interaction of ANCOVA
Hi there,
The following data is obtained from a long-term experiments.
> mydata <- read.table(textConnection("
+ y year Trt
+ 9.37 1993 A
+ 8.21 1995 A
+ 8.11 1999 A
+ 7.22 2007 A
+ 7.81 2010 A
+ 10.85 1993 B
+ 12.83 1995 B
+ 13.21 1999 B
+ 13.70 2007 B
+ 15.15 2010 B
+ 5.69 1993 C
+ 5.76 1995 C
+ 6.39 1999
2013 Mar 13
1
multi-comparison of means
Hi all:
I have a question about multi-comparison.
The data is in the attachment.
My purpose:
Compare the predicted means of the 3 methods(a,b,c) pairwisely.
I have 3 ideas:
#idea1
result_aov<-aov(y~ method + x1 + x2)
TukeyHSD(result_aov)
diff lwr upr p adj
b-a 0.845 0.5861098 1.1038902 0.0000001
c-a 0.790 0.5311098 1.0488902 0.0000002
c-b -0.055 -0.3138902
2012 Feb 12
2
ANCOVA post-hoc test
Could you please help me on the following ANCOVA issue?
This is a part of my dataset:
sampling dist h
1 wi 200 0.8687212
2 wi 200 0.8812909
3 wi 200 0.8267464
4 wi 0 0.8554508
5 wi 0 0.9506721
6 wi 0 0.8112781
7 wi 400 0.8687212
8 wi 400 0.8414646
9 wi 400 0.7601675
10 wi 900 0.6577048
11 wi 900
2010 Mar 25
1
Expected pairwise.student.t and TukeyHSD behavior?
pairwise.t.test is returning NAs when one of the samples only has one entry, while TukeyHSD returns results (maybe not trustworthy or believable, but results).
I stumbled on this because I did not realize one of my samples only had one entry while most of the others had several hundred, so I realize this is not a desirable situation. I'm really just curious about the difference between how
2010 Jan 12
0
post-hoc after ancova
I have done ancova with categorical and continuous predictor variables.
The categorical predictor variable shows significant effect on the dependent
variable.
I would like to do a post-hoc test to see which groups in the categorical
variable differ.
I have explored Tukey test in multcomp package. My study is similar to the
"litter data". In the code it's mentioned that the contrast
2008 Jan 11
1
glht() and contrast() comparison
Hi, I have been trying glht() from multcomp package
and contrast() from contrast package to test a
contrast that I am interested in.
With the following simulated dataset (fixed effect
"type" with 3 levels (b, m, t), and random effect
"batch" of 4 levels, a randomized block design with
interaction), sometimes both glht() and contrast()
worked and gave nearly the same p values;
2012 May 16
1
TukeyHSD plot error
Hi, I am seeking help with an error when running the example from R
Documentation for TukeyHSD. The error occurs with any example I run, from
any text book or website. thank you...
> plot(TukeyHSD(fm1, "tension")).
Error in plot(confint(as.glht(x)), ylim = c(0.5, n.contrasts + 0.5), ...) :
error in evaluating the argument 'x' in selecting a method for function
2011 Sep 05
0
glht (multcomp): NA's for confidence intervals using univariate_calpha (fwd)
fixed @ R-forge. New version should appear on CRAN soon.
Thanks for the report!
Torsten
>
> ---------- Forwarded message ----------
> Date: Sat, 3 Sep 2011 23:56:35 +0200
> From: Ulrich Halekoh <Ulrich.Halekoh at agrsci.dk>
> To: "r-help at r-project.org" <r-help at r-project.org>
> Subject: [R] glht (multcomp): NA's for confidence intervals using
2005 Jul 15
1
Adjusted p-values with TukeyHSD (patch)
Dear R-developeRs,
Attached follows a patch against svn 34959 that adds the
printing of p-values to the TukeyHSD.aov function in stats package. I
also updated the corresponding documentation file and added a 'see also'
reference to the simint function of the multcomp package.
As it was already brought up in a previous thread [1] in R-help,
one can obtain the adjusted
2013 Jan 10
0
Questions about the glht function for planned comparison
Hi all,
I've posted this question before, but did not get any reply. I post it
again here and see if anybody can help. Thank you.
I have a nested model with the following effects
fixed: treatments
random: experiment_date
I used lme() to model the data
mod1 <- lme(N_cells ~treatments-1, random=~1|experiment_date, method='ML')
Then I want to compare all the other
2011 Sep 03
0
glht (multcomp): NA's for confidence intervals using univariate_calpha
Hej,
Calculation of confidence intervals for means
based on a model fitted with lmer
using the package multcomp
- yields results for calpha=adjusted_calpha
- NA's for calpha=univariate_calpha
Example:
library(lme4)
library(multcomp)
### Generate data
set.seed(8)
d<-expand.grid(treat=1:2,block=1:3)
e<-rnorm(3)
names(e)<-1:3
d$y<-rnorm(nrow(d)) + e[d$block]
2012 Jun 22
0
R: Error with glht function: Error in mcp2matrix(model, linfct = linfct) : Variable(s) 'Type' have been specified in 'linfct' but cannot be found in 'model'!
Hello everybody,
problem solved, there was a typo.
I wrote Type instead of Material
Best
----Messaggio originale----
Da: angelo.arcadi@virgilio.it
Data: 22-giu-2012 11.05
A: <r-help@r-project.org>
Ogg: Error with glht function: Error in mcp2matrix(model, linfct = linfct) : Variable(s) 'Type' have been specified in 'linfct' but cannot be found in 'model'!
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)),
2008 Sep 09
0
New member with question on multiple comparisons in mixed effects models
Dear fellow R.users/.lovers,
I am very new to both R and this list, so I hope you will be patient with me in the beginning if my enquiries are inappropriate/unclear.
I am trying to perform some rather complex statistical modelling using mixed-effects models.
I have, after a rather difficult beginning, finally boiled down my model (using the lme function in nlme) to a couple of fixed effects
2012 Jun 22
0
Error with glht function: Error in mcp2matrix(model, linfct = linfct) : Variable(s) 'Type' have been specified in 'linfct' but cannot be found in 'model'!
Dear list members,
I get the following error when using the glht function to perform a post hoc analysis for an ANOVA with repeated measures:
require(nlme)
lme_H2H_musicians = lme(H2H ~ Emotion*Material, data=musicians, random = ~1|Subject)
require(multcomp)
summary(glht(lme_H2H_musicians, linfct=mcp(Type = "Tukey")), test = adjusted(type = "bonferroni"))
Error in
2007 Aug 28
0
Problem with lme using glht for multiple comparisons
Hi everyone,
I am new to R and have a question that relates to unplanned post-hoc comparisons using the multcomp package after a mixed effects model. I couldn't find the answer to it in the archive or in any manual.
I have a dataset in which several plants have been treated in a particular way and a continuous response variable has been measured depending on several leaves per plant. I am