On May 16, 2014, at 2:07 PM, Tham Tran wrote:
> Dear Mr. Dalgaard,
You do realize that was a posting from 2012, right?
>
> Could you help me know the name of post-hoc multi-comparaison test mentioned
> in kruskal function of agricolae package?
There are multiple such tests mentioned on that fu
Dear Mr. Dalgaard,
Could you help me know the name of post-hoc multi-comparaison test mentioned
in kruskal function of agricolae package?
Thank you in advance.
Tham Tran
--
View this message in context:
http://r.789695.n4.nabble.com/Multiple-Comparisons-Kruskal-Wallis-Test-kruskal-agricolae-
Ariel THANK YOU for posting this. worked great for me just pasting this part
into R
Ariel wrote
> model.matrix.gls <- function(object, ...)
> model.matrix(terms(object), data = getData(object), ...)
>
>
> model.frame.gls <- function(object, ...)
> model.frame(formula(object), data
On Aug 7, 2012, at 18:48 , Bird_Girl wrote:
> I have looked into using glht (‘multcomp’ package) to do multiple comparisons
> for a model fit with GAM {mgcv} but after reading the description of the
> ‘multcomp’ package, I believe this method only applies to parametric models
> and linear hypothe
I have looked into using glht (‘multcomp’ package) to do multiple comparisons
for a model fit with GAM {mgcv} but after reading the description of the
‘multcomp’ package, I believe this method only applies to parametric models
and linear hypotheses. When I ran the code glht(model,linfct….) I got a
I see.. So apparently the different functions are doing the same thing! :-)
Besides I didn't know the groups should have about the same size.
Thank you four your time Mr. Dalgaard.
--
View this message in context:
http://r.789695.n4.nabble.com/Multiple-Comparisons-Kruskal-Wallis-Test-kruskal-ag
On Aug 3, 2012, at 18:49 , David L Carlson wrote:
> Generally multiple comparisons are conducted after a test for a significant
> difference among any of the groups. For your data
>
>> kruskal.test(x[,1]~x[,2])
>
>Kruskal-Wallis rank sum test
>
> data: x[, 1] by x[, 2]
> Kruskal-Wal
From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of peter dalgaard
> Sent: Friday, August 03, 2012 5:59 AM
> To: greatest.possible.newbie
> Cc: r-help@r-project.org
> Subject: Re: [R] Multiple Comparisons-Kruskal-Wallis-Test:
> kruskal{agricolae
On Aug 3, 2012, at 11:33 , greatest.possible.newbie wrote:
> Thank you for your answer.
> The p.adj argument in the kruskal()-function doesn't seem to change
> anything... Not even the "bonferroni"-method although it is described as the
> most conservative one (multiplying all p-values with the n
Thank you for your answer.
The p.adj argument in the kruskal()-function doesn't seem to change
anything... Not even the "bonferroni"-method although it is described as the
most conservative one (multiplying all p-values with the number of
comparisons). I suppose the kruskal()-function is not workin
On Aug 2, 2012, at 21:09 , Bird_Girl wrote:
> Hi,
>
> I have a question regarding whether it is possible to do post hoc tests on a
> model fit with GAM {mgcv}. My response variable is abundance (no.
> individuals per plot), and I have one continuous predictor (light) and one
> factor (height) w
On Aug 3, 2012, at 07:42 , greatest.possible.newbie wrote:
> I am doing multiple comparisons for data that is not normally distributed.
> For this purpose I tried both functions kruskal{agricolae} and
> kruskalmc{pgirmess}. It confuses me that these functions do not yield the
> same results altho
Hi there,
I am doing multiple comparisons for data that is not normally distributed.
For this purpose I tried both functions kruskal{agricolae} and
kruskalmc{pgirmess}. It confuses me that these functions do not yield the
same results although they are doing the same thing, don't they? Can anyone
Hi,
I have a question regarding whether it is possible to do post hoc tests on a
model fit with GAM {mgcv}. My response variable is abundance (no.
individuals per plot), and I have one continuous predictor (light) and one
factor (height) which includes 7 levels.
> mod2=gam(log_abundance~s(light
racmar wrote
>
> I have also been searching various forums and books to see if there are
> any methods I could use and have only found people, such as yourself,
> asking the same question.
>
I was looking into this recently, as well, and found that the problem has to
do with building the model
Hi Sandy,
I was wondering if you ever recieved an answer regarding the use of multiple
comparisons for gls models?
I have also been searching various forums and books to see if there are any
methods I could use and have only found people, such as yourself, asking the
same question.
Many thanks
Dear list,
Please excuse my ignorance, but I'm trying to model some data using the lme
package. vot is a numeric response, and condition, location and obs are all
categories.
This works:
> anova(vot.lme <- lme(vot ~ condition * location *
obs,data=mergedCodesL,random= ~1 |patient))
The last model is:
AnovaModel.4 <- lmer(VR ~ trat+(1|patient:trat), data=Mesures)
Sorry.
El 06/02/12 13:43, José Trujillo Carmona escribió:
> Dear professors and collegues
>
> I need to perform a analysis of dates from a nested experimental design.
>
> From
>
> "Bioestatical Analysis" of Zar
>
Dear professors and collegues
I need to perform a analysis of dates from a nested experimental design.
From
"Bioestatical Analysis" of Zar
"Bimetry of Sokal" & Rohlf
"Design and Analysis of Experiments" of Montgomery
I have:
Sum (mean(x)_i - mean(x)_T)2 / (a-1) -> var(epsilon) + n sigma2_B +
Hi ExpeRts,
I find this post very interesting... as I have the same problem. I found on
other webpages that a solution would be to run simple comparison and then to
adjust for the P-value accordingly; However, I am not very satisfied by such
approach, and i would very greatful to R if a multiple c
: "Richard M. Heiberger"
Gesendet: 27.04.2011 16:58:43
An: elgo...@web.de
Betreff: Re: [R] multiple comparisons on a between factor
Lisa,
Please look at some of the demos in the HH package.
These are built on the capabilities of the glht function in th
Lisa,
Please look at some of the demos in the HH package.
These are built on the capabilities of the glht function in the multcomp
package.
## install.packages("HH") ## if necessary
library(HH)
demo("MMC.WoodEnergy-aov", package="HH") ## first
demo("MMC.WoodEnergy", package="HH") ## second
Ri
Dear list,
im facing an issue of statistical data analysis that I consider myself
unable to resolve in R so i hope to get some valuable insights from you. i
run an ANOVA with four factors; factor4 is an between factor (two different
groups measured), the others are withins (tested a
Dear R users,
I have used the following model:
M1 <- gls(Nblad ~ Concentration+Season + Concentration:Season, data=DDD,
weights=varIdent(form=~ 1 | Season*Concentration))
to assess the effect of Concentration and Season on nitrogen uptake by
leaves (Nblad). I accounted for the difference in vari
Dear list members,
I have a question concerning multiple comparisons after using glm.
My response variable is days until emergence of an insect species. The
explanatory variables are sex (two levels), parasitoids added (two levels) and
populations (34 levels). I would like to know now which po
boun...@r-project.org [mailto:r-help-boun...@r-project.org]
On
> Behalf Of Bert Gunter
> Sent: April-16-10 4:19 PM
> To: 'Kaufman Gabriel'; r-help@r-project.org
> Subject: Re: [R] Multiple comparisons on Anova.mlm object
>
> Gabriel:
>
> The post hoc comparis
stics
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Kaufman Gabriel
Sent: Friday, April 16, 2010 12:32 PM
To: r-help@r-project.org
Subject: [R] Multiple comparisons on Anova.mlm object
I would like to perform multiple comparisons
I would like to perform multiple comparisons or post-hoc testing on the
independent variable in an Anova.mlm object generated by the Anova function of
the car package. I have defined a multivariate linear model and subsequently
performed a repeated measures ANOVA as per the instructions in secti
On Wed, Apr 7, 2010 at 9:25 PM, Eric Scott wrote:
> Thank you for your reply. The WoodEnergy example helped a lot. I
> understand now that it is inappropriate to make all pairwise comparisons
> with an interaction present and better to make comparisons between levels of
> one factor within a co
Thank you for your reply. The WoodEnergy example helped a lot. I
understand now that it is inappropriate to make all pairwise comparisons
with an interaction present and better to make comparisons between levels of
one factor within a constant level of the second factor. As I understand it,
the s
In addtition to the example I mentioned previously,
demo("MMC.WoodEnergy-aov", "HH")
Please also see
demo("MMC.WoodEnergy", "HH")
In this example, since anova(energy.aov.4),
shows that the Wood factor and Stove:Wood interaction are significant,
all possible pairwise comparisons of the 12 Stove:Wo
Thanks for the example, but I'm still not sure from this example how to see
the pairwise comparisons for the interaction. For example, if I have two
factors, X and Y; and X has 2 levels, A and B, and Y has 3 levels, 1, 2, and
3, a TukeyHSD would give the following comparisons with p-values for eac
Please see the maiz example in ?MMC in the HH package.
maiz is the last example in the help file. Keep going all the way to the
end of
the help file. See also the
demo("MMC.WoodEnergy-aov", "HH")
These examples show how to use glht in the presence of interactions and
covariates.
Rich
Eric Scott-3 wrote:
>
> 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
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 mu
Hello,
I have used R in the past to conduct multiple comparisons on standard linear
models, but am a bit confused as to how to go about doing it with a mixed
effects model.
I am conducting a bioindication study using carabid beetles in which I have
four treatment types (forest harvest types wi
I've forgot to cite the Games and Howell procedure, which some
literature (eg in "Pairwise Multiple Comparison Procedures: A Review"
[1]) referes as a good one for uneual samples and non homogeneus
variance. Yet I haven't found an implementation for R...
giovanni
[1] http://psycnet.apa.org/journa
This is a mixed question, between theory and practice.
I have a dataset with a continous variable grouped by a 33 levels
factor. After having log-tranformed my original data I can assume the
normality of my data but I have two strong departures from the basic
assumptions for anova and t tests: *unb
Hello, all R-users!
I am working on fitting a survival analysis model using the coxph
function for Cox proportional hazards regression model. Data look like
usual:
==
group blockdeathcensor
Group1 1 4 1
Group1 1 12 1
..
Dear R users,
I 'm working on a dataset consisting of 4 different dataframes with
tree, leaf, fruit and seed measurements made on 300 trees, coming from
10 provenances (30 trees per provenance, 10 leaves/fruits/seeds per
tree). Provenances are fixed effects (they were not randomly chosen),
but
On Sun, 22 Mar 2009, lara harrup (IAH-P) wrote:
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 (va
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 diffe
Hi all,
This is both a general and r specific question. I am analyzing some
morphological data and have performed a MANOVA on the 5 groups with 5
measured morphological traits. The results provide me with some pairwise
comparisons, but I would like to run a multiple comparison test on the
result
Hello again R help.
This is a simple question perhaps(laughing as I type) with a simple answer.
Is the multcomp package appropriate for using with a lme built under
the nlme package.
I know i can get it to work (i.e report p-values for a tukey test) but
i am not unsure if this is appropriate fo
Good morning,
I'm trying to write a function to assign the mean separation grouping
letters to the factors of an experiment. A client wants this so I
thought I'd write my own function for future use. But I'm having
trouble doing it because I don't understand the logic of the
assignment. As
On 2007-11-23, hadley wickham <[EMAIL PROTECTED]> wrote:
>>
>> What I need is a reference to the tests implemented in glht, so I can
>> decide which one is appropriate for my data. Sequen, Changepoint et
>> al. may be common terms in some fields, but not in the references I'm
>> working from.
>
> H
> However, I don't know what exactly glht does, and the help file is
> extremely terse. It offers the following options (in contrMat()):
>
> contrMat(n, type=c("Dunnett", "Tukey", "Sequen", "AVE",
> "Changepoint", "Williams", "Marcus",
> "McDermo
Thank you for your response. I think you have misunderstood what I'm
asking, though.
On 2007-11-23, Emmanuel Charpentier <[EMAIL PROTECTED]> wrote:
>
> - Tukey HSD will enable you to test the p(p-1)/2 pair differences one
> can create with p groups ;
> - Dunnett's procedure is made to compare (p-
Tyler Smith a écrit :
> Hi,
>
> I'm trying to make sense of the options for multiple comparisons
> options in R. I've found the following options:
[ Snip ... ]
> As I understand it, there is no universal consensus as to which test
> is best.
There is no such thing. Each of the procedures is aim
Hi,
I'm trying to make sense of the options for multiple comparisons
options in R. I've found the following options:
pairwise.t.test, which provides standard t-tests, with options for
choosing an appropriate correction for multiple comparisons
TukeyHSD, which provides the usual Tukey test
glht(
Dear List-Members,
Is the application of multiple comparison procedures (using the multcomp
package) to the output from a rank-based ANOVA straightforward, or do I need
to take heed ?
That is, is it as simple as:
glht( aov(rank(NH4) ~ Site, data=mydat), linfct=mcp(Site="Tukey") )
Thanks in adv
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