Displaying 20 results from an estimated 3000 matches similar to: "How Rcmdr or na.exclude blocks TukeyHSD"
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
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
2006 Aug 01
1
plot() with TukeyHSD
Hello,
When plotting the results of a TukeyHSD multiple comparisons
procedure with an ANOVA (lm) object, an extra line appears
in the confidence intervals that contain 0. For example (this
is straight from the TukeyHSD helpfile):
> summary(fm1 <- aov(breaks ~ wool + tension, data = warpbreaks))
> TukeyHSD(fm1, "tension", ordered = TRUE)
> plot(TukeyHSD(fm1,
2003 Dec 08
0
TukeyHSD changes if I create interaction term
Dear R community,
I'm trying to understand this behavior of TukeyHSD. My goal is to obtain
defensible, labelled multiple comparisons of an interaction term.
Firstly, if I plot the TukeyHSD from the model that calculates its own
interactions, then the y-axis labels appear to be reflected on their median
when compared to the text output of the TukeyHSD statement. The labels are
integers.
2004 May 14
0
Work around for TukeyHSD names
R Version 2.0.0 Under development (unstable) (2004-05-07)
I have been wanting to use TukeyHSD for two and three way aov's, as
they are especially simple for students to use correctly. I realize
model simplification is usually a preferred methodology, but my
disciplinary enertia and simplicity of TukeyHSD is also compelling.
However, the lack of names on the comparisons for the higher order
2006 Jan 15
1
Multiple comparison and two-way ANOVA design
Dear useRs,
I'm working on multiple comparison design on two factor (2 3 levels)
ANOVA. Each of the tests I have tried (Tukey, multcomp package) seem to
do only with one factor at a time.
fm1 <- aov(breaks ~ wool * tension, data = warpbreaks)
tHSD <- TukeyHSD(fm1, "tension", ordered = FALSE)
$tension
diff lwr upr p adj
M-L -10.000000 -19.35342
2005 May 15
3
adjusted p-values with TukeyHSD?
hi list,
i have to ask you again, having tried and searched for several days...
i want to do a TukeyHSD after an Anova, and want to get the adjusted
p-values after the Tukey Correction.
i found the p.adjust function, but it can only correct for "holm",
"hochberg", bonferroni", but not "Tukey".
Is it not possbile to get adjusted p-values after
2011 Jun 03
1
Outputting data from TukeyHSD
Hi All,
I am wondering if their is a convenient way to export the results of the
TukeyHSD function to Word or Excel.
I have used capture.output(tukey.contrast, file="tukey.contrast.xls")
and this works, but the data are not in a table form, and so it is sort of a
pain to manipulate the output.
I have be unable to find a more convenient way and any assistance would be
greatly
2008 Apr 28
0
restricting pairwise comparisons of interaction effects
I'm interested in restricting the pairwise comparisons of interaction
effects in a multi-way factorial ANOVA, because I find comparisons of
interactions between all different variables different to interpret.
For example (supposing a p<0.10 cutoff just to be able to use this
example):
> summary(fm1 <- aov(breaks ~ wool*tension, data = warpbreaks))
Df Sum Sq Mean Sq F
2012 Dec 13
1
Physically extracting P-value from TukeyHSD test output
Hey,
I have this TukeyHSD output from which I would like to extract only the
P-values (p adj, last number).
The problem is that the test output is a character list.
How can I "break" this sentence to separate the Pv?
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Fe1$Fe ~ Fe1$genotype)
$`Fe1$genotype`
diff lwr upr
2018 Jan 09
0
SpreadLevelPlot for more than one factor
Dear Sir,
Many thanks for your reply.
I have a query.
I have a whole set of distributions which should be made normal /
homoscedastic. Take for instance the warpbreaks data set.
We have the following boxplots for the warpbreaks dataset:
a. boxplot(breaks ~ wool)
b. boxplot(breaks ~ tension)
c. boxplot(breaks ~ interaction(wool,tension))
d. boxplot(breaks ~ wool @ each level of tension)
e.
2018 Jan 14
1
SpreadLevelPlot for more than one factor
Dear Ashim,
I?ll address your questions briefly but they?re really not appropriate for
this list, which is for questions about using R, not general statistical
questions.
(1) The relevant distribution is within cells of the wool x tension
cross-classification because it?s the deviations from the cell means that
are supposed to be normally distributed with equal variance. In the
warpbreaks data
2018 Jan 07
2
SpreadLevelPlot for more than one factor
Dear All,
I want a transformation which will make the spread of the response at all
combinations
of 2 factors the same.
See for example :
boxplot(breaks ~ tension * wool, warpbreaks)
The closest I can do is :
spreadLevelPlot(breaks ~tension , warpbreaks)
spreadLevelPlot(breaks ~ wool , warpbreaks)
I want to do :
spreadLevelPlot(breaks ~tension * wool, warpbreaks)
But I get :
>
2008 May 01
0
customization of pairwise comparison plots
I am wondering how to customize a pairwise comparisons plot of a factorial
ANOVA, without doing a lot of manual manipulation of a TukeyHSD object. The
customizations I'd like are:
1. The aov used log-transformed response data, but I'd like to plot the
intervals on their original, untransformed scales
2. Plot all the main and interaction effects together, rather than in a
separate
2018 Jan 07
2
SpreadLevelPlot for more than one factor
Dear Ashim,
Try spreadLevelPlot(breaks ~ interaction(tension, wool), data=warpbreaks) .
I hope this helps,
John
-----------------------------
John Fox, Professor Emeritus
McMaster University
Hamilton, Ontario, Canada
Web: socialsciences.mcmaster.ca/jfox/
> -----Original Message-----
> From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Ashim
> Kapoor
> Sent:
2018 Jan 07
0
SpreadLevelPlot for more than one factor
Dear All,
we need to do :
library(car) for the spreadLevelPlot function
I forgot to say that.
Apologies,
Ashim
On Sun, Jan 7, 2018 at 10:37 AM, Ashim Kapoor <ashimkapoor at gmail.com> wrote:
> Dear All,
>
> I want a transformation which will make the spread of the response at all
> combinations
> of 2 factors the same.
>
> See for example :
>
>
2012 Nov 29
2
Deleting certain observations (and their imprint?)
I'm manipulating a large dataset and need to eliminate some observations based on specific identifiers. This isn't a problem in and of itself (using which.. or subset..) but an imprint of the deleted observations seem to remain, even though they have 0 observations. This is causing me problems later on. I'll use the dataset warpbreaks to illustrate, I apologize if this isn't in
2012 Jul 27
1
Understanding the intercept value in a multiple linear regression with categorical values
Hi!
I'm failing to understand the value of the intercept value in a
multiple linear regression with categorical values. Taking the
"warpbreaks" data set as an example, when I do:
> lm(breaks ~ wool, data=warpbreaks)
Call:
lm(formula = breaks ~ wool, data = warpbreaks)
Coefficients:
(Intercept) woolB
31.037 -5.778
I'm able to understand that the value of
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:
2006 Aug 02
2
best way to calculate per-parameter differences in across-subject means
Hello,
I have some data in a data.frame where for each of a number of
subjects, I have scores for all of a number of symptoms.
Subjects are subdivided in a number of groups, which have unequal sizes.
I'd like to plot between-group differences in the scores on the
various symptoms. Ideally, that would be in a form as would be
produced by
> bwplot( Score~Symptom )
but I'm not sure