Displaying 20 results from an estimated 2000 matches similar to: "Post Hoc Groupings"
2010 Oct 28
1
xyplot and panel.curve
Hi All
I have regression coefficients from an experiment and I want to plot them
in lattice using panel curve but I have run into error messages.
I want an 3 panel conditioned plot of 2 curves of Treatment 2 in each panel
conditioned by Treatment1, the example curve expression is x+value*x^2
A rough toy example to give an idea of what I want is:
Data:
data = expand.grid(Treatment1 =
2008 Sep 17
1
ANOVA contrast matrix vs. TukeyHSD?
Dear Help List,
Thanks in advance for reading...I hope my questions are not too ignorant.
I have an experiment looking at evolution of wing size [centroid] in
fruitflies and the effect of 6 different experimental treatments
[treatment]. I have five replicate populations [replic] in each
treatment and have reared the flies in two different temperatures [cond]
to assay the wing size, making
2010 May 18
1
proportion of treatment effect by a surrogate (fitting multivariate survival model)
Dear R-help,
I would like to compute the variance for the proportion of treatment
effect by a surrogate in a survival model (Lin, Fleming, and De
Gruttola 1997 in Statistics in Medicine). The paper mentioned that
the covariance matrix matches that of the covariance matrix estimator
for the marginal hazard modelling of multiple events data (Wei, Lin,
and Weissfeld 1989 JASA), and is implemented
2008 Feb 03
1
Effect size of comparison of two levels of a factor in multiple linear regression
Dear R users,
I have a linear model of the kind
outcome ~ treatment + covariate
where 'treatment' is a factor with three levels ("0", "1", and "2"),
and the covariate is continuous. Treatments "1" and "2" both have
regression coefficients significantly different from 0 when using
treatment contrasts with treatment "0" as the
2008 Oct 10
1
Correlation among correlation matrices cor() - Interpretation
Hello,
If I have two correlation matrices (e.g. one for each of two treatments) and
then perform cor() on those two correlation matrices is this third
correlation matrix interpreted as the correlation between the two
treatments?
In my sample below I would interpret that the treatments are 0.28
correlated. Is this correct?
> var1<- c(.000000000008, .09, .1234, .5670008, .00110011002200,
2008 Oct 09
1
Interpretation in cor()
Hello,
I am performing cor() of some of my data. For example, I'll do 3 corr()
(many variables) operations, one for each of the three treatments.
I then do the following:
i <-lower.tri(treatment1.cor)
cor(cbind(one = treatment1.corr[i], two = treatment2.corr[i], three =
treatment3.corr[i]))
Does this operation above tell me how correlated each of the three
treatments is? Because this
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
2006 Sep 05
1
help: advice on the structuring of ReML models for analysing growth curves
Hi R experts,
I am interested on the effects of two dietry compunds on the growth of
chicks. Rather than extracting linear growth functions for each chick and
using these in an analysis I thought using ReML might provide a neater and
better way of doing this. (I have read the pdf vignette("MlmSoftRev") and
"Fitting linear mixed models in R" by Douglas Bates but I am not
2006 May 09
2
post hoc comparison in repeated measure
Hi, I have a simple dataset with repeated measures.
one factor is treatment with 3 levels (treatment1,
treatment2 and control), the other factor is time (15
time points). Each treatment group has 10 subjects
with each followed up at each time points, the
response variable is numeric, serum protein amount. So
the between subject factor is treatment, and the
within subject factor is time. I ran a
2010 Jul 07
0
interaction post hoc/ lme repeated measures
Hi Everyone,
I’m trying to figure out how to get R to analyze this experiment properly. I have a series of subjects each with two legs. Within each leg there are two bones that I am interested in. There are also two treatments that I am interested in. That results in four different combinations of treatments. Obviously, since the subjects only have two legs, they can’t receive each
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
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
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,
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
2002 Sep 11
0
Contrasts with interactions
Dear All,
I'm not sure of the interpretation of interactions with contrasts. Can anyone help?
I do an ANCOVA, dryweight is covariate, block and treatment are factors, c4 the response variable.
model<-aov(log(c4+1)~dryweight+treatment+block+treatment:block)
summary(model);
Df Sum Sq Mean Sq F value Pr(>F)
dryweight 1 3.947 3.947 6.6268 0.01076 *
2012 Sep 28
1
Anova and tukey-grouping
Hello,
I am really new to R and it's still a challenge to me.
Currently I'm working on my Master's Thesis. My supervisor works with SAS
and is not familiar with R at all.
I want to run an Anova, a tukey-test and as a result I want to have the
tukey-grouping ( something like A - AB - B)
I came across the HSD.test in the agricolae-package, but... unfortunately I
do not get an output
2008 May 28
2
Tukey HSD (or other post hoc tests) following repeated measures ANOVA
Hi everyone,
I am fairly new to R, and I am aware that others have had this
problem before, but I have failed to solve the problem from previous
replies I found in the archives.
As this is such a standard procedure in psychological science, there
must be an elegant solution to this...I think.
I would much appreciate a solution that even I could understand... ;-)
Now, I want to calculate a
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
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
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: