Dear Vera,
I had a similar problem once and as far as I can remember the reason were
some negative inputs or outputs.
# Check for negative values
x1a.neg <- apply(x1a, 1, function(x) any(x<0))
y1.neg <- apply(y1, 1, function(x) any(x<0))
exclude <- x1a.neg | y1.neg
# Exclude negative rows
x1a.ne
Ok I see that point with the quotes. But what I want to do still doesn't
work:
a <- matrix(1:15,ncol=3)
b <- paste( paste("a[," ,paste(1:3), "]",sep="")
,"^",1:3,sep="",collapse="+")
b
#[1] "a[,1]^1+a[,2]^2+a[,3]^3"
#instead of (which I want)
a[,1]^1+a[,2]^2+a[,3]^3
#[1] 1368 1779 2264 2829 3480
Thank you for your improvement suggestions.
I forgot to write the I() and it's clear that I have to specify the data
(which I was indicating with the '...' without explicitly writing it).
In this special case where I need a formula you really helped me with the
function as.formula() to transform t
Dear R community,
I have been spending hours to solve my problem myself but I'm just unable to
do it. So excuse me if I demand your time.
My problem is, that inside my function, I want to pass conditional arguments
(that are the result of a loop) into a function.
I am sure that my approach is kind
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
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
I didn't do any research about this but I think it's the following:
If you run several t-tests to compare groups and then do a tukey-HSD you
won't get the same results either.
It's the same with the kruskal-wallis test. This happens because the
variance that is used for computing significant differ
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
Dear community,
I have a little problem filling in a vector with loop output.
I think the problem is the following: I am caluclating the index which
indicates where the loop output should be placed at by a formula. But when I
want to fill in or read out some specific index places this doesn't work
Dear R community.
I am using the identify() function to identify outliers in my dataset.
This is the code I am using:
# Function to allow identifying points in the QQ plot (by mouseclicking)
qqInteractive <- function(..., IDENT
Hi there,
To see the results of my clustering graphically I was using clusplot. But it
only provides a look at the two most important components of the dataset.
I recently found the Mclust() function which produces very nice colored pair
plots for the clustered dataset.
see Graph: http://www.stat
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