Hi Tanya,
Have you looked at the return value of pheatmap?
ret<-pheatmap(counts_filtered_df,scale="row",cluster_col=FALSE,
cluster_row=TRUE,border_color=NA,show_rownames = TRUE)
str(ret)
names(ret$tree_row)
names(ret$tree_col)
Look at what is in "ret" to see if your numeric matrix is hidden ther
Hi Pedro,
I have encountered similar situations in a number of areas. Great care
is taken to record significant events of low probability, but not the
non-occurrence of those events. Sometimes this is due to a problem
with the definition of non-occurrence. To use your example, how close
does an ani
On Mon, 3 Sep 2018, Sorkin, John wrote:
Might it help to take a random subset of the data and plot the sub set? If
the relation is linear you could include a regression line obtained from
the entire data set
John,
I'll definitely explore this option. Thanks for the idea.
Regards,
Rich
__
On Tue, 4 Sep 2018, Paul Murrell wrote:
Another option is to just rasterize the points (but leave the rest of the
plot vector). See ...
https://www.stat.auckland.ac.nz/~paul/Reports/rasterize/rasterize.html
Paul,
Thanks very much for the suggestion and URL.
Regards,
Rich
Hi
Another option is to just rasterize the points (but leave the rest of
the plot vector). See ...
https://www.stat.auckland.ac.nz/~paul/Reports/rasterize/rasterize.html
Paul
On 04/09/18 06:20, Bert Gunter wrote:
1. Plot a random sample of the points (e.g. of rows of matrix/dataframe
conta
On Mon, 3 Sep 2018, David L Carlson wrote:
If the plot is being displayed on a monitor, it is being bitmapped to the
resolution of the display device regardless of how you save it. Most
computer monitors are about 100dpi.
David,
I'm looking at the report on the monitor. I suspect that most
If the plot is being displayed on a monitor, it is being bitmapped to the
resolution of the display device regardless of how you save it. Most computer
monitors are about 100dpi.
If the problem is that the points are overprinting, Bert's suggestion to use
hexbin() is the way to go.
If the poin
On Mon, 3 Sep 2018, Bert Gunter wrote:
1. Plot a random sample of the points (e.g. of rows of matrix/dataframe
containing "x" and "y" columns
2. See the hexbin package
3. Check out the graphics taskview on cran:
https://cran.r-project.org/web/views/Graphics.html
(though it may be somewhat date
1. Plot a random sample of the points (e.g. of rows of matrix/dataframe
containing "x" and "y" columns
2. See the hexbin package
3. Check out the graphics taskview on cran:
https://cran.r-project.org/web/views/Graphics.html
(though it may be somewhat dated by now)
4. Internet search: e.g. on "d
Thank you for the feedback Petr, I was able to run the code without any
problems.
Best regads,
Paul
El lun., 3 sept. 2018 a las 9:57, PIKAL Petr ()
escribió:
> Hi
>
> AFAIK it was probably answered several days ago that mice conflicts with
> some other loaded package.
>
> I did not get any erro
Thank you all for your **very good** answers:
Using aovp(..., perm="Exact") seems to be the way to go for small datasets,
and also I should definitely try ?kruskal.test.
Juan
[[alternative HTML version deleted]]
__
R-help@r-project.org mailin
This may be an inappropriate forum for this question. If so, please point
me in a better direction.
A current project includes scatter plots with thousands of points. Saved
as PDF files they display slowly using a pdf viewer or when included in the
PDF output of a LaTeX document.
Is there
> This package uses a modified version of aov() function, which uses
> Permutation Tests
>
> I obtain different p-values for each run!
Could that be because you are defaulting to perm="Prob"?
I am not familiar with the package, but the manual is informative.
You may have missed something when r
Juan,
Your question might be borderline for this list, as it ultimately rather seems
a stats question coming in R disguise.
Anyway, the short answer is that you *expect* to get a different p value from a
permutation test unless you are able to do all possible permutation and
therefore use the
Dear Juan
I do not use the package but if it does permutation tests it presumably
uses random numbers and since you are not setting the seed you would get
different values for each run.
Michael
On 03/09/2018 16:17, Juan Telleria Ruiz de Aguirre wrote:
Dear R users,
I have the following Que
Dear R users,
I have the following Question related to Package lmPerm:
This package uses a modified version of aov() function, which uses
Permutation Tests instead of Normal Theory Tests for fitting an Analysis of
Variance (ANOVA) Model.
However, when I run the following code for a simple linear
Hi
AFAIK it was probably answered several days ago that mice conflicts with some
other loaded package.
I did not get any error.
After importing your data
str(dataFrame)
'data.frame': 383 obs. of 5 variables:
$ TransitDate: POSIXct, format: "1985-10-01 06:00:00" "1985-11-01 06:00:00" ...
$
You should post this on the r-sig-mixed-models list, not here.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Mon, Sep 3, 2018 at 7:43 AM Pedro V
We did a field study in which we tried to understand which factors
significantly explain the probability of a group of animals (5 species in
total) crossing through 30 wildlife road-crossing structures. The response
variable is binomial (yes=crossed; no = did not cross) and was recorded by
animal s
Dear friends,
It seems to me that there is something wrong with the complete function. I
am using R version 3.5.0 and mice package for data imputation.
I am working on a Windows 8.1 Enterprise machine with 64-bit Operating
System.
So here is my code:
Imputed_Data <- mice(dataFrame[2:5])
Complet
Hi
I am plotting a pheatmap using following line in a R code
pheatmap(counts_filtered_df,scale="row",cluster_col=FALSE,cluster_row=TRUE,border_color=NA,show_rownames
= T)
I want to extract the row names in the same order as shown in pheatmap and the
z scores for them.
So basically a num
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