ok, I would have paired = FALSE option. What other options you would recommend?
On Tue, Oct 22, 2019 at 8:34 PM Bert Gunter wrote:
>
> "How would I do say t test considering these two have different number
> of entries?"
>
> Read and follow ?t.test .
>
> Bert Gunter
>
> "The trouble with having
"How would I do say t test considering these two have different number
of entries?"
Read and follow ?t.test .
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
Hello,
I would like to calculate a p value from two distributions, one looks like this:
> head(b)
gene_id number_of_eqtles_per_gene
1: ENSG0237683.5 5
2: ENSG0225972.1 267
3: ENSG0225630.197
4:
Notice that I used the argument stringsAsFactors=FALSE to do this when
reading in.
What I did was to change Rn columns to 1 if there were any characters
in the corresponding Runnerxxx column and 0 otherwise. The "nchar"
function returns the number of characters in a string. If I apply ">0"
to it,
On 10/22/19 1:54 PM, Phillip Heinrich wrote:
Row Outs RunnerFirst RunnerSecond RunnerThird R1 R2 R3
1 0
2 1
3 1
4 1 arenn001
5 2 arenn001
6 0
7 0 perad001
8 0 polla001 perad001
9 0 goldp001 polla001 perad001
10 0
Hi Philip,
Try this:
phdf<-read.table(
text="Row Outs RunnerFirst RunnerSecond RunnerThird R1 R2 R3
1 0
2 1
3 1
4 1 arenn001
5 2 arenn001
6 0
7 0 perad001
8 0 polla001 perad001
9 0 goldp001 polla001 perad001
10 0 lambj001 goldp001
11 1 lambj001 goldp001
12 2 lambj001
13 0
14 1 ",
Hi Yeasmin,
I suspect that you didn't intend to have conditions like:
a<0 && b>0 && b 0 && abs(b) < abs(a)
If this is the case, the following function seems to return the values
of phase that you want:
assign_phase<-function(x,y) {
phase<-c(1,2,7,8,3,4,6,5)
phase_index<-4 * (x > 0) + 2 * (y >
Row Outs RunnerFirst RunnerSecond RunnerThird R1 R2 R3
1 0
2 1
3 1
4 1 arenn001
5 2 arenn001
6 0
7 0 perad001
8 0 polla001 perad001
9 0 goldp001 polla001 perad001
10 0
I believe this is correct behavior for computing the p-value, though the
wording is awkward in that it implies that R is not implementing the
continuity correction in this situation, when in fact, this behavior is
part of how the continuity correction is defined. The correction simply
treats the
I thought it was a major package for ecological analysis. Anyway,
thank you for the tips. I'll dip from there.
On Tue, Oct 22, 2019 at 5:29 PM Jeff Newmiller wrote:
>
> Probably, assuming that function returns a ggplot object. You will need to
> identify the levels of the factor used for
Both your syntax and semantics are wrong. This indicates to me that you
should spend more time with some basic R tutorials before proceeding.
That said, here are some of the errors:
1) You are not using sapply correctly. Moreover, no R level iteration is
needed anyway (sapply() iterates over
Here is another way of doing it by computing the index based on the
conditions
> input <- read_delim(" YEAR DAY X Y Sig
+ 1981 9 -0.213 1.08 1.10
+ 198110 0.065 1.05 1.05", delim = ' ', trim_ws = TRUE)
>
> input <- mutate(input,
+ phase = case_when(X < 0 & Y < 0 &
Had the condition for phase=2 incorrect:
library(tidyverse)
input <- read_delim(" YEAR DAY X Y Sig
1981 9 -0.213 1.08 1.10
198110 0.065 1.05 1.05", delim = ' ', trim_ws = TRUE)
input <- mutate(input,
phase = case_when(X < 0 & Y < 0 & Y < X ~ 'phase=1',
Here is one way of doing it; I think the output you show is wrong:
library(tidyverse)
input <- read_delim(" YEAR DAY X Y Sig
1981 9 -0.213 1.08 1.10
198110 0.065 1.05 1.05", delim = ' ', trim_ws = TRUE)
input <- mutate(input,
phase = case_when(X < 0 & Y < 0 & Y <
Hello Team
I would like to add a new column (for example-Phase) from the below data
set based on the conditions
YEAR DAY X Y Sig
1 1981 9 -0.213 1.08 1.10
2 198110 0.065 1.05 1.05
*Conditions*
D$Phase=sapply(D,function(a,b) {
a <-D$X
b<-D$Y
if (a<0
Hi Bert,
thanks for the quick reply. I spent a while searching before I posted, and
also read through the documentation for the mle fn and the maxLik and
bbmle packages. As you say, it seems likely I'm reinventing something
standard, but nothing I can find quite seems to do what I need. Hence
Dear David, Dear Jiefei,
Many thanks for your comments. I got it now. It works.
Best,
Sacha
Le lundi 21 octobre 2019 à 22:00:39 UTC+2, David Winsemius
a écrit :
On 10/21/19 9:40 AM, varin sacha via R-help wrote:
> Dear R-Experts,
>
> Here below my reproducible example working but
Probably, assuming that function returns a ggplot object. You will need to
identify the levels of the factor used for distinguishing groups, and add a
scale_colour_manual() to the ggplot object with colors specified in the same
order as those levels.
Support for obscure packages is technically
Dear all,
is it possible to provide custom color to the rarefaction curve of the
package iNEXT (ggiNEXT)?
If I have these data:
```
library(iNEXT)
library(ggplot2)
data(spider)
out <- iNEXT(spider, q=0, datatype="abundance")
ggiNEXT(out, type=1)
```
can i colour the lines with, let's say, yellow
Dear all,
version 2.0-1 of package NMOF is on CRAN now.
NMOF stands for 'Numerical Methods and Optimization in Finance',
and it accompanies the book with the same name, written by
Manfred Gilli, Dietmar Maringer and Enrico Schumann.[1]
The new version of the package provides all R code and
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