and yes I can sleep well now. Thank you, Jim.
ne<-rep(0,ne)
total<-c(neggg,posss,ne)
hist(total)
Best,
Ani Jaya
On Tue, Sep 7, 2021 at 9:38 AM ani jaya wrote:
>
> Hello Jim, thank you for your response. What I am trying to achieve is
> like this:
>
> #calculate the positive significant station
Hello Jim, thank you for your response. What I am trying to achieve is
like this:
#calculate the positive significant station for every row based on p-value
df5<-df3
df5[df4>0.05|df5<0]<-NA
#remove the insignificant one or negative statistic value
df5[df5>0]<-1
#change the positi
Dear spm users and all,
I am glad to inform you that the spm package is available on CRAN again. It
is an updated version with a few bugs fixed. Please note that some
functions in the package are not only for spatial predictive modelling but
also for general predictive modeling.
Please feel free
You get this error from this kind of operation on tibbles:
library(tibble)
t1 <- tibble(x = c(TRUE, FALSE))
t2 <- tibble(x = c(1.2, 1.3))
t1[1,] <- t2[1,]
#> Error: Assigned data `t2[1, ]` must be compatible with existing data.
#> ℹ Error occurred for column `x`.
#> x Can't convert from to due
> Run `rlang::last_error()` to see where the error occurred
What did rlang::last_error() show?
-Bill
On Mon, Sep 6, 2021 at 9:19 AM John Tully
wrote:
> Dear colleagues
> >
> > in conducting a meta-analysis (of MRI data) I am running into the
> repeated issue:
> >
> > Error: Assigned data `si
On 06/09/2021 10:16 a.m., John Tully wrote:
Dear colleagues
in conducting a meta-analysis (of MRI data) I am running into the repeated
issue:
Error: Assigned data `single_study_df` must be compatible with existing data. ℹ Error
occurred for column `accumbens_sd`. x Can't convert from to
d
Dear colleagues
>
> in conducting a meta-analysis (of MRI data) I am running into the repeated
> issue:
>
> Error: Assigned data `single_study_df` must be compatible with existing data.
> ℹ Error occurred for column `accumbens_sd`. x Can't convert from to
> due to loss of precision. * Location
On 06/09/2021 11:06 a.m., Ivan Calandra wrote:
Yes Jeff, you are right. I hate manually editing figures too, but
sometimes I find it's still the easiest way (e.g. when you submit your
paper several times when journals have differing guidelines, or when you
build figures from several (sub)plots +
Yes Jeff, you are right. I hate manually editing figures too, but
sometimes I find it's still the easiest way (e.g. when you submit your
paper several times when journals have differing guidelines, or when you
build figures from several (sub)plots + other images, or when you
combine plots that
I don't always use rmarkdown to write papers either, but you can capture
figures from it. I avoid hand editing figures like the plague of
irreproducibility. But sometimes you get stuck in an approach... I cannot
answer your original post, but wanted to point out that it may not actually be
nece
Thank you Jeff for your answer.
I do use rmarkdown but I do not write papers completely with it. I do
output a report in HTML but I also like to export the plots as PDF so
that I can edit them (using Inkscape or similar) if and as needed.
And because I like to have both the HTML report includin
I use an rmarkdown file to generate consistent output figures and tables for
html or Word. I just use Rnw files directly if I am generating LaTeX. I do not
use R files for building output... and I never use ggsave. So you might
consider altering your approach to bypass the question entirely.
On
Dear useRs,
I produce several independent ggplot2 plots and I would like to save
them to a fixed width (for publications), but the height (and therefore
aspect ratio) is different from plot to plot.
How can I save my plots with ggsave() supplying only a fixed width but
without knowing the he
Hi Bert and All: good morning
I promise this would be the last time to write about this topic.
I come up with this R function (please see below), for sure with your help.
It works for all sample sizes. I also provided three different simple
examples.
with many thanks
abou
##
HI Ani,
I would create these two matrices:
# matrix of logicals for positive stat values
posvalue<-df3 > 0
# matrix of logicals for significance
sigstat<-df4 < 0.05
Then you can identify the positive/negative and significant values:
which(posvalue & sigstat)
[1] 12
which(!posvalue & sigstat)
[1]
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