Before posting on the r-help list I did run Rprof(). In my posting I asked
for help with re-writing the specific script into sapply() or
foreach()/doParallel format.
Thanks anyway for your time and suggestions,
Lexo
On Thu, Jan 24, 2019 at 12:38 AM Eric Berger wrote:
> Charles writes about savi
Dear Bill,
Appreciate all your effort. I hope some one here can respond to this query.
Many thanks,
Ashim
On Thu, Jan 24, 2019 at 12:49 AM Bill Poling wrote:
>
> Ashim.
>
> I see where I was mistaken, using MARSSparamsCIs(fit) <--Somehow I got an
> s in between param & Cis.
>
> I now get ne
Hi Jeff;
I figured out the problem. I do apologize to you and members in the list to
bother you with this simple problem.
Regards,
Greg
On Wed, Jan 23, 2019 at 6:46 PM Jeff Newmiller
wrote:
> Problem is in your data not matching your values, but you did not share
> your data. Try using the uni
Problem is in your data not matching your values, but you did not share your
data. Try using the unique() function to see what values you have in your data.
I will say that when I want to assign discrete colors I always start by
converting my character column in the data frame to a factor and sp
Hi Dear all;
I am getting the "sufficient values in manual scale. 10 needed but only 7
provided." problem when running the followings. Your help is highly
appreciated.
Regards,
Greg
p2<-p1+scale_color_manual(name="Diseases",
labels=c("Myocardial Infarction", "Coronary artery disease", "Stroke",
Something like
files <- list.files(pattern="*.xls", full.names = TRUE)
data <- lapply(files, read_excel, sheet="Flow Data", range=("b9:c10"))
should do it.
--Ista
On Wed, Jan 23, 2019 at 12:42 PM Thomas Subia via R-help
wrote:
>
>
> Colleagues,
>
> I have a workbook which has 3 worksheets
>
>
Charles writes about saving execution time by eliminating redundancies.
If you see redundancies related to calling a time-consuming function
multiple times with the same arguments, a very easy way to speed up your
program is to memoise the functions using the package memoise.
HTH,
Eric
On Wed,
Ashim.
I see where I was mistaken, using MARSSparamsCIs(fit) <--Somehow I got an s in
between param & Cis.
I now get new error similarly as you, my apologies.
final <- MARSSparamCIs(fit)
Error in dpari[time.varying] <- dparmat(MLEobj, time.varying, t = t) :
replacement has length zero
WHP
sessionInfo()
#R version 3.5.2 (2018-12-20)
#Platform: x86_64-w64-mingw32/x64 (64-bit)
#Running under: Windows >= 8 x64 (build 9200)
Hello Ashim. I am not familiar with the MARSS pkg, however, I am always
interested in following many of these R-Help questions and often run them for
my own edific
On 23/01/2019 12:27 p.m., AbouEl-Makarim Aboueissa wrote:
here is the messages I got when I install the "car" package:
You didn't install it, you got errors during the install.
I'm not sure why there was no attempt to install Rcpp (which was
required by rio, see the error message). Perhaps t
See inline.
> On Jan 23, 2019, at 2:17 AM, Aleksandre Gavashelishvili
> wrote:
>
> I'm trying to speed up a script that otherwise takes days to handle larger
> data sets. So, is there a way to completely vectorize or paralellize the
> following script:
>
>*# k-fold cross valida
I'd recommend you upgrade to R version 3.5.2, the version you have is quite
out of date.
On Wed, Jan 23, 2019 at 9:42 AM AbouEl-Makarim Aboueissa <
abouelmakarim1...@gmail.com> wrote:
> here is the messages I got when I install the "car" package:
>
> > install.packages("car")
> Installing package
Colleagues,
I have a workbook which has 3 worksheets
I need to extract data from two specific cells from one ofthose worksheets.
I can use read_excel to do this for one file.
data<-read_excel("C:/Desktop/Excel_raw_data/0020-49785 8768.xls",
sheet="Flow Data",range=("b
here is the messages I got when I install the "car" package:
> install.packages("car")
Installing package into ‘C:/Users/aaboueissa/Documents/R/win-library/3.3’
(as ‘lib’ is unspecified)
also installing the dependency ‘rio’
There are binary versions available but the source versions are later:
Hi Duncan,
On Wed, Jan 23, 2019 at 10:02:00AM -0500, Duncan Murdoch wrote:
> On 23/01/2019 5:27 a.m., Jan T Kim wrote:
> >Hi Ivan & All,
> >
> >R's scoping system basically goes to all environments along the call
> >stack when trying to resolve an unbound variable, see the language
> >definition [
On 23/01/2019 12:13 p.m., AbouEl-Makarim Aboueissa wrote:
Dear All:
After installing the packages "car" and "alr3", I got the following error
messages:
library(car)
Error in library(car) : there is no package called ‘car’
library(alr3)
Error in library(alr3) : there is no package called ‘
Dear All:
After installing the packages "car" and "alr3", I got the following error
messages:
> library(car)
Error in library(car) : there is no package called ‘car’
> library(alr3)
Error in library(alr3) : there is no package called ‘alr3’
any helps would be appreciated.
with many thanks
ab
I'm trying to speed up a script that otherwise takes days to handle larger
data sets. So, is there a way to completely vectorize or paralellize the
following script:
*# k-fold cross validation*
df <- trees # a data frame 'trees' from R.
df <- df[sample(nrow(df)), ] # randomly shuf
Also quantile() and cut(). The only tricky part is making sure the minimum and
maximum values are included.
> set.seed(42)
> x <- rnorm(100, 25, 3)
> bks <- quantile(x, prob=c(0, .2, .4, .6, .8, 1))
> y <- cut(x, breaks=bks, labels=1:5, include.lowest=TRUE)
> table(y)
y
1 2 3 4 5
20 20 20 2
On 23/01/2019 5:27 a.m., Jan T Kim wrote:
Hi Ivan & All,
R's scoping system basically goes to all environments along the call
stack when trying to resolve an unbound variable, see the language
definition [1], section 4.3.4, and perhaps also 2.1.5.
You are misinterpreting that section. It's no
On 23/01/2019 4:53 a.m., Ivan Krylov wrote:
Hi!
I needed to generalize a loss function being optimized inside another
function, so I made it a function argument with a default value. It
worked without problems, but later I noticed that the inner function,
despite being defined in the function ar
Hi akshay Kulkarni, I just worked through this great tutorial the other day,
hope this helps!
WHP
https://www.r-bloggers.com/how-to-combine-multiple-ggplot-plots-to-make-publication-ready-plots/
From: R-help On Behalf Of Eric Berger
Sent: Tuesday, January 22, 2019 8:59 AM
To: PIKAL Petr
Cc:
Hi Ivan & All,
R's scoping system basically goes to all environments along the call
stack when trying to resolve an unbound variable, see the language
definition [1], section 4.3.4, and perhaps also 2.1.5.
Generally, unbound variables should be used with care. It's a bit
difficult to decide wheth
Hi!
I needed to generalize a loss function being optimized inside another
function, so I made it a function argument with a default value. It
worked without problems, but later I noticed that the inner function,
despite being defined in the function arguments, somehow closes over a
variable belong
Hi
Yes, you should get multipage pdf, each page populated by single call to plot
function.
However I am not sur if your proposal with function will work.
I usually do simply
pdf("sample.pdf", 7, 5)
for (i in 1:n) {
hist(L[[i]])
}
dev.off()
Cheers
Petr
From: akshay kulkarni
Sent: Wedne
cut can do the job
q_prob <- seq(0, 1, 0.2)
cut(x, breaks = quantile(x, probs = q_prob), include.lowest = T , labels =
1:5)
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