. Juli 2015 um 18:22 Uhr
Von: "jim holtman"
An: "Martin Spindler"
Cc: "r-help@r-project.org"
Betreff: Re: [R] R parallel / foreach - aggregation of results
Try this chance to actually return values:
library(doParallel)
Simpar3 <- function(n1) {
L2distan
Dear all,
when I am running the code attached below, it seems that no results are
returned, only the predefined NAs. What mistake do I make?
Any comments and help is highly appreciated.
Thanks and best,
Martin
Simpar3 <- function(n1) {
L2distance <- matrix(NA, ncol=n1, nrow=n1)
data <- rn
t,
Martin
Gesendet: Donnerstag, 30. Juli 2015 um 15:28 Uhr
Von: "jim holtman"
An: "Jeff Newmiller"
Cc: "Martin Spindler" , "r-help@r-project.org"
Betreff: Re: [R] R parallel - slow speed
I ran a test on my Windows box with 4 CPUs. THere were 4 RScript processes
Uhr
Von: "Jeff Newmiller"
An: "Martin Spindler" , "r-help@r-project.org"
Betreff: Re: [R] R parallel - slow speed
Parallelizing comes at a price... and there is no guarantee that you can afford
it. Vectorizing your algorithms is often a better approach. Microbenchma
Dear all,
I am trying to parallelize the function npnewpar given below. When I am
comparing an application of "apply" with "parApply" the parallelized version
seems to be much slower (cf output below). Therefore I would like to ask how
the function could be parallelized more efficient. (With in
Thank you! I think I now understand where the problem was.
Best,
Martin
Gesendet: Mittwoch, 29. April 2015 um 16:50 Uhr
Von: "David L Carlson"
An: "Martin Spindler" , "r-help@r-project.org"
Betreff: RE: [R] Problem with predict.lm()
Since you passed a matrix
on: "ARNAB KR MAITY"
An: "Martin Spindler" , "r-help@r-project.org"
Betreff: Re: [R] Problem with predict.lm()
Hi,
It seems to be working in my R. Although it is throwing the warning message
Warning message:
'newdata' had 200 rows but vari
Dear all,
the following example somehow uses the "old data" (X) to make the predictions,
but not the new data Xnew as intended.
y <- rnorm(100)
X <- matrix(rnorm(100*10), ncol=10)
lm <- lm(y~X)
Xnew <- matrix(rnorm(100*20), ncol=10)
ynew <- predict(lm, newdata=as.data.frame(Xnew)) #prediction
Dear all,
I would like to us openBLAS in R under Linux / Unix.
Which steps do I have to undertake? Does someone know a detailed
description? (I found some sources on the web, but none was really
helpful for me.)
Thanks and best,
Martin
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Dear all,
I am working with glmnet but the problem arises also in all other Lasso
implementations:
It is ususally recommended to standardize the variables / use intercept and
this works well with the implemented options:
x <- matrix(rnorm(1), ncol=50)
y <- rnorm(200)
Dear all,
I would like to ask, if there is a way to make the variance / dispersion
parameter $\theta$ (referring to MASS, 4th edition, p. 206) in the function
glm.nb dependent on the data, e.g. $1/ \theta = exp(x \beta)$ and to estimate
the parameter vector $\beta$ additionally.
If this is not
Dear R users,
I have a dataframe which consists of variables of type numeric and factor.
What is the easiest way to split up the dataframe to two dataframe which
contain all variables of the type numeric resp. factors?
Thank you very much for your efforts in advance!
Best,
Martin
Dear all,
I am looking for a function in R which returns all possible permutations of an
object x with r number of repitions. For example
If x <- c(0,1) and r <-3 the result should be
0 0 0
0 0 1
0 1 0
0 1 1
1 0 0
1 0 1
1 1 0
1 1 1
and consist of 2^3=8 elements.
Unfortunately, I have found on
Dear all,
when applying the optim function the following error occured
"non-finite finite-difference value"
Therefore I would like to ask how one can try to handle such a problem and
which strategies have proven useful. (There is only litte guidance on the help
list for this kind of problem.)
Dear all,
I would like to convert the first column of a dataframe to a date (original
format: year (4 digits) and month (last 2 digits))
>str(dat_FF)
'data.frame': 1022 obs. of 4 variables:
$ date : int 192607 192608 192609 192610 192611 192612 192701 192702
192703 192704 ...
$ Rm.Rf: num
Dear all,
I am estimating a bivariate probit model using the package VGAM:
fit1 = vglm(cbind(y1, y2) ~ x1+x2, binom2.rho, data=dat, trace=TRUE)
I would like to estimate this via the method vglm.fit (in an analogous way
as lm.fit for lm or glm.fit for glm). Unfortunately my trials did no
Dear all,
I have a matrix X which consists of 2 columns. I would like to convert this
matrix into a list where every entry of the list consists of a single row of
the matrix.
Does anyone have a suggestions how to manage this?
Thank you for your efforts in advance!
Best,
Martin
Dear all,
I have the following problem:
add <- function(x,y) {x+y}
What is the easiest / most elegant way to create a new function (e.g. with the
name "addev") that sets the second argument of the function "add" to a fixed
value (e.g. y=3), i.e. addev <- add(x,3). But this does not work.
Than
Hey Michael,
Thank you very much. It works!
Best,
Martin
Original-Nachricht
> Datum: Fri, 10 Dec 2010 22:35:56 +1100
> Von: Michael Bedward
> An: Martin Spindler
> CC: r-help@r-project.org
> Betreff: Re: [R] subset with two factors
> Hello Martin,
&g
Dear all,
I have a dataframe of the following strucutre
numacc_b coverage_b Geschlecht GG
10 1 W A
20 1 M A
30 1 M B
40 1 M B
50 1 W A
60 1
Hello everyone,
using the VGAM package and the following code
library(VGAM)
bp1 <- vglm(cbind(daten$anzahl_b, daten$deckung_b) ~ ., binom2.rho,
data=daten1)
summary(bp1)
coef(bp1, matrix=TRUE)
produced this error message:
error in object$coefficients : $ operator not defined for
Hello,
currently I am estimating an ordered probit model with the function polr
(MASS package).
Is there a simple way to obtain values for the prediction of the index
function ($X*\hat{\beta}$)?
(E..g. in the GLM function there is the linear.prediction value for this
purpose).
If not, i
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