similar to: functions for subsets: a minor R programming challenge

Hello, I have a question regarding how to speed up the t.test on large dataset. For example, I have a table "tab" which looks like: a b c d e f g h.... 1 2 3 4 5 ... 100000 dim(tab) is 100000 x 100 I need to do the t.test for each row on the two subsets of columns, ie to compare a b d group against e f g group at each row. subset 1: a b d 1 2 3 4 5 ... 100000

Hi, I am trying to speed up part of an algorithm in which certain columns of a large matrix (X) are replaced by the element-wise product of a matrix (M) and a vector (v). In R, the code might be X[, ind] <- M * v I have written a small C routine to do this for me, but the timing depends on how I define the storage.mode of the objects in R and the data types in C, in a way which I don't

hello everyone. Look at the following R idiom: a <- array(1:30,c(3,5,2)) M <- (matrix(1:15,c(3,5)) %% 4) < 2 a[M,] <- 0 Now, I think that "a[M,]" has an unambiguous meaning (to a human). However, the last line doesn't work as desired, but I expected it to...and it recently took me an indecent amount of time to debug an analogous case. Just to be explicit, I would

Dear All, I'm getting a error while trying to apply the BreastCancer dataset (package=mlbench) to kknn (package=kknn) that I don't understand as I'm new to R. The codes are as follow: rm = (list = ls()) library(mlbench) data(BreastCancer) library(kknn) BCancer = na.omit(BreastCancer) d = dim(BCancer)[1] i1 = seq(1, d, 2) i2 = seq(2, d, 2) t1 = BCancer[i1, ] t2 =

Hi friends, Please can anyone help me with an easier solution of doing the below mentioned work. Suppose i have a dataset like this:--- i1 i2 i3 i4 i5 1 7 13 1 2 2 8 14 2 2 3 9 15 3 3 4 10 16 4 4 5 11 17 5 5 6 12 18 6 7 *i1,i2,i3,i4,i5 are my items.I am able to find all possible pairs i.e Say this dataframe is "item_pairs" **i1,i2 **i1,i3 **i1,i4 i1,i5 **i2,i1

Hi Duncan & Tobi, Thanks a lot for your interest, and for pointing out differences in GIMPLE I missed. Attached is simplified test case. Is it good? Tobi, regarding runtime alias analysis: in KernelGen we already do it along with runtime values substitution. For example: <------------------ __kernelgen_main_loop_17: compile started ---------------------> Integer args substituted:

Hello, I am not only interested in finding out which genes are the most highly up- or down-regulated (which I have done using the linear models and Bayesian statistics in Limma), but I also want to know which genes are consistently highly transcribed (ie. they have a high intensity in the channel of interest eg. Cy5 or Cy3 across the set of experiments). I might have missed a straight forward

I'm using functions to return a matrix of all permutations of a specified size from a larger list for predictor selection. For each predictor size I use a seperate function like this: bag2 <- function(n) { rl <- c() for (i1 in seq(n)) { for (i2 in seq(n)) { if (length(unique(c(i1,i2)))==1) {next} rl <- cbind(rl,matrix(c(i1,i2))) } }

On 9/25/2017 9:14 AM, Björn Pettersson A wrote: > > (Not sure if this exactly maps to “truncating store”, but I think it > at least touches some of the subjects discussed in this thread) > > Our out-of-tree-target need several patches to get things working > correctly for us. > > We have introduced i24 and i40 types in ValueTypes/MachineValueTypes > (in addition to

(Not sure if this exactly maps to “truncating store”, but I think it at least touches some of the subjects discussed in this thread) Our out-of-tree-target need several patches to get things working correctly for us. We have introduced i24 and i40 types in ValueTypes/MachineValueTypes (in addition to the normal pow-of-2 types). And we have vectors of those (v2i40, v4i40). And the byte size in our

I am trying to refer to a variable name in a lm regression where the variable name is in another variable, but it does seem to work. Here is an example: y<-rnorm(10) dat<-data.frame(x1=rnorm(10),x2=rnorm(10),x3=rnorm(10)) nam<-c('x1','x2','x3') library(gtools) com<-combinations(3,2,1:3) mod<-lm(y~nam[com[1,1]],data=dat) #error in model frame....:variable

False Alarm!! Still don't know how to do it! I am trying to write a transformation pass to extract a loop body into a function. For example: The Loop in question is: for (i2 = 0; i2 < LOOP_SIZE; i2++) { A[B[i2]] = 2 * B[i2]; } The IR for which is: bb13: ; preds = %bb13, %bb %i2.0.reg2mem.0 = phi i32 [ 0, %bb ], [ %indvar.next62, %bb13 ] ; <i32>

* * Hi , Can anyone help me please with this problem?* * *CASE-I* all_raw_data_NAomitted is my data frame.It has columns with names i1 ,i2, i3,i4…, till i15.It has 291 rows actually ,couldn’t show here. The data frame looks like this:-- i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12 i13 i14 i15 2 2 2 2 2 2 2 2 2 2 2 1 2 2 3 2 3 2 2 2 2 3 2 2 3 3

I am trying to multiply a 3D array of 4x4x4 by the 4 3D arrays of a 4D array with dimensions 4x4x4x4 (the last dimension being the one that I want to split by). (4x4x4 array) > hiaAry , , a1 i1 i2 i3 i4 h1 9.5936098 6.001040 0.08772 0.3138600 h2 1.2003500 1.454570 2.79248 0.0000000 h3 0.1346500 0.201220 0.39256 0.5464000 h4 0.0109000 0.012270 0.16417 0.2766900 ,

Crossed random effects: > lmer( y ~ (1 | i1) + (1|i2) ,data=dta) Linear mixed-effects model fit by REML Formula: y ~ (1 | i1) + (1 | i2) Data: dta AIC BIC logLik MLdeviance REMLdeviance 91.18 94.84 -42.59 85.2 85.18 Random effects: Groups Name Variance Std.Dev. i1 (Intercept) 0.068224 0.26120 i2 (Intercept) 0.463112

Hi Everyone, quick question before the end of the year. I have soem indices to select data from a bigger sample. I want to select n days before each index and n days after the index. Any clever way to do it. A for loop would do but I wanted to know if there is a moreR-friendly way to approach this Example # InitialIndices i2 = (90, 190, 290) # Indices I want to end up with i3 = c(85, 86, 87,

Dear list, I have a list of matrices : i1 <- matrix(1:10, nrow = 2, ncol = 5) i2 <- matrix(11:20, nrow = 2, ncol = 5) j <- list(i1 = i1, i2 = i2) I would like to attribute names to each dimension, for each matrix, as follows : $i1 B1 B2 B3 B4 B5 A1 1 3 5 7 9 A2 2 4 6 8 10 $i2 B1 B2 B3 B4 B5 A1 11 13 15 17 19 A2 12 14 16 18 20 However, I have to use lapply function and attribute

Dear R-users, I have output files having a variable number of tables in the following format: ------------- 1 Pietje I1 I2 Value 1 1 0.11 1 2 0.12 2 1 0.21 2 Jantje I1 I2 I3 Value 1 1 1 0.111 3 3 3 0.333 ... ------------- Would there be an easy way of turning this into (a list of) data.frames with names Pietje, Jantje and variables I1,I2,...Value? (I1,I2 are string or categorical,

Hi Tobias, could we use information about memory accesses of a SCoP statement and def-use chains to determine statements, which don’t contain matrix multiplication of the following form? for (int i = 0; i < Upper Bound1; i++) for (int j = 0; j < Upper Bound2; j++) for (int k = 0; k < Upper Bound3; j++) C[i][j] += A[i][k] * B[k][j] We could probably check that memory access

Hi, I'm seeing something that may be a bug in R's standalone math library, which is packaged by Debian as r-mathlib. I reported it to the Debian BTS as http://bugs.debian.org/657573 I'm using Debian squeeze, and the code was tested with r-mathlib 2.11.1-6 (default on stable) and 2.14.1-1 (from testing/unstable). I summarize this report below. The following code with the R math