similar to: Coercing elements of a matrix from integer to double

Dear R-Help list: I'm using the Matrix library to operate on 600 X ~5000 element unsymmetrical sparse arrays. So far, so good, but if I find I need more speed or functionality, how hard would it be to utilize other sparse matrix toolsets from within R, say MUMPS, PARDISO or UMFPACK, that do not have explicit R interfaces? More information on these is available here

This shouldn't be hard, but it's just not coming to me: Given a vector, e.g., v <- c(20, 134, 45, 20, 24, 500, 20, 20, 20) how can I get the index of the last value in the vector having a value greater than n, in this case, greater than 20? I'm looking for an efficient function I can use on very large matrices, as the FUN argument in the apply() command. Confidentiality

Hello, The code below is supposed to be a wrapper for matplot to do columnwise visible comparison of several matrices, but I'm doing something wrong because I can't access an argument called 'colnum'. I'd be most grateful for some insight. Thanks, John Thaden Little Rock, AR ################################ # mmatplot is a matplot wrapper to compare the same column of #

## While in R v. 2.3.1 (the mid-July patch for Windows) ## on a Windows XP machine, this call to order() works fine... order(1:10,decreasing = TRUE) ## [1] 10 9 8 7 6 5 4 3 2 1 ## ...however, the argument name 'decreasing' ## must be typed in toto (note the missing 'g' ## in the following): > order(1:10,decreasin = TRUE) ## Error in order(na.last, decreasing, ...) :

In the Matrix package v. 0.995-11 I see that the dgTMatrix Class for compressed, sparse, triplet-form matrices handles Identically indexed data instances by summing their values, e.g., library(Matrix) (Mt <- new("dgTMatrix", i = as.integer(c(0,0,1,1,4)), j = as.integer(c(0,1,2,2,4)), x = as.double(1:5), Dim = as.integer(c(5,5)))) ## 5 x 5 sparse Matrix of class

In the Matrix package v. 0.995-11 I see that the dgTMatrix Class for compressed, sparse, triplet-form matrices handles Identically indexed data instances by summing their values, e.g., library(Matrix) (Mt <- new("dgTMatrix", i = as.integer(c(0,0,1,1,4)), j = as.integer(c(0,1,2,2,4)), x = as.double(1:5), Dim = as.integer(c(5,5)))) ## 5 x 5 sparse Matrix of class

I am pretty pleased with baselines I fit to chromatograms using the runquantile() function in caTools(v1.6) when its probs parameter is set to 0.2 and its k parameter to ~1/20th of n (e.g., k ~ 225 for n ~ 4500, where n is time series length). This ignores occasional low- side outliers, and, after baseline subtraction, I can re-adjust any negative values to zero. But runquantile's

Does R, or will R, be integrating the changes to the Chambers/Lucent S language under their version 5.0? If not already, then when? John Thaden Little Rock, Arkansas, USA -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe"

Peter Dalgaard BSA <p.dalgaard at biostat.ku.dk> wrote: > Martin Maechler <maechler at stat.math.ethz.ch> writes: [ snip... ] > > >From this logic, coercing to a matrix should coerce to character rather > > than integer/numeric codes. > > Hm. I'm not sure there really is much logic in this... Factors are > sometimes character-like, sometimes

I use the step() function occasionally, and I think I understand its objective, proper use, and limitations. Now I see stepwise model selection being used in what seems to be an unusual way, and I wonder if it is right or wrong. May I describe? Genetic mapping tries to find where in an animal's genome are genetic elements that influence a particular physical trait. Say there are 100

R pattern-matching and replacement functions are vectorized: they can operate on vectors of targets. However, they can only use one pattern and replacement. Here is code to apply a different pattern and replacement for every target. My question: can it be done better? sub2 <- function(pattern, replacement, x) { len <- length(x) if (length(pattern) == 1) pattern <-

Hello all, How do I actually use the output of predict.lm(..., type="terms") to predict new term values from new response values? I'm a chromatographer trying to use R (2.15.1) for one of the most common calculations in that business: - Given several chromatographic peak areas measured for control samples containing a molecule at known (increasing) concentrations, first

Hello, Could someone please explain the following warning while subsetting in the Matrix package? Thanks, John Thaden, PhD U. Arkansas for Med. Sci. Little Rock AR USA > # In the Matrix package... > library("Matrix") > # ...I had previously created a sparse matrix in triplet form: > str(x) Formal class 'dgTMatrix' [package "Matrix"] with 6 slots ..@ i

Something has changed in the S4 default for coercing. Am I now suppose to use setAs, or is there something else I should do to make this work: R version 2.7.0 (2008-04-22) .... > require("methods") > setClassUnion("OptionalPOSIXct", c("POSIXct", "NULL")) [1] "OptionalPOSIXct" > setClass("TSmetax",

Dear all, Coercing a logical vector to a numeric one is easy. The as.numeric function is used. However what do we use when we have a matrix or an array? Sumona

Hi, I have a sparse Matrix (kronecker product of spline design matrices), and I need to multiply each row by a number to get another matrix. If the matrix is A and the numbers are stored in a vector k, with plain vanilla matrices I would do A*k But when using the Matrix package (class of A is "dgCMatrix"), I get the warning "coercing sparse to dense matrix for arithmetic".

Dear R listers, I am trying to compute the mean of a dummy variable that is encoded as a factor. However, even though the levels of my factor are 0 - 1, when I compute the mean (after coercing the factor to be numeric), R changes 0 into 1 and 1 into yes, thus altering my expected result. Please, consider the following working example: pp <- rep(0:1, 10) pp <- factor(pp, levels=(0:1),

I'm trying to produce a series of powers of a number as follows: |> 0.05^0:5 [1] 1 2 3 4 5 This is not the result I expected. I guess some kind of coercion happened, since, |> class(0.05^0:5) [1] "integer" Could anyone explain me what is happening here? Thanks, -Sergio.

I am using acf() to get the autocorrelations of a time series. It works but I want to then get the autocorrelations into a simple list of numbers. > x <- acf(price_changes, lag.max = 12,type = "correlation",plot = FALSE) > x Autocorrelations of series ‘price_changes’, by lag 0 1 2 3 4 5 6 7 8 9 10 11 12 1.000

all of a sudden, after a SparseM upgrade(?) I get this error: > str(z) Formal class 'matrix.csr' [package "SparseM"] with 4 slots ..@ ra : num [1:85372672] -0.4288 0.0397 0.0104 -0.1843 -0.1203 ... ..@ ja : int [1:85372672] 1 2 3 4 5 6 7 8 9 10 ... ..@ ia : int [1:699777] 1 123 245 367 489 611 733 855 977 1099 ... ..@ dimension: int [1:2] 699776 122