search for: hasna

Hi, is there a hasNA() / an anyNA() function in R? Of course, hasNA <- function(x) { any(is.na(x)); } would do, but that would scan all elements in 'x' and then do the test. I'm looking for a more efficient implementation that returns TRUE at the first NA, e.g. hasNA <- function(x) { for (kk...

R-helpers! na.omit() can be used to remove rows with NA's but how can I remove columns ? and remember, which columns have been removed ? I guess I can do t(na.omit(t(o))) as shown below, but this probably creates a lot of overhead and I do not know which columns have been removed. Yours, R > o [,1] [,2] [,3] [1,] 1 NA 7 [2,] 2 NA 8 [3,] 3 NA 9 >

...i, icc could vectorize the "nobreak" version, but the performance of it was the same as "break". For my experiments I extracted NaN checks into a function. This was the "break" version (same performance as the current code): static __attribute__ ((noinline)) Rboolean hasNA(double *x, int n) { for (R_xlen_t i = 0; i < n; i++) if (ISNAN(x[i])) return TRUE; return FALSE; } And this was the "nobreak" version: static __attribute__ ((noinline)) Rboolean hasNA(double *x, int n) { Rboolean has = FALSE; for (R_xlen_t i = 0; i < n; i++)...

> Do you have R code (including set.seed(.) if relevant) to show on how to generate > the large square matrices you've mentioned in the beginning? So we get to some > reproducible benchmarks? Hi Martin, Here is the program I used. I only generate 2 random numbers and reuse them to make the benchmark run faster. Let me know if there is something I can do to help--alternate

...icc could vectorize the "nobreak" version, but the performance of it was the same as "break". For my experiments I extracted NaN checks into a function. This was the "break" version (same performance as the current code): static __attribute__ ((noinline)) Rboolean hasNA(double *x, int n) { for (R_xlen_t i = 0; i < n; i++) if (ISNAN(x[i])) return TRUE; return FALSE; } And this was the "nobreak" version: static __attribute__ ((noinline)) Rboolean hasNA(double *x, int n) { Rboolean has = FALSE; for (R_xlen_t i = 0; i < n; i++)...

..., Vec2)), length(match(c("a9","a99","a999","a9999"), iVec2)), 10, 1000) nom denom factor user.self 0.125 0.00022 568.1818 sys.self 0.002 0.00000 Inf elapsed 0.126 0.00022 572.7273 > > # and not too bad with respect to the recent hasNA() / anyNA() thread (once the index has been built) > timefactor(any(is.na(Vec1)), any(is.na(iVec1)), 50, 50) nom denom factor user.self 0.0040 0.003 1.333333 sys.self 0.0004 0.000 Inf elapsed 0.0044 0.003 1.466667 > timefactor(any(is.na(Vec2)), any(is.na(iVec2)), 50, 50...

Hi, I'm just following your messages the overhead that the code for dealing with possible NA/NaN values brings. When I was setting up part of the matrixStats package, I've also though about this. I was thinking of having an additional logical argument 'hasNA'/'has.na' where you as a user can specify whether there is NA/NaN:s or not, allowing the code to avoid it or not. Of course, if hasNA=FALSE but there really are NAs you'll get very funny results. Even better would be if there would be an internal state attached to the basic data t...