Dear Dirk, I dont see how to do that in Armadillo, but I think I can create same size NumericMatrix B = is.na(X); This maybe I could use for indexing ? To save time, and not to introduce extra looping.
Also, I want to perform regression column X on Z, and column Y on Z. Does arma::solve(...) handle nan values ? or I guess I have to check that myself ? The function works nicely, and while R implementation takes at least 40min (surely more because I terminated), Armadillo computes everything in less then one minute. But I skipped columns with missing cells, which now I want to include. Thanks, Mateusz On 9 October 2012 17:37, Dirk Eddelbuettel <e...@debian.org> wrote: > > On 9 October 2012 at 10:08, Douglas Bates wrote: > | You may find it easier to use the Rcpp class NumericMatrix than to use > | RcppArmadillo. Detection of NA's is built in to R and Rcpp classes but > not > | RcppArmadillo. For each pair of columns, run a loop that checks for NA's > at > | each position in each column, skips the position if NA's are detected and > | otherwise increments the squared sums, cross-product and number of > elements. > > All true, but at the same time, once in RcppArmadillo or RcppEigen ... it > is > convenient to stay there. > > You should be able to set up a little "sweeper" function which applies one > of > > R_IsNA just NA > R_IsNaN just NaN > R_IsFinite NA, NaN or Inf > > across a vector or matrix and returns you an index vector. Armadillo can > use > indexing vectors in ways that are similar in R. > > Dirk > > > | > | On Oct 9, 2012 9:53 AM, "mateusz.ka...@gmail.com" < > mateusz.ka...@gmail.com> > | wrote: > | > | Hi, > | > | I have written small code in C++ using Armadillo and inline with > | RcppArmadillo package. > | The input is data.marix(X). Some cells might be NAs. Example in R: X > = > | matrix(sample(c(rnorm(10*9.9),NA)),ncol=10) > | > | I am calculating conditional correlation on columns of that matrix, > just > | picking vectors, so cor(X,Y). > | The problem is that sometimes I might have empty cell in one or both > | vectors, in that case I would like to skip that row, and procede with > | calculating Pearson's correlation on remaining data. I know that > there will > | be difference in degrees of freedom, but I have over 100 rows, so > skiping > | few shouldnt matter that much. > | > | Basically my question boils down to solving the problem: > | How to find which colvec cells are nan, and remove this index from > both X > | and Y colvec, before calculating correlation. > | > | I would be very grateful for help, > | > | Kind regards, > | Mateusz Kaduk > | > | _______________________________________________ > | Rcpp-devel mailing list > | Rcpp-devel@lists.r-forge.r-project.org > | > https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/rcpp-devel > | > | > | ---------------------------------------------------------------------- > | _______________________________________________ > | Rcpp-devel mailing list > | Rcpp-devel@lists.r-forge.r-project.org > | https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/rcpp-devel > -- > Dirk Eddelbuettel | e...@debian.org | http://dirk.eddelbuettel.com >
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