For each matrix row apply a function to each cell of a ragged array, that is to each (non-empty) group of values given by a unique combination of the levels of certain factors.
tgs_matrix_tapply(x, index, fun, ...)a matrix or a sparse matrix of 'dgCMatrix' type
a 'list' of one or more 'factor's, each of same length as the number of columns in 'x'. The elements are coerced to factors by 'as.factor'.
the function to be applied
optional arguments to 'fun'
A matrix of length(index) X nrow(x) size. Each [i,j] element
represents the result of applying 'fun' to
x[i,which(index==levels(index)[j])].
Note that the return value is a dense matrix even when x is sparse.
'tgs_matrix_tapply(x, index, fun)' is essentialy an efficient implementation of 'apply(mat, 1, function(x) tapply(x, index, fun))'.
# \donttest{
# Note: all the available CPU cores might be used
set.seed(seed = 1)
nr <- 6
nc <- 10
mat <- matrix(sample(c(rep(0, 6), 1:3), nr * nc, replace = TRUE), nrow = nr, ncol = nc)
index <- factor(rep_len(1:3, ncol(mat)), levels = 0:5)
r1 <- apply(mat, 1, function(x) tapply(x, index, sum))
r2 <- tgs_matrix_tapply(mat, index, sum)
# }
# \dontshow{
options(tgs_use.blas = FALSE)
options(tgs_max.processes = 1)
set.seed(seed = 1)
nr <- 6
nc <- 10
mat <- matrix(sample(c(rep(0, 6), 1:3), nr * nc, replace = TRUE), nrow = nr, ncol = nc)
index <- factor(rep_len(1:3, ncol(mat)), levels = 0:5)
r1 <- apply(mat, 1, function(x) tapply(x, index, sum))
r2 <- tgs_matrix_tapply(mat, index, sum)
# }