| colSums {Matrix} | R Documentation |
Form row and column sums and means for Matrix objects.
colSums (x, na.rm = FALSE, dims = 1, ...)
rowSums (x, na.rm = FALSE, dims = 1, ...)
colMeans(x, na.rm = FALSE, dims = 1, ...)
rowMeans(x, na.rm = FALSE, dims = 1, ...)
## S4 method for signature 'sparseMatrix':
colSums(x, na.rm = FALSE,
dims = 1, sparseResult = FALSE, ...)
## S4 method for signature 'sparseMatrix':
rowSums(x, na.rm = FALSE,
dims = 1, sparseResult = FALSE, ...)
## S4 method for signature 'sparseMatrix':
colMeans(x, na.rm = FALSE,
dims = 1, sparseResult = FALSE, ...)
## S4 method for signature 'sparseMatrix':
rowMeans(x, na.rm = FALSE,
dims = 1, sparseResult = FALSE, ...)
x |
a Matrix, i.e., inheriting from Matrix. |
na.rm |
logical. Should missing values (including NaN)
be omitted from the calculations? |
dims |
completely ignored by the Matrix methods. |
... |
potentially further arguments, for method <->
generic compatibility. |
sparseResult |
logical indicating if the result should be sparse,
i.e., inheriting from class sparseVector. |
returns a numeric vector if sparseResult is FALSE as per
default. Otherwise, returns a sparseVector.
colSums and the
sparseVector classes.
(M <- bdiag(Diagonal(2), matrix(1:3, 3,4), diag(3:2)))
colSums(M)
d <- Diagonal(10, c(0,0,10,0,2,rep(0,5)))
MM <- kronecker(d, M)
dim(MM) # 40 140
cm <- colSums(MM)
(scm <- colSums(MM, sparseResult = TRUE))
stopifnot(is(scm, "sparseVector"),
identical(cm, as.numeric(scm)))
rowSums(MM, sparseResult = TRUE) # 8 of 40 are not zero
if(FALSE) ## FIXME! -- requires <sparseVec> / <scalar>
rowMeans(MM, sparseResult = TRUE)