Matrix (version 0.999375-42)

colSums: Form Row and Column Sums and Means

Description

Form row and column sums and means for Matrix objects.

Usage

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, ...)
## S3 method for class 'CsparseMatrix':
colSums(x, na.rm = FALSE,
        dims = 1, sparseResult = FALSE)
## S3 method for class 'CsparseMatrix':
rowSums(x, na.rm = FALSE,
        dims = 1, sparseResult = FALSE)
## S3 method for class 'CsparseMatrix':
colMeans(x, na.rm = FALSE,
        dims = 1, sparseResult = FALSE)
## S3 method for class 'CsparseMatrix':
rowMeans(x, na.rm = FALSE,
        dims = 1, sparseResult = FALSE)

Arguments

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.

Value

• returns a numeric vector if sparseResult is FALSE as per default. Otherwise, returns a sparseVector.

See Also

colSums and the sparseVector classes.

Examples

(M <- bdiag(Diagonal(2), matrix(1:3, 3,4), diag(3:2))) # 7 x 8
colSums(M)
d <- Diagonal(10, c(0,0,10,0,2,rep(0,5)))
MM <- kronecker(d, M)
dim(MM) # 70 80
length(MM@x) # 160, but many are '0' ; drop those:
MM <- drop0(MM)
length(MM@x) # 32
  cm <- colSums(MM)
(scm <- colSums(MM, sparseResult = TRUE))
stopifnot(is(scm, "sparseVector"),
          identical(cm, as.numeric(scm)))
rowSums(MM, sparseResult = TRUE) # 16 of 70 are not zero
colMeans(MM, sparseResult = TRUE)
## Since we have no 'NA's, these two are equivalent :
stopifnot(identical(rowMeans(MM, sparseResult = TRUE),
                    rowMeans(MM, sparseResult = TRUE, na.rm = TRUE)),
	  rowMeans(Diagonal(16)) == 1/16,
	  colSums(Diagonal(7)) == 1)