Matrix (version 1.4-0)

colSums: Form Row and Column Sums and Means

Description

Form row and column sums and means for objects, for '>sparseMatrix the result may optionally be sparse ('>sparseVector), too. Row or column names are kept respectively as for base matrices and colSums methods, when the result is numeric vector.

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, …)
# S4 method for CsparseMatrix
colSums(x, na.rm = FALSE,
        dims = 1, sparseResult = FALSE)
# S4 method for CsparseMatrix
rowSums(x, na.rm = FALSE,
        dims = 1, sparseResult = FALSE)
# S4 method for CsparseMatrix
colMeans(x, na.rm = FALSE,
        dims = 1, sparseResult = FALSE)
# S4 method for 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. Only applicable when x is inheriting from a '>sparseMatrix class.

Value

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

dimnames(x) are only kept (as names(v)) when the resulting v is numeric, since sparseVectors do not have names.

See Also

colSums and the '>sparseVector classes.

Examples

# NOT RUN {
(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) # 14 of 70 are not zero
colMeans(MM, sparseResult = TRUE) # 16 of 80 are not zero
## 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)

## dimnames(x) -->  names( <value> ) :
dimnames(M) <- list(paste0("r", 1:7), paste0("V",1:8))
M
colSums(M)
rowMeans(M)
## Assertions :
stopifnot(all.equal(colSums(M),
		    setNames(c(1,1,6,6,6,6,3,2), colnames(M))),
	  all.equal(rowMeans(M), structure(c(1,1,4,8,12,3,2) / 8,
					   .Names = paste0("r", 1:7))))
# }