R: Form Row and Column Sums and Means

colSums {Matrix}

R Documentation

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 signature 'CsparseMatrix'
colSums(x, na.rm = FALSE,
        dims = 1, sparseResult = FALSE)
## S4 method for signature 'CsparseMatrix'
rowSums(x, na.rm = FALSE,
        dims = 1, sparseResult = FALSE)
## S4 method for signature 'CsparseMatrix'
colMeans(x, na.rm = FALSE,
        dims = 1, sparseResult = FALSE)
## S4 method for signature '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.

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) # 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))))