Form Row and Column Sums and Means

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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

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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.

See Also

colSums and the sparseVector classes.

Examples

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(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))))

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