matrixStats (version 0.58.0)

rowVars: Variance estimates for each row (column) in a matrix

Description

Variance estimates for each row (column) in a matrix.

Usage

rowVars(x, rows = NULL, cols = NULL, na.rm = FALSE, center = NULL,
  dim. = dim(x), ...)
colVars(x, rows = NULL, cols = NULL, na.rm = FALSE, center = NULL,
  dim. = dim(x), ...)

Arguments

x

A numeric NxK matrix.

rows, cols

A vector indicating subset of rows (and/or columns) to operate over. If NULL, no subsetting is done.

na.rm

If TRUE, missing values are excluded first, otherwise not.

center

(optional; a vector or length N (K)) If the row (column) means are already estimated, they can be pre-specified using this argument. This avoid re-estimating them again. (*Warning: If biased estimated are given, the estimate of the spread will also be biased.*) If NULL (default), the row/column means are estimated internally.

dim.

An integer vector of length two specifying the dimension of x, also when not a matrix.

...

Additional arguments passed to rowMeans() and rowSums().

Value

Returns a numeric vector of length N (K).

See Also

See rowMeans() and rowSums() in colSums().

Examples

# NOT RUN {
set.seed(1)

x <- matrix(rnorm(20), nrow = 5, ncol = 4)
print(x)

# Row averages
print(rowMeans(x))
print(rowMedians(x))

# Column averages
print(colMeans(x))
print(colMedians(x))


# Row variabilities
print(rowVars(x))
print(rowSds(x))
print(rowMads(x))
print(rowIQRs(x))

# Column variabilities
print(rowVars(x))
print(colSds(x))
print(colMads(x))
print(colIQRs(x))

# Row ranges
print(rowRanges(x))
print(cbind(rowMins(x), rowMaxs(x)))
print(cbind(rowOrderStats(x, which = 1), rowOrderStats(x, which = ncol(x))))

# Column ranges
print(colRanges(x))
print(cbind(colMins(x), colMaxs(x)))
print(cbind(colOrderStats(x, which = 1), colOrderStats(x, which = nrow(x))))


x <- matrix(rnorm(2400), nrow = 50, ncol = 40)

# Row standard deviations
d <- rowDiffs(x)
s1 <- rowSds(d) / sqrt(2)
s2 <- rowSds(x)
print(summary(s1 - s2))

# Column standard deviations
d <- colDiffs(x)
s1 <- colSds(d) / sqrt(2)
s2 <- colSds(x)
print(summary(s1 - s2))
# }