matrixStats: rowAvgsPerColSet – R documentation

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rowAvgsPerColSet

Applies a row-by-row (column-by-column) averaging function to equally-sized subsets of matrix columns (rows)

Description

Applies a row-by-row (column-by-column) averaging function to equally-sized subsets of matrix columns (rows). Each subset is averaged independently of the others.

Usage

rowAvgsPerColSet(X, W = NULL, rows = NULL, S, FUN = rowMeans, ...,
  na.rm = NA, tFUN = FALSE)

colAvgsPerRowSet(X, W = NULL, cols = NULL, S, FUN = colMeans, ...,
  na.rm = NA, tFUN = FALSE)

Arguments

`X`	A `numeric` NxM `matrix`.
`W`	An optional `numeric` NxM `matrix` of weights.
`rows, cols`	A `vector` indicating subset of rows (and/or columns) to operate over. If `NULL`, no subsetting is done.
`S`	An `integer` KxJ `matrix` specifying the J subsets. Each column holds K column (row) indices for the corresponding subset.
`FUN`	The row-by-row (column-by-column) `function` used to average over each subset of `X`. This function must accept a `numeric` NxK (KxM) `matrix` and the `logical` argument `na.rm`, and return a `numeric` `vector` of length N (M).
`...`	Additional arguments passed to then `FUN` `function`.
`na.rm`	(logical) Argument passed to `FUN()` as `na.rm = na.rm`. If `NA` (default), then `na.rm = TRUE` is used if `X` or `S` holds missing values, otherwise `na.rm = FALSE`.
`tFUN`	If `TRUE`, the NxK (KxM) `matrix` passed to `FUN()` is transposed first.

Details

If argument S is a single column vector with indices 1:N, then rowAvgsPerColSet(X, S = S, FUN = rowMeans) gives the same result as rowMeans(X). Analogously, for colAvgsPerRowSet().

Value

Returns a numeric JxN (MxJ) matrix, where row names equal rownames(X) (colnames(S)) and column names colnames(S) (colnames(X)).

Author(s)

Henrik Bengtsson

Examples

X <- matrix(rnorm(20 * 6), nrow = 20, ncol = 6)
rownames(X) <- LETTERS[1:nrow(X)]
colnames(X) <- letters[1:ncol(X)]
print(X)


# - - - - - - - - - - - - - - - - - - - - - - - - - -
# Apply rowMeans() for 3 sets of 2 columns
# - - - - - - - - - - - - - - - - - - - - - - - - - -
nbr_of_sets <- 3
S <- matrix(1:ncol(X), ncol = nbr_of_sets)
colnames(S) <- sprintf("s%d", 1:nbr_of_sets)
print(S)

Z <- rowAvgsPerColSet(X, S = S)
print(Z)

# Validation
Z0 <- cbind(s1 = rowMeans(X[, 1:2]),
            s2 = rowMeans(X[, 3:4]),
            s3 = rowMeans(X[, 5:6]))
stopifnot(identical(drop(Z), Z0))


# - - - - - - - - - - - - - - - - - - - - - - - - - -
# Apply colMeans() for 5 sets of 4 rows
# - - - - - - - - - - - - - - - - - - - - - - - - - -
nbr_of_sets <- 5
S <- matrix(1:nrow(X), ncol = nbr_of_sets)
colnames(S) <- sprintf("s%d", 1:nbr_of_sets)
print(S)

Z <- colAvgsPerRowSet(X, S = S)
print(Z)

# Validation
Z0 <- rbind(s1 = colMeans(X[  1:4, ]),
            s2 = colMeans(X[  5:8, ]),
            s3 = colMeans(X[ 9:12, ]),
            s4 = colMeans(X[13:16, ]),
            s5 = colMeans(X[17:20, ]))
stopifnot(identical(drop(Z), Z0))


# - - - - - - - - - - - - - - - - - - - - - - - - - -
# When there is only one "complete" set
# - - - - - - - - - - - - - - - - - - - - - - - - - -
nbr_of_sets <- 1
S <- matrix(1:ncol(X), ncol = nbr_of_sets)
colnames(S) <- sprintf("s%d", 1:nbr_of_sets)
print(S)

Z <- rowAvgsPerColSet(X, S = S, FUN = rowMeans)
print(Z)

Z0 <- rowMeans(X)
stopifnot(identical(drop(Z), Z0))


nbr_of_sets <- 1
S <- matrix(1:nrow(X), ncol = nbr_of_sets)
colnames(S) <- sprintf("s%d", 1:nbr_of_sets)
print(S)

Z <- colAvgsPerRowSet(X, S = S, FUN = colMeans)
print(Z)

Z0 <- colMeans(X)
stopifnot(identical(drop(Z), Z0))

matrixStats

Functions that Apply to Rows and Columns of Matrices (and to Vectors)

v0.58.0

Artistic-2.0

Authors

Henrik Bengtsson [aut, cre, cph], Constantin Ahlmann-Eltze [ctb], Hector Corrada Bravo [ctb], Robert Gentleman [ctb], Jan Gleixner [ctb], Peter Hickey [ctb], Ola Hossjer [ctb], Harris Jaffee [ctb], Dongcan Jiang [ctb], Peter Langfelder [ctb], Brian Montgomery [ctb], Hugh Parsonage [ctb]

Initial release