rowFreqs: Rowwise Frequencies
In scrime: Analysis of High-Dimensional Categorical Data Such as SNP Data

Description Usage Arguments Value Author(s) See Also Examples

Computes the frequencies of the levels that the categorical variables in a matrix show.

1 2	rowFreqs(x, levels = 1:3, divide.by.n = FALSE, affy = FALSE, includeNA = FALSE, useNN = c("not", "only", "also"), check = TRUE)

`x`	a matrix in which each row represents a categorical variable (e.g., a SNP) and each column an observation, where the variables are assumed to show the levels specified by `levels`. Missing values are allowed in `x`.
`levels`	vector specifying the levels that the categorical variables in `x` show. Ignored if `affy = TRUE`.
`divide.by.n`	should the numbers of observations showing the respective levels be divided by the total number of observations, i.e.\ by `ncol(x)`? If `FALSE`, these numbers are divided by the number of non-missing values of the respective variable. Ignored if `includeNA = TRUE`.
`affy`	logical specifying whether the SNPs in `x` are coded in the Affymetrix standard way. If `TRUE`, `levels = c("AA", "AB", "BB")` and `useNN = "also"` will be used (the latter only when `includeNA = TRUE`).
`includeNA`	should a column be added to the output matrix containing the number of missing values for each variable?
`useNN`	character specifying whether missing values can also be coded by `"NN"`. If `useNN = "not"` (default), missing values are assumed to be coded only by `NA`. If `"only"`, then missing values are assumed to be coded only by `"NN"` (and not by `NA`. If `"both"`, both `"NN"` and `NA` are considered. Ignored if `affy = TRUE`.
`check`	should it be checked whether some of the variables show other levels than the one specified by `levels`?

A matrix with the same number of rows as x containing for each variable the numbers of observations showing the levels specified by levels.

Holger Schwender, holger.schwender@udo.edu

rowTables

## Not run: 
# Generate a matrix containing data for 10 categorical 
# variables with levels 1, 2, 3.

mat <- matrix(sample(3, 500, TRUE), 10)

rowFreqs(mat)

# leads to the same results as 

rowTables(mat) / ncol(mat)

# If mat contains missing values

mat[sample(500, 20)] <- NA

# then

rowFreqs(mat)

# leads to the same result as

rowTables(mat) / rowSums(!is.na(mat))


## End(Not run)