JWileymisc (version 1.4.1)

corOK: Return a non-missing correlation matrix

Description

Given a square, symmetric matrix (such as a correlation matrix) this function tries to drop the fewest possible number of variables to return a (square, symmetric) matrix with no missing cells.

Usage

corOK(x, maxiter = 100)

Value

A list with two elements

x

The complete non missing matrix.

keep.indices

A vector of the columns and rows from the original matrix to be kept (i.e., that are nonmissing).

Arguments

x

a square, symmetric matrix or object coercable to such (such as a data frame).

maxiter

a number indicating the maximum number of iterations, currently as a sanity check. See details.

Details

The assumption that x is square and symmetric comes because it is assumed that the number of missing cells for a given column are identical to that of the corresponding row. corOK finds the column with the most missing values, and drops that (and its corresponding row), and continues on in like manner until the matrix has no missing values. Although this was intended for a correlation matrix, it could be used on other types of matrices. Note that because corOK uses an iterative method, it can be slow when many columns/rows need to be removed. For the intended use (correlation matrices) there probably should not be many missing. As a sanity check and to prevent tediously long computations, the maximum number of iterations can be set.

Examples

cormat <- cor(iris[, -5])
# set missing
cormat[cbind(c(1,2), c(2,1))] <- NA

# print
cormat

# return complete
corOK(cormat)

# using maximum iterations
corOK(cormat, maxiter=0)

# clean up
rm(cormat)