statgenGxE (version 1.0.2)

multMissing: Estimate missing values in multivariate data

Description

This function estimates missing values for units in a multivariate dataset, using an iterative regression technique.

Usage

multMissing(Y, maxIter = 10, naStrings = NULL)

Arguments

Y

A matrix, data.frame or vector of multivariate data.

maxIter

An integer specifying the maximum number of iterations.

naStrings

A character vector of strings which are to be interpreted as NA values.

Value

An object of the same class as the input Y with the missing values replaced by their estimates.

Details

Initial estimates of the missing values in each variate are formed from the variate means using the values for units that have no missing values for any variate. Estimates of the missing values for each variate are then recalculated as the fitted values from the multiple regression of that variate on all the other variates. When all the missing values have been estimated the variate means are recalculated. If any of the means differs from the previous mean by more than a tolerance (the initial standard error divided by 1000) the process is repeated, subject to a maximum number of repetitions defined by maxIter option. The default maximum number of iterations (10) is usually sufficient when there are few missing values, say two or three. If there are many more, 20 or so, it may be necessary to increase the maximum number of iterations to around 30. The method is similar to that of Orchard & Woodbury (1972), but does not adjust for bias in the variance-covariance matrix as suggested by Beale & Little (1975).

References

Beale, E.M.L. & Little, R.J.A. (1975). Missing values in multivariate analysis. Journal of the Royal Statistical Society, Series B, 37, 129-145.

Orchard, T. & Woodbury, M.A. (1972). A missing information principle: theory and applications. In: Proceedings of the 6th Berkeley Symposium in Mathematical Statistics and Probability, Vol I, 697-715.

Examples

# NOT RUN {
M <- matrix(c("1", "2", "3", NA, "b", "5", "6",
              "6", "5", "b", NA, "3", "2", "1"), nrow = 7, ncol = 2)

## Estimate missing values treating "b" as NA.
multMissing(M, naStrings = "b")

# }