This function estimates missing values for units in a multivariate dataset, using an iterative regression technique.
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
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")
#> [,1] [,2]
#> [1,] 1.000000 6.000000
#> [2,] 2.000000 5.000000
#> [3,] 3.000000 3.999972
#> [4,] 3.499975 3.499995
#> [5,] 3.999851 3.000000
#> [6,] 5.000000 2.000000
#> [7,] 6.000000 1.000000