Error performance measures

Various error measures evaluating the quality of imputations

evaluation(x, y, m, vartypes = "guess", where = NULL)

nrmse(x, y, m)

pfc(x, y, m)

msecov(x, y)

msecor(x, y)

Arguments

x

matrix or data frame

y

matrix or data frame of the same size as x

m

the indicator matrix for missing cells (kept for backward compatibility; where is the documented name)

vartypes

a vector of length ncol(x) specifying the variable types ("numeric" or "factor"). The default "guess" infers the types from the columns of x (numeric columns become "numeric", everything else "factor").

where

the indicator matrix for missing cells under its documented name – the amputed-cell mask as returned in makeMissing()'s "where" attribute. Supply either m or where, not both.

Value

the error measures value

Details

This function has been mainly written for procudures that evaluate imputation or replacement of rounded zeros. The ni parameter can thus, e.g. be used for expressing the number of rounded zeros.

References

M. Templ, A. Kowarik, P. Filzmoser (2011) Iterative stepwise regression imputation using standard and robust methods. Computational Statistics & Data Analysis, Vol. 55, pp. 2793-2806.

Author

Matthias Templ

Examples

data(iris)
iris_orig <- iris_imp <- iris
iris_imp$Sepal.Length[sample(1:nrow(iris), 10)] <- NA
iris_imp$Sepal.Width[sample(1:nrow(iris), 10)] <- NA
iris_imp$Species[sample(1:nrow(iris), 10)] <- NA
m <- is.na(iris_imp)
iris_imp <- kNN(iris_imp, imp_var = FALSE)
evaluation(iris_orig, iris_imp, m = m, vartypes = c(rep("numeric", 4), "factor"))
#> $err_num
#> [1] 0.149
#> 
#> $err_cat
#> [1] 0
#> 
#> $error
#> [1] 0.149
#> 
msecov(iris_orig[, 1:4], iris_imp[, 1:4])
#> [1] 0.0001986385