evaluation: Error performance measures

In VIM: Visualization and Imputation of Missing Values

Error performance measures

Description

Various error measures evaluating the quality of imputations

Usage

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

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
vartypes	a vector of length ncol(x) specifying the variables types, like factor or numeric

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.

Value

the error measures value

Author(s)

Matthias Templ

References

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

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"))
msecov(iris_orig[, 1:4], iris_imp[, 1:4])

VIM documentation built on Aug. 25, 2022, 5:07 p.m.