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", 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.

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. Computational Statistics & 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 Sept. 2, 2026, 5:07 p.m.