VIM (version 6.0.0)

evaluation: 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

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. Journal of Computational Statistics and Data Analysis, Vol. 55, pp. 2793-2806.

Examples

# NOT RUN {
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])
# }