VIM: evaluation – R documentation

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Documentation

VIM

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

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

Visualization and Imputation of Missing Values

v6.1.0

GPL (>= 2)

Authors

Matthias Templ [aut, cre], Alexander Kowarik [aut] (<https://orcid.org/0000-0001-8598-4130>), Andreas Alfons [aut], Gregor de Cillia [aut], Bernd Prantner [ctb], Wolfgang Rannetbauer [aut]

Initial release