Error performance measures

Various error measures evaluating the quality of imputations

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.

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)
#>  Sepal.Width Petal.Length  Petal.Width  Sepal.Width Petal.Length  Petal.Width 
#>          2.0          1.0          0.1          4.4          6.9          2.5 
#> Sepal.Length Petal.Length  Petal.Width Sepal.Length Petal.Length  Petal.Width 
#>          4.3          1.0          0.1          7.9          6.9          2.5 
#> Sepal.Length  Sepal.Width Petal.Length  Petal.Width Sepal.Length  Sepal.Width 
#>          4.3          2.0          1.0          0.1          7.9          4.4 
#> Petal.Length  Petal.Width 
#>          6.9          2.5 
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