R: Error performance measures

evaluation {VIM} | R Documentation

## 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)

    mape(x, y, m)

    lr(x, y, m)

    smape(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 <- VIM::kNN(iris_imp, imp_var = FALSE)
    ## Not run:
    evaluation(iris_orig, iris_imp, m = m, vartypes = c(rep("numeric", 4), "factor"))
    msecov(iris_orig[, 1:4], iris_imp[, 1:4])

    ## End(Not run)

[Package VIM version 5.1.1 Index]