single_imputation function

Apply single imputation to data

This function accommodates several methods for single imputation of data. Currently, the following methods are defined:

• "imputeData"Applies the mclust native imputation function imputeData
• "missForest"Applies non-parameteric, random-forest based data imputation using missForest. Radom forests can accommodate any complex interactions and non-linear relations in the data. My simulation studies indicate that this method is preferable to mclust's imputeData (see examples).

single_imputation(x, method = "imputeData")

Arguments

• x: A data.frame or matrix.
• method: Character. Imputation method to apply, Default: 'imputeData'

Returns

A data.frame

Examples

## Not run:

library(ggplot2)
library(missForest)
library(mclust)

dm <- 2
k <- 3
n <- 100
V <- 4

# Example of one simulation
class <- sample.int(k, n, replace = TRUE)
dat <- matrix(rnorm(n*V, mean = (rep(class, each = V)-1)*dm), nrow  = n,
              ncol = V, byrow = TRUE)
results <- estimate_profiles(data.frame(dat), 1:5)
plot_profiles(results)
compare_solutions(results)

# Simulation for parametric data (i.e., all assumptions of latent profile
# analysis met)
simulation <- replicate(100, {
    class <- sample.int(k, n, replace = TRUE)
    dat <- matrix(rnorm(n*V, mean = (rep(class, each = V)-1)*dm), nrow  = n,
                  ncol = V, byrow = TRUE)

    d <- prodNA(dat)

    d_mf <- missForest(d)$ximp
    m_mf <- Mclust(d_mf, G = 3, "EEI")
    d_im <- imputeData(d, verbose = FALSE)
    m_im <- Mclust(d_im, G = 3, "EEI")

    class_tabl_mf <- sort(prop.table(table(class, m_mf$classification)),
                          decreasing = TRUE)[1:3]
    class_tabl_im <- sort(prop.table(table(class, m_im$classification)),
                          decreasing = TRUE)[1:3]
    c(sum(class_tabl_mf), sum(class_tabl_im))
})
# Performance on average
rowMeans(simulation)
# Performance SD
colSD(t(simulation))
# Plot shows slight advantage for missForest
plotdat <- data.frame(accuracy = as.vector(simulation), model =
                      rep(c("mf", "im"), n))
ggplot(plotdat, aes(x = accuracy, colour = model))+geom_density()

# Simulation for real data (i.e., unknown whether assumptions are met)
simulation <- replicate(100, {
    d <- prodNA(iris[,1:4])

    d_mf <- missForest(d)$ximp
    m_mf <- Mclust(d_mf, G = 3, "EEI")
    d_im <- imputeData(d, verbose = FALSE)
    m_im <- Mclust(d_im, G = 3, "EEI")

    class_tabl_mf <- sort(prop.table(table(iris$Species,
                          m_mf$classification)), decreasing = TRUE)[1:3]
    class_tabl_im <- sort(prop.table(table(iris$Species,
                          m_im$classification)), decreasing = TRUE)[1:3]
    c(sum(class_tabl_mf), sum(class_tabl_im))
})

# Performance on average
rowMeans(simulation)
# Performance SD
colSD(t(simulation))
# Plot shows slight advantage for missForest
plotdat <- data.frame(accuracy = as.vector(tmp),
                      model = rep(c("mf", "im"), n))
ggplot(plotdat, aes(x = accuracy, colour = model))+geom_density()
## End(Not run)

Author(s)

Caspar J. van Lissa