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'

Value

A data.frame

Author

Caspar J. van Lissa

Examples

if (FALSE) { 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() }