imputeData: Missing data imputation via the 'mix' package

In mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

Missing data imputation via the mix package

Description

Imputes missing data using the mix package.

Usage

imputeData(data, categorical = NULL, seed = NULL, verbose = interactive())

Arguments

data	A numeric vector, matrix, or data frame of observations containing missing values. Categorical variables are allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.
categorical	A logical vectors whose ith entry is TRUE if the ith variable or column of data is to be interpreted as categorical and FALSE otherwise. The default is to assume that a variable is to be interpreted as categorical only if it is a factor.
seed	A seed for the function rngseed that is used to initialize the random number generator in mix. By default, a seed is chosen uniformly in the interval (.Machine$integer.max/1024, .Machine$integer.max).
verbose	A logical, if TRUE reports info about iterations of the algorithm.

Value

A dataset of the same dimensions as data with missing values filled in.

References

Schafer J. L. (1997). Analysis of Imcomplete Multivariate Data, Chapman and Hall.

See Also

imputePairs

Examples

# Note that package 'mix' must be installed
data(stlouis, package = "mix")
 
# impute the continuos variables in the stlouis data
stlimp <- imputeData(stlouis[,-(1:3)])

# plot imputed values
imputePairs(stlouis[,-(1:3)], stlimp)

mclust documentation built on May 29, 2024, 8:06 a.m.