MIPCA: Multiple Imputation with PCA

In missMDA: Handling missing values with/in multivariate data analysis (principal component methods)

Description

MIPCA performs Multiple Imputation with a PCA model. Can be used as a preliminary step to perform Multiple Imputation in PCA

Usage

MIPCA(X, ncp = 2, scale = TRUE, method = "Regularized", 
   threshold = 1e-04, nboot = 100)

Arguments

X	a data.frame with continuous variables containing missing values
ncp	integer corresponding to the number of components used to reconstruct data with the PCA reconstruction formulae
scale	boolean. By default TRUE leading to a same weight for each variable
method	"Regularized" by default or "EM"
threshold	the threshold for the criterion convergence
nboot	the number of imputed datasets

Details

MIPCA generates nboot imputed datasets from a PCA model. The observed values are the same from one dataset to the others whereas the imputed values change. The variation among the imputed values reflects the variability with which missing values can be predicted. The multiple imputation is proper in the sense of Little and Rubin (2002) since it takes into account the variability of the parameters.

Value

res.imputePCA	A matrix corresponding to the imputed dataset obtained with the function imputePCA (the completed dataset)
res.MI	An array corresponding to nboot imputed dataset. The dimensions of the array are: the number of row of X, the number of column of X and nboot

Author(s)

Francois Husson [e-mail] and Julie Josse [e-mail]

References

Josse, J., Husson, F. (2010). Multiple Imputation in PCA.

See Also

imputePCA,plot.MIPCA

Examples

data(orange)
## First the number of components has to be chosen 
##   (for the reconstruction step)
## nb <- estim_ncpPCA(orange,ncp.max=5) ## Time consuming, nb = 2

## Multiple Imputation
resMI <- MIPCA(orange,ncp=2)

## Visualization on the PCA map
plot(resMI)

Example output

dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")
dev.new(): using pdf(file="Rplots3.pdf")

missMDA documentation built on May 2, 2019, 5:46 p.m.