VIM (version 6.2.2)

impPCA: Iterative EM PCA imputation

Description

Greedy algorithm for EM-PCA including robust methods

Usage

impPCA(
  x,
  method = "classical",
  m = 1,
  eps = 0.5,
  k = ncol(x) - 1,
  maxit = 100,
  boot = FALSE,
  verbose = TRUE
)

Value

the imputed data set. If boot = FALSE this is a data.frame. If boot = TRUE this is a list where each list element contains a data.frame.

Arguments

x

data.frame or matrix

method

"classical" or "mcd" (robust estimation)

m

number of multiple imputations (only if parameter boot equals TRUE)

eps

threshold for convergence

k

number of principal components for reconstruction of x

maxit

maximum number of iterations

boot

residual bootstrap (if TRUE)

verbose

TRUE/FALSE if additional information about the imputation process should be printed

Author

Matthias Templ

References

Serneels, Sven and Verdonck, Tim (2008). Principal component analysis for data containing outliers and missing elements. Computational Statistics and Data Analysis, Elsevier, vol. 52(3), pages 1712-1727

See Also

Other imputation methods: hotdeck(), irmi(), kNN(), matchImpute(), medianSamp(), rangerImpute(), regressionImp(), sampleCat()

Examples

data(Animals, package = "MASS")
Animals$brain[19] <- Animals$brain[19] + 0.01
Animals <- log(Animals)
colnames(Animals) <- c("log(body)", "log(brain)")
Animals_na <- Animals
probs <- abs(Animals$`log(body)`^2)
probs <- rep(0.5, nrow(Animals))
probs[c(6,16,26)] <- 0
set.seed(1234)
Animals_na[sample(1:nrow(Animals), 10, prob = probs), "log(brain)"] <- NA
w <- is.na(Animals_na$`log(brain)`)
impPCA(Animals_na)
impPCA(Animals_na, method = "mcd")
impPCA(Animals_na, boot = TRUE, m = 10)
impPCA(Animals_na, method = "mcd", boot = TRUE)[[1]]
plot(`log(brain)` ~ `log(body)`, data = Animals, type = "n", ylab = "", xlab="")
mtext(text = "impPCA robust", side = 3)
points(Animals$`log(body)`[!w], Animals$`log(brain)`[!w])
points(Animals$`log(body)`[w], Animals$`log(brain)`[w], col = "grey", pch = 17)
imputed <- impPCA(Animals_na, method = "mcd", boot = TRUE)[[1]]
colnames(imputed) <- c("log(body)", "log(brain)")
points(imputed$`log(body)`[w], imputed$`log(brain)`[w], col = "red", pch = 20, cex = 1.4)
segments(x0 = Animals$`log(body)`[w], x1 = imputed$`log(body)`[w], y0 = Animals$`log(brain)`[w],
y1 = imputed$`log(brain)`[w], lty = 2, col = "grey")
legend("topleft", legend = c("non-missings", "set to missing", "imputed values"),
pch = c(1,17,20), col = c("black","grey","red"), cex = 0.7)
mape <- round(100* 1/sum(is.na(Animals_na$`log(brain)`)) * sum(abs((Animals$`log(brain)` -
imputed$`log(brain)`) / Animals$`log(brain)`)), 2)
s2 <- var(Animals$`log(brain)`)
nrmse <- round(sqrt(1/sum(is.na(Animals_na$`log(brain)`)) * sum(abs((Animals$`log(brain)` -
imputed$`log(brain)`) / s2))), 2)
text(x = 8, y = 1.5, labels = paste("MAPE =", mape))
text(x = 8, y = 0.5, labels = paste("NRMSE =", nrmse))