impPCA function

Iterative EM PCA imputation

Iterative EM PCA imputation

Greedy algorithm for EM-PCA including robust methods

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

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

Returns

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.

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))

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()

Author(s)

Matthias Templ

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