impRZalr: alr EM-based imputation of rounded zeros

In robCompositions: Compositional Data Analysis

alr EM-based imputation of rounded zeros

Description

A modified EM alr-algorithm for replacing rounded zeros in compositional data sets.

Usage

impRZalr(
  x,
  pos = ncol(x),
  dl = rep(0.05, ncol(x) - 1),
  eps = 1e-04,
  maxit = 50,
  bruteforce = FALSE,
  method = "lm",
  step = FALSE,
  nComp = "boot",
  R = 10,
  verbose = FALSE
)

Arguments

x	compositional data
pos	position of the rationing variable for alr transformation
dl	detection limit for each part
eps	convergence criteria
maxit	maximum number of iterations
bruteforce	if TRUE, imputations over dl are set to dl. If FALSE, truncated (Tobit) regression is applied.
method	either “lm” (default) or “MM”
step	if TRUE, a stepwise (AIC) procedure is applied when fitting models
nComp	if determined, it fixes the number of pls components. If “boot”, the number of pls components are estimated using a bootstraped cross validation approach.
R	number of bootstrap samples for the determination of pls components. Only important for method “pls”.
verbose	additional print output during calculations.

Details

Statistical analysis of compositional data including zeros runs into problems, because log-ratios cannot be applied. Usually, rounded zeros are considerer as missing not at random missing values. The algorithm first applies an additive log-ratio transformation to the compositions. Then the rounded zeros are imputed using a modified EM algorithm.

Value

xOrig	Original data frame or matrix
xImp	Imputed data
wind	Index of the missing values in the data
iter	Number of iterations
eps	eps

Author(s)

Matthias Templ and Karel Hron

References

Palarea-Albaladejo, J., Martin-Fernandez, J.A. Gomez-Garcia, J. (2007) A parametric approach for dealing with compositional rounded zeros. Mathematical Geology, 39(7), 625-645.

See Also

impRZilr

Examples

data(arcticLake)
x <- arcticLake
## generate rounded zeros artificially:
x[x[,1] < 5, 1] <- 0
x[x[,2] < 47, 2] <- 0
xia <- impRZalr(x, pos=3, dl=c(5,47), eps=0.05)
xia$xImp

robCompositions documentation built on Aug. 25, 2023, 5:13 p.m.