imputeUDLs: Imputation of values above an upper detection limit in...

In matthias-da/robCompositions: Compositional Data Analysis

Imputation of values above an upper detection limit in compositional data

Description

Parametric replacement of values above upper detection limit for compositional data using classical and robust methods (possibly also the pls method) based on ilr-transformations with special choice of balances.

Usage

imputeUDLs(
  x,
  maxit = 10,
  eps = 0.1,
  method = "lm",
  dl = NULL,
  variation = TRUE,
  nPred = NULL,
  nComp = "boot",
  bruteforce = FALSE,
  noisemethod = "residuals",
  noise = FALSE,
  R = 10,
  correction = "normal",
  verbose = FALSE
)

Arguments

x	data.frame or matrix
maxit	maximum number of iterations
eps	convergency criteria
method	either "lm", "lmrob" or "pls"
dl	Detection limit for each variable. zero for variables with variables that have no detection limit problems.
variation,	if TRUE those predictors are chosen in each step, who's variation is lowest to the predictor.
nPred,	if determined and variation equals TRUE, it fixes the number of predictors
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.
bruteforce	sets imputed values above the detection limit to the detection limit. Replacement above the detection limit are only exeptionally occur due to numerical instabilities. The default is FALSE!
noisemethod	adding noise to imputed values. Experimental
noise	TRUE to activate noise (experimental)
R	number of bootstrap samples for the determination of pls components. Only important for method “pls”.
correction	normal or density
verbose	additional print output during calculations.

Details

imputeUDLs

An imputation method for right-censored compositional data. Statistical analysis is not possible with values reported in data, for example as ">10000". These values are replaced using tobit regression.

The algorithm iteratively imputes parts with values above upper detection limit whereas in each step (1) compositional data are expressed in pivot coordinates (2) tobit regression is applied (3) the values above upper detection limit are replaced by the expected values (4) the corresponding inverse ilr mapping is applied. After all parts are imputed, the algorithm starts again until the imputations only change marginally.

Value

x	imputed data
criteria	change between last and second last iteration
iter	number of iterations
maxit	maximum number of iterations
wind	index of values above upper detection limit
nComp	number of components for method pls
method	chosen method

Author(s)

Peter Filzmoser, Dominika Miksova based on function imputeBDLs code from Matthias Templ

References

Martin-Fernandez, J.A., Hron K., Templ, M., Filzmoser, P. and Palarea-Albaladejo, J. (2012). Model-based replacement of rounded zeros in compositional data: Classical and robust approaches. Computational Statistics and Data Analysis, 56, 2688-2704.

Templ, M. and Hron, K. and Filzmoser and Gardlo, A. (2016). Imputation of rounded zeros for high-dimensional compositional data. Chemometrics and Intelligent Laboratory Systems, 155, 183-190.

See Also

imputeBDLs

Examples

data(gemas)  # read data
dat <- gemas[gemas$COUNTRY=="HEL",c(12:29)]
UDL <- apply(dat,2,max)
names(UDL) <- names(dat)
UDL["Mn"] <- quantile(dat[,"Mn"], probs = 0.8)  # UDL present only in one variable
whichudl <- dat[,"Mn"] > UDL["Mn"] 
# classical method
imp.lm <- dat
imp.lm[whichudl,"Mn"] <- Inf
res.lm <- imputeUDLs(imp.lm, dl=UDL, method="lm", variation=TRUE)
imp.lm <- res.lm$x

matthias-da/robCompositions documentation built on Jan. 15, 2024, 11:24 p.m.