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

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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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

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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

robCompositions documentation built on Jan. 13, 2021, 10:07 p.m.