robCompositions (version 2.4.1)

imputeBDLs: EM-based replacement of rounded zeros in compositional data

Description

Parametric replacement of rounded zeros for compositional data using classical and robust methods based on ilr coordinates with a special choice of balances.

Usage

imputeBDLs(
  x,
  maxit = 10,
  eps = 0.1,
  method = "subPLS",
  dl = rep(0.05, ncol(x)),
  variation = TRUE,
  nPred = NULL,
  nComp = "boot",
  bruteforce = FALSE,
  noisemethod = "residuals",
  noise = FALSE,
  R = 10,
  correction = "normal",
  verbose = FALSE,
  test = FALSE
)
adjustImputed(xImp, xOrig, wind)
checkData(x, dl)
# S3 method for replaced
print(x, ...)

Value

x

imputed data

criteria

change between last and second last iteration

iter

number of iterations

maxit

maximum number of iterations

wind

index of zeros

nComp

number of components for method pls

method

chosen method

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.

test

an internal test situation (this parameter will be deleted soon)

xImp

imputed data set

xOrig

original data set

wind

index matrix of rounded zeros

...

further arguments passed through the print function

Author

Matthias Templ, method subPLS from Jiajia Chen

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 iteratively imputes parts with rounded zeros whereas in each step (1) compositional data are expressed in pivot coordinates (2) tobit regression is applied (3) the rounded zeros 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 do not change.

References

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

Chen, J., Zhang, X., Hron, K., Templ, M., Li, S. (2018). Regression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional data. Journal of Applied Statistics, 45 (11), 2067-2080.

See Also

imputeBDLs

Examples

p <- 10
n <- 50
k <- 2
T <- matrix(rnorm(n*k), ncol=k)
B <- matrix(runif(p*k,-1,1),ncol=k)
X <- T %*% t(B)
E <-  matrix(rnorm(n*p, 0,0.1), ncol=p)
XE <- X + E
data <- data.frame(pivotCoordInv(XE))
col <- ncol(data)
row <- nrow(data)
DL <- matrix(rep(0),ncol=col,nrow=1)
for(j in seq(1,col,2))
{DL[j] <- quantile(data[,j],probs=0.06,na.rm=FALSE)}

for(j in 1:col){        
  data[data[,j]