robCompositions (version 2.4.1)

impCoda: Imputation of missing values in compositional data

Description

This function offers different methods for the imputation of missing values in compositional data. Missing values are initialized with proper values. Then iterative algorithms try to find better estimations for the former missing values.

Usage

impCoda(
  x,
  maxit = 10,
  eps = 0.5,
  method = "ltsReg",
  closed = FALSE,
  init = "KNN",
  k = 5,
  dl = rep(0.05, ncol(x)),
  noise = 0.1,
  bruteforce = FALSE
)

Value

xOrig

Original data frame or matrix

xImp

Imputed data

criteria

Sum of the Aitchison distances from the present and previous iteration

iter

Number of iterations

maxit

Maximum number of iterations

w

Amount of imputed values

wind

Index of the missing values in the data

Arguments

x

data frame or matrix

maxit

maximum number of iterations

eps

convergence criteria

method

imputation method

closed

imputation of transformed data (using ilr transformation) or in the original space (closed equals TRUE)

init

method for initializing missing values

k

number of nearest neighbors (if init $==$ “KNN”)

dl

detection limit(s), only important for the imputation of rounded zeros

noise

amount of adding random noise to predictors after convergency

bruteforce

if TRUE, imputations over dl are set to dl. If FALSE, truncated (Tobit) regression is applied.

Author

Matthias Templ, Karel Hron

Details

eps: The algorithm is finished as soon as the imputed values stabilize, i.e. until the sum of Aitchison distances from the present and previous iteration changes only marginally (eps).\

method: Several different methods can be chosen, such as ‘ltsReg’: least trimmed squares regression is used within the iterative procedure. ‘lm’: least squares regression is used within the iterative procedure. ‘classical’: principal component analysis is used within the iterative procedure. ‘ltsReg2’: least trimmed squares regression is used within the iterative procedure. The imputated values are perturbed in the direction of the predictor by values drawn form a normal distribution with mean and standard deviation related to the corresponding residuals and multiplied by noise.

References

Hron, K., Templ, M., Filzmoser, P. (2010) Imputation of missing values for compositional data using classical and robust methods Computational Statistics and Data Analysis, 54 (12), 3095-3107.

See Also

impKNNa, pivotCoord

Examples

data(expenditures)
x <- expenditures
x[1,3]
x[1,3] <- NA
xi <- impCoda(x)$xImp
xi[1,3]
s1 <- sum(x[1,-3])
impS <- sum(xi[1,-3])
xi[,3] * s1/impS

# other methods
impCoda(x, method = "lm")
impCoda(x, method = "ltsReg")