Imputation: Imputation of missing parts in compositions from a SGB model
In SGB: Simplicial Generalized Beta Regression

Description Usage Arguments Value Examples

Applied to a completely missing composition, the function returns the Aitchison expectation.
Applied to a partially missing composition, it returns the conditional Aitchison expectation, given the observed sub-composition.
Applied to a complete case, it returns the complete case.

1	impute.regSGB(obj, dsup, usup)

`obj`	list, output of regSGB.
`dsup`	data frame with explanatory variables for the incomplete compositions. Missing values not allowed.
`usup`	compositions corresponding to `dsup`. On each row, the non-missing parts sum to 1.

data frame with imputed compositions instead of missing or partially missing compositions. Complete cases are also returned.

## Arctic lake
data(arc)
arcmis <- arc
arc[11:13,]

## Introduce NA alues

arcmis[11,2] <- NA      # "core" observation
arcmis[12,3] <- NA      # outlying clay value
arcmis[13,1:3] <- NA    # totally missing observation
umis <- arcmis[,1:3]
umis <- umis/rowSums(umis,na.rm=TRUE)
umis[11:13,]

d <- data.frame(depth=arc[["depth"]])

## original compositions
arc[11:13,1:3]

## unconditional predicted value                            
MeanA.SGB(oilr[["par"]][1],oilr[["scale"]],oilr[["par"]][4:6] )[11:13,]

## predicted value given the sub-composition (sand,clay) for 11, (sand,silt) for 12        
impute.regSGB(oilr,arcmis,umis)[11:13, ]

impute.regSGB(oilr,arcmis[11:13, ],umis[11:13, ])  # same result.