Nothing

```
#' function redcube
#'
#' This function produces a reduced small count frequency hypercube for rounding.
#'
#' @encoding UTF8
#'
#' @param A A data frame representing a micro dataset or a frequency count hypercube. The (first) columns
#' define the variables. If A is a hypercube the last column contains the number of units in each cell.
#' If A is a micro dataset it is reduced to hypercube by the function aggrtab.
#' @param d A list d{[[j]]} whose elements are vectors of variable names from A defining marginal tables/cubes D
#' of A that we are interested in.
#' @param b Rounding base. Counts in A less than b tat are contributing to counts less than b in the marginal
#' cubes D are selected from A. The selected dataframe is called B
#' @param micro Logical. TRUE if A is a micro dataset (default). FALSE if A i a frequency count hypercube.
#' @param nin Name of count variable if A is a hypercube. Default name: "n".
#'
#' @return
#' A: The input dataframe reduced to a hypercube.
#'
#' B: The dataframe of small count rows selected from A.
#'
#' C: The dataframe of rows in A that are nor selected from A.
#'
#' D: The cubes defined by d.
#'
#' Dr: The small counts (<b) in D.
#'
#' The input elements d, b and nin
#'
#' @keywords internal
#'
#' @author Johan Heldal, November 2017
#'
redcube <- function(A,d,b=3,micro=TRUE,nin="n") {
AD <- aggrtab(A,d,micro=micro,nin=nin,nout=nin)
A <- AD[[1]]
ncA <- ncol(A) - 1 # Number of variables spanning A
Avars <- colnames(A[,1:ncA])
#
# For each j, create the tables Dr[[j]] of small cell counts in D[[j]].
# For each j identify the cells DA[[j]] in A aggregating to a cell in Dr[[j]]
# Build the reduced hypercube B as the union of cells in {DA[[j]]}.
#
D <- AD[[2]]
ld <- length(d)
Dr <- as.list(NULL) # List of small count cells in D
ninx <- paste(nin,".x",sep="",collapse=NULL)
niny <- paste(nin,".y",sep="",collapse=NULL)
j <- 0
while((j <- j + 1) <= ld) {
dn <- d[[j]] # Select the variable names for marginal table j
Dr[[j]] <- D[[j]][D[[j]][,nin]<b,] # Selects small cells in D[[j]]
#
# Select cells in A that aggregate to cells in Dr[[j]]
#
E <- merge(A,Dr[[j]],by.x=d[[j]],by.y=d[[j]])[,1:(ncA + 1)]
colnames(E) <- c(colnames(E[,1:ncA]),nin)
if (j==1) {B <- E}
if (j>1) {
B <- merge(B,E,by=Avars,all=TRUE)
B[,ninx][is.na(B[,ninx])] <- B[,niny][is.na(B[,ninx])]
}
B <- B[,1:(ncA+1)]
colnames(B) <- c(colnames(B[,1:ncA]),nin)
#cat("j = ", j,"\n","dim(E) = ",dim(E),"\n","dim(D[[j]]) = ",dim(D[[j]]),"\n",
#"dim(Dr[[j]]) = ",dim(Dr[[j]]),"\n","dim(B) = ",dim(B),"\n")
}
#
# Calculate C, the dataframe og rows in A that are not in B
#
C <- merge(A,B,by=Avars,all.x=TRUE)
C <- C[is.na(C[,niny]),]
C <- C[,1:(ncA+1)]
colnames(C) <- colnames(B)
return(list(Acube=A, Bcube=B, Ccube=C, Dcubes=D, Drcubes=Dr, b=b, d=d, nin=nin))
}
```

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