padMis.syn.r

padMis.syn <- function(data, method, predictor.matrix, visit.sequence,
                       nvar, rules, rvalues, default.method, cont.na, 
                       smoothing, event, denom) {

 # Function called by syn to make dummy/factor variable for missing values
 # in continuous variables. Data is augmented by columns for dummy/factor 
 # variables when they are used in synthesis. 

 # Check presence of missing values not covered by missing rules
 # (missing values for non-numeric variables are not counted)   
 No.NA <- vector("list", nvar)
 yes.rules <- sapply(rules, function(x) any(x != ""))
 com.rules <- lapply(rules, paste, collapse = " | ")
 for (j in 1:nvar) {
   if (yes.rules[j]) {
     No.NA[j] <- with(data,sum(data[!eval(parse(text = com.rules[[j]])), j] %in% cont.na[[j]]))      
   } else {
     No.NA[j] <- sum(data[,j] %in% cont.na[[j]]) 
   }
 }
 No.NA    <- sapply(No.NA,function(x) x > 0)
 inpred   <- apply(predictor.matrix != 0, 1, any) | apply(predictor.matrix != 0, 2, any)
 factorNA <- rep(FALSE, nvar)

 for (j in 1:nvar) {
    # if (No.NA[j] & is.numeric(data[,j]) & inpred[j]==TRUE){  #GR1.7-1
    if (No.NA[j] & is.numeric(data[,j]) & inpred[j] == TRUE & 
        !is.passive(method[j]) & !method[j] %in% c("nested", "ipf", "catall")) {

    # augment the data with a column for the original continuous variable with 
    # missing values replaced by zeros and a column for a new factor for 
    # missing values 
      nonmiscode <- 10^(nchar(round(max(data[,j], na.rm = TRUE))) + 1) - 1               
      y.0  <- ifelse(data[,j] %in% c(cont.na[[j]], rvalues[[j]]), 0, data[,j])
      y.NA <- ifelse(data[,j] %in% c(cont.na[[j]], rvalues[[j]]), data[,j], nonmiscode) #BN13/11 0 changed with nonmiscode
      y.NA <- addNA(y.NA, ifany = TRUE) 
      levels(y.NA)[is.na(levels(y.NA))] <- "NAtemp"   # to allow random forest
      data <- cbind(data,y.0,y.NA)           
      name.0  <- paste(attr(data,"names")[j], 0, sep = ".")
      name.NA <- paste(attr(data,"names")[j], NA, sep = ".")
      
      names(data)[(ncol(data) - 1):ncol(data)] <- c(name.0, name.NA)
      factorNA[(ncol(data) - 1):ncol(data)] <- c(FALSE,TRUE) 

    # predictor.matrix is given two extra rows and columns for the new variables
    # rows and columns are copied from an original variable j in predictor.matrix
      predictor.matrix <- rbind(predictor.matrix,  matrix(rep(predictor.matrix[j,], 
                               times = 2), byrow = TRUE, nrow = 2))
      predictor.matrix <- cbind(predictor.matrix, matrix(rep(predictor.matrix[,j], 
                               times = 2), ncol = 2))
    # the original variable is removed from predictors (=insert zeros) 
      predictor.matrix[,j] <- 0
    # the original variable is synthesized passively so its predictors can be removed as well
      predictor.matrix[j,] <- 0

    # add methods for new variables
      method[ncol(data) - 1] <- method[j]
      if (method[j] %in% c("ctree", "ctree.proper", "cart", "cart.proper",
                           "rf", "ranger", "bag",
                           "sample", "")) {
        method[ncol(data)] <- method[j] 
      } else {
        method[ncol(data)] <- ifelse(nlevels(data[,ncol(data)]) == 2,
                                     default.method[2], default.method[3])
      }   
     
    # pass smoothing to new variable and remove from original one
      smoothing[ncol(data) - 1] <- smoothing[j]
      smoothing[ncol(data)]   <- ""
      smoothing[j]            <- ""  
      
      cont.na[ncol(data) - 1] <- cont.na[j]
      cont.na[ncol(data)]     <- NA
      cont.na[j]              <- NA  
    
    # pass denom and event for new variable and remove from original one
      denom[ncol(data) - 1] <- denom[j]    #!!!!!!!!!!!!!! check if correct
      denom[ncol(data)]     <- 0 
      denom[j]              <- 0
      event[ncol(data) - 1] <- event[j]    
      event[ncol(data)]     <- 0 
      event[j]              <- 0
    
    # insert the column numbers for the new variables into the visit sequence 
    # before the jth column
      if (any(visit.sequence == j)) {                    
        newcols <- c(ncol(data), ncol(data) - 1)
        idx <- (1:length(visit.sequence))[visit.sequence == j] - 1
        visit.sequence <- append(visit.sequence,newcols,idx)
      # modify method for the original variable
        method[j] <- paste0("~(ifelse(",name.NA,"!=", nonmiscode," | is.na(",name.0,
            "),as.numeric(levels(",name.NA,"))[",name.NA,"],",name.0,"))")    
      }

    # update missing rules and values for the new variables
      if (any(rules[[j]] != "")) rules[[ncol(data) - 1]] <-
        c(rules[[j]][rules[[j]] != ""],paste(name.NA, "!=", nonmiscode, sep = ""))  #BN13/11
      else rules[[ncol(data) - 1]] <- paste(name.NA, "!=", nonmiscode, sep = "")    #BN13/11 
      rules[[ncol(data)]]        <- rules[[j]]                      
      rules[[j]]                 <- ""
      #!BN1513 rule "year_death.NA!=0" should have only one correspnding 
      #! rvalue equal to 0; before a vector c(NA,0) was assigned instead of 0 
      if (length(rules[[j]]) == 1) rvalues[[ncol(data) - 1]] <- 0           
      else rvalues[[ncol(data) - 1]] <- c(rvalues[[j]], 0)                 
      rvalues[[ncol(data)]]        <- rvalues[[j]]
    }
  }

  varnames <- dimnames(data)[[2]]  
  dimnames(predictor.matrix) <- list(varnames,varnames)
  names(method) <- varnames
  names(visit.sequence) <- varnames[visit.sequence]
  return(list(data = as.data.frame(data), 
              nvar = ncol(data),
              predictor.matrix = predictor.matrix, 
              method = method, 
              visit.sequence = visit.sequence, 
              rules = rules,
              rvalues = rvalues,
              factorNA = factorNA,
              smoothing = smoothing,
              event = event,
              denom = denom,
              cont.na = cont.na))
}