padModel.syn.r

padModel.syn <- function(data, method, predictor.matrix, visit.sequence,
                         nvar, rules, rvalues, factorNA, smoothing, event, denom) {

 # Function called by syn to make dummy variables data frame data is 
 # augmented by columns for dummy variables when they are used as predictors. 
 # This is returned as part of a list which also contains structures 
 # to tell sampler.syn how to select columns

  categories <- data.frame(yes.no.categorical = factor(rep(FALSE, nvar), 
                                                levels = c("TRUE", "FALSE")), 
                           number.of.dummies  = rep(0, nvar), 
                           yes.no.dummy       = factor(rep(FALSE, nvar), 
                                                levels = c("TRUE", "FALSE")), 
                           corresponding.column.dummy = rep(0, nvar))
 
 # This is a data frame with a row for each variable and extra rows for 
 # each of the dummy variables added at the end of the j loop with
 # col1 = T/F if category and predictor in parametric model                   #!BN1605
 # col2 = number of dummy variables for factors with col1==TRUE, else 0       #!BN1605
 # col3 = yes.no.dummy TRUE for dummy variables
 # col4 = corresponding.column.dummy original column number for dummies, else 0

 # pred.with.cart <- method %in% c("ctree", "ctree.proper", "cart", "cart.proper", "collinear", "satcat") #!BN to check if collinear needed
                                                                               
  pred.with.cart <- !method %in% c("norm", "normrank", "logreg", "lognorm", 
                                   "polr", "polyreg", "cubertnorm", "sqrtnorm")  #!GR050318
  for (j in 1:nvar) {
    if ((is.factor(data[,j]) & any(predictor.matrix[1:nvar,j] != 0 & !pred.with.cart)) |  #!BN-16/05/2016
        (factorNA[j] == TRUE & !pred.with.cart[j])) {                                      #!BN-16/05/2016
      categories[j, 1] <- TRUE

      # all factors defined to have treatment contrasts
      #!data[, j] <- C(data[, j], contr.treatment)                              UNCOMMENT???? BN-28/04/2016
      n.dummy   <- length(levels(data[, j])) - 1
      categories[j, 2] <- n.dummy

      # predictor.matrix is given extra rows and columns for the dummy variables
      # rows are set to zero initially
      predictor.matrix <- rbind(predictor.matrix, matrix(0,
        ncol = ncol(predictor.matrix), nrow = n.dummy))
      
      # columns are set to zero and then for vars with non-CART method        #!BN1605
      # copied from an original variable j in predictor.matrix for            #!BN1605
      # -> 1 for all the rows for which this variable is being used as        #!BN1605
      # a predictor in a non-CART model, 0 otherwise                          #!BN1605
      predictor.matrix <- cbind(predictor.matrix, matrix(0, ncol = n.dummy,     #!BN1605
                                nrow = nrow(predictor.matrix)))                  #!BN1605
      predictor.matrix[!pred.with.cart,(ncol(predictor.matrix) - n.dummy + 1):ncol(predictor.matrix)] <- matrix(rep(predictor.matrix[!pred.with.cart,j], times = n.dummy)) #!BN1605
                               
      # the original categorical variable is removed from predictors (=insert zeros)
      # for variables with non-CART method 
      predictor.matrix[!pred.with.cart,j] <- 0                                   #!BN1605


 # insert the column number for first of this set of dummies into
 # the visit sequence immediately after the jth column is predicted
      if (any(visit.sequence == j)) {
        # set an original categorical variable as predictor for its dummies  
        predictor.matrix[(ncol(predictor.matrix) - n.dummy + 1):ncol(predictor.matrix), j] <- rep(1, times = n.dummy)
        # insert dummies into visit sequence 
        newcol <- ncol(predictor.matrix) - n.dummy + 1
        nloops <- sum(visit.sequence == j)
          for (ii in 1:nloops) {
            idx <- (1:length(visit.sequence))[visit.sequence == j][ii]
            visit.sequence <- append(visit.sequence, newcol, idx)
          }
      }

 # augment the data with columns for the new dummies
      data <- (cbind(data, matrix(0, ncol = n.dummy, nrow = nrow(data))))

 # set dummies to missing when variable is missing
      data[is.na(data[, j]), (ncol(predictor.matrix) - n.dummy + 1):ncol(predictor.matrix)] <- NA
      cat.column <- data[!is.na(data[, j]), j]  # these are the non missing values of this factor

 # next bit sets the colums for the dummies to the dummy variables 
 # when data are not missing and labels columns
      data[!is.na(data[, j]), (ncol(predictor.matrix) - n.dummy + 1):ncol(predictor.matrix)] <- model.matrix(~cat.column - 1)[,-1]
      names(data)[(ncol(predictor.matrix) - n.dummy + 1):ncol(predictor.matrix)] <- paste(attr(data,"names")[j],(1:n.dummy), sep = ".")
      method     <- c(method, rep("dummy", n.dummy))
      rules      <- c(rules,rep(rules[j],n.dummy))
      rvalues    <- c(rvalues,rep(rvalues[j],n.dummy))
      categories <- rbind(categories, 
                          data.frame(yes.no.categorical = rep(FALSE,n.dummy),
                          number.of.dummies = rep(0, n.dummy), 
                          yes.no.dummy      = rep(TRUE, n.dummy), 
                          corresponding.column.dummy = rep(j,n.dummy)))
    }
  }                                                       
   
  varnames <- dimnames(data)[[2]]  # now includes dummy names
  dimnames(predictor.matrix) <- list(varnames, varnames)
  names(method) <- varnames
  names(visit.sequence) <- varnames[visit.sequence]
  dimnames(categories)[[1]] <- dimnames(data)[[2]]
  
  return(list(data = as.data.frame(data), 
              syn = as.data.frame(data),
              predictor.matrix = predictor.matrix, 
              method = method, 
              visit.sequence = visit.sequence, 
              rules = rules,
              rvalues = rvalues, 
              categories = categories,
              smoothing = smoothing,
              event = event,
              denom = denom))
}