.logit4Pred <-
function(lm.out, nm, d, my_formula, brief, res_rows,
         n.vars, n.pred, n.obs, n.keep, pt_size, transparency,
         digits_d, pre, line,
         new.data, pred, pred_all, prob_cut, 
         in.data.frame, X1_new, 
         X2_new, X3_new, X4_new, X5_new, X6_new,
         xlab, ylab, pdf_file, width, height, ...) {


  pred_sort <- TRUE  # data must be sorted to find cases close to fitted=0.5

  # ----------
  # prediction
  # ----------

  for (i.cut in 1:length(prob_cut)) {

    # if length of prob_cut > 1, additional values for confusion matrix only
    # p.cut  for pred output, equal to prob_cut if only 1 value
    # prob_cut for confusion matrices
    if (i.cut == 1) {

      if (length(prob_cut) == 1)
        p.cut <- prob_cut[1]
      else
        p.cut <- 0.5
    }
    else
      p.cut <- prob_cut[i.cut]

    # get output pred table, with label
    if (!new.data) {
      p.int <- data.frame(predict(lm.out, type="response", se.fit=TRUE))


      # classify
      label <- integer(length=nrow(p.int))
      for (irow in 1:nrow(p.int))
        label[irow] <- ifelse (p.int$fit[irow] < p.cut, 0, 1)
      out <- cbind(lm.out$model[c(nm[seq(2,n.vars)], nm[1])], label, 
                   p.int$fit, p.int$se.fit)
    }  # end !new.data

    else {  # new data
      Xnew.val <- list(X1_new)
      if (n.vars > 2) for (i in 2:(n.pred)) {
        pp <- eval(parse(text=paste("X", toString(i),"_new",sep="")))
        Xnew.val <- c(Xnew.val, list(pp))
      }
      Xnew <- expand.grid(Xnew.val)
      for (i in 1:(n.pred)) names(Xnew)[i] <- nm[i+1]

      p.int <- data.frame(predict(lm.out, type="response",
                          se.fit=TRUE, newdata=Xnew))

      # classify
      label <- integer(length=nrow(p.int))
      for (i in 1:nrow(p.int))
        label[i] <- ifelse (p.int$fit[i] < p.cut, 0, 1)

      Ynew <- character(length=nrow(Xnew))  # blank Y column for new data
      out <- cbind(Xnew, Ynew, label, p.int$fit,p.int$se.fit)
    }

    # display pred output only if on first iteration of prob_cut
    if (i.cut == 1) {
     
      cat("\n\n", "  PREDICTION", "\n\n")

      cat("Probability threshold for classification ",
          levels(lm.out$model[,nm[1]])[2], ":", " ", p.cut, "\n\n", sep="")

      if (is.factor(lm.out$model[,nm[1]]))
        cat(" 0: ", levels(lm.out$model[,nm[1]])[1], "\n",
            " 1: ", levels(lm.out$model[,nm[1]])[2], "\n", sep="")
      cat("\n")

      cat("Data, Fitted Values, Standard Errors\n")
      cat("   [sorted by fitted value]\n")
      if (n.keep > 50 && pred_all == FALSE && !new.data) 
        cat("   [pred_all=TRUE to see all intervals displayed]\n")

      names(out)[n.vars+1] <- "label"
      names(out)[n.vars+2] <- "fitted"
      names(out)[n.vars+3] <- "std.err"
      out <- data.frame(out, stringsAsFactors=TRUE)
      if (pred_sort) {
        o <- order(out[,n.vars+2])  # fitted value
        out <- out[o,]
      }

      .dash(68)
      if (n.keep < 25  || pred_all == TRUE || new.data)
        print(out, digits=digits_d)
      else {
        print(out[1:4,], digits=digits_d)
        cat("\n... for the rows of data where fitted is close to 0.5 ...\n\n")
        i.mid <- which.min(abs(0.5-sort(p.int$fit)))  # need out sorted by fit
        if (i.mid < 3) i.mid <- 3              # lower bound guard
        if (i.mid > n.keep - 2) i.mid <- n.keep - 2  # upper bound guard
        print(out[(i.mid-2):(i.mid+2),], digits=digits_d)
        cat("\n... for the last 4 rows of sorted data ...\n\n")
        print(out[(n.keep-3):n.keep,], digits=digits_d)
      }
      .dash(68)

      if (!new.data) {
        cat("\n\n")
        cat("----------------------------\n")
        cat("Specified confusion matrices\n")
        cat("----------------------------\n")
        cat("\n")
      }
    }  # end (i.cut == 1)  # pred output


    # confusion matrix, based on prob_cut directly
    if (i.cut > 1) cat("\n\n")
    if (!new.data) {
      if (n.vars == 2) {
        b0 <- lm.out$coefficients[1]
        b1 <- lm.out$coefficients[2]
        x.cut <- (log(prob_cut[i.cut] / (1-prob_cut[i.cut])) - b0) / b1
      }
      else
        x.cut <- NULL
      .logit5Confuse(lm.out, out, n.vars, nm, prob_cut[i.cut], x.cut)
    }
    else  # new.data
      cat("\n\nWith X1_new, etc., no confusion matrix.\n")

  }  # end (i.cut in 1:length(prob_cut))


  # graphics
  if (pred && n.pred==1 && !is.factor(lm.out$model[,nm[2]]) && is.null(X1_new)){

    .opendev(pdf_file, width, height)

    x.values <- lm.out$model[,nm[2]]

    if (!is.factor(lm.out$model[,nm[1]])) 
      lm.out$model[,nm[1]] <- factor(lm.out$model[,nm[1]], levels=0:1)

    y.values <- as.numeric(lm.out$model[,nm[1]])
    min.y <- min(y.values, na.rm=TRUE)
    if (is.null(xlab))
       x.lab <- nm[2]
    else
       x.lab <- xlab
    
    if (is.null(ylab))
       y.lab <- paste("Probability ", nm[1], " = ",
                      levels(lm.out$model[,nm[1]])[2], sep="")
    else
       y.lab <- ylab
    for (i in 1:length(y.values))
      y.values[i] <- ifelse (y.values[i]==min.y, 0, 1) 
 
    # set margins
    max.width <- strwidth(as.character(max(pretty(y.values))), units="inches")
    
    margs <- .plt.marg(max.width, y.lab=y.lab, x.lab=x.lab, main=NULL, sub=NULL)
    lm <- margs$lm+0.2;  tm <- margs$tm;  rm <- margs$rm;  bm <- margs$bm+0.1

    nc <- max(nchar(levels(lm.out$model[,nm[1]])))
    rm <- rm + .076*nc + 0.18

    orig.params <- par(no.readonly=TRUE)
    on.exit(par(orig.params))
    par(bg=getOption("window_fill"))
    par(mai=c(bm, lm, tm, rm))

    # plot
    plot(x.values,y.values, type="n", axes=FALSE, ann=FALSE,
         ylim=c(-.10,1.10), ...)
    usr <- par("usr")

    min.x <- min(x.values, na.rm=TRUE)
    max.x <- max(x.values, na.rm=TRUE)
    .plt.bck(usr, pretty(c(min.x, max.x)), seq(0,1,.2))
    .axes(NULL, NULL, axTicks(1), axTicks(2))

    # right axis, two values of response variable
    ax <- .axes_dim()
    axis(4, at=c(0,1), labels=FALSE,
         col=ax$axis_y_color, lwd=ax$axis_y_lwd, lty=ax$axis_y_lty) 
    text(x=usr[2]+0.07, y=c(0,1), labels=levels(lm.out$model[,nm[1]]),
         pos=4, xpd=TRUE, cex=ax$axis_y_cex, col=ax$axis_y_text_color)

    .axlabs(x.lab, y.lab, labels=FALSE, main.lab=NULL, sub.lab=NULL, 
            cex.lab=getOption("lab_cex"), cex.main=1.0, ...) 

    fill <- getOption("pt_fill")
    fill <- .maketrans(fill, (1-transparency)*256)
    color <- getOption("pt_color")
    if (n.vars == 2  &&  length(prob_cut) == 1) {
      pc <- prob_cut[1]
      segments(usr[1], pc, usr[2], pc, col="gray35", lty="dashed", lwd=0.5)  
      segments(x.cut, usr[3], x.cut, usr[4], col="gray35", lty="dashed", lwd=0.5)  
    }
    points(x.values, y.values, pch=21, col=color, bg=fill, cex=pt_size)
    ord <- order(x.values)
    lines(x.values[ord], p.int$fit[ord], col=color, lwd=2)

    if (!is.null(pdf_file)) {
      dev.off()
      .showfile(pdf_file, "fitted values and scatter plot")
    }
  }  # end sigmoid plot

  else {  # scatterplot matrix for multiple regression
    if (in.data.frame) {

      .opendev(pdf_file, width, height)

      panel2.smooth <- function (x, y, pch=par("pch"), cex=.9,
        col.pt=getOption("pt_color"), col.smooth=getOption("bar_color"),
        span=2/3, iter=3, ...) 
      {
          usr <- par("usr")          
          rect(usr[1], usr[3], usr[2], usr[4],
               col=getOption("border_fill"), border=getOption("border_color"))
          points(x, y, pch=pch, col=col.pt, bg=getOption("pt_fill"), cex=cex)

          ok <- is.finite(x) & is.finite(y)
          if (any(ok)) 
            lines(lowess(x[ok], y[ok], f=span, iter=iter), col=col.smooth, ...)
      }
      pairs(lm.out$model[c(nm)], panel=panel2.smooth)

      if (!is.null(pdf_file)) {
        dev.off()
        .showfile(pdf_file, "scatter plot matrix")
      }
    }
    else {
      cat("\n\n>>> No scatterplot matrix reported because not all variables are ")
      if (!in.data.frame) cat("in the data frame.\n")
    }
  }  # end scatterplot matrix

}
