#  File src/library/stats/R/termplot.R
#  Part of the R package, https://www.R-project.org
#
#  Copyright (C) 1995-2019 The R Core Team
#
#  This program is free software; you can redistribute it and/or modify
#  it under the terms of the GNU General Public License as published by
#  the Free Software Foundation; either version 2 of the License, or
#  (at your option) any later version.
#
#  This program is distributed in the hope that it will be useful,
#  but WITHOUT ANY WARRANTY; without even the implied warranty of
#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#  GNU General Public License for more details.
#
#  A copy of the GNU General Public License is available at
#  https://www.R-project.org/Licenses/

termplot <- function(model, data = NULL, envir = environment(formula(model)),
                     partial.resid = FALSE,
		     rug = FALSE, terms = NULL, se = FALSE,
                     xlabs = NULL, ylabs = NULL,
                     main = NULL, col.term = 2, lwd.term = 1.5,
                     col.se = "orange", lty.se = 2, lwd.se = 1,
                     col.res = "gray", cex = 1, pch = par("pch"),
                     col.smth = "darkred", lty.smth = 2, span.smth = 2/3,
                     ask = dev.interactive() && nb.fig < n.tms,
                     use.factor.levels = TRUE, smooth = NULL,
                     ylim = "common", plot = TRUE, transform.x = FALSE, ...)
{
    which.terms <- terms
    terms <- ## need if(), since predict.coxph() has non-NULL default terms :
	if (is.null(terms))
	    predict(model, type = "terms", se.fit = se)
	else
	    predict(model, type = "terms", se.fit = se, terms = terms)
    n.tms <- ncol(tms <- as.matrix(if(se) terms$fit else terms))
    transform.x <- rep_len(transform.x, n.tms)

    mf <- model.frame(model)
    if (is.null(data))
        data <- eval(model$call$data, envir)
    if (is.null(data))
        data <- mf
    ## maybe rather use naresid() as for factor variables.
    use.rows <- if (NROW(tms) < NROW(data))
        match(rownames(tms), rownames(data)) ## else NULL
    nmt <- colnames(tms)
    if (any(grepl(":", nmt, fixed = TRUE)))
        warning("'model' appears to involve interactions: see the help page",
                domain = NA, immediate. = TRUE)
    cn <- str2expression(nmt)
    pf <- envir
    carrier <- function(term, transform) { # used for non-factor ones
	if (length(term) > 1L){
	    if (transform) tms[,i]
	    else carrier(term[[2L]], transform)
	} else
	    eval(term, data, enclos = pf)
    }
    carrier.name <- function(term){
      	if (length(term) > 1L)
	    carrier.name(term[[2L]])
	else
	    as.character(term)
    }

    in.mf <- nmt %in% names(mf)
    is.fac <- vapply(nmt,
                     function(i) i %in% names(mf) && is.factor(mf[, i]),
                     NA)

    if (!plot) {
        outlist <- vector("list", sum(in.mf))
        for (i in 1L:n.tms) {
            if (!in.mf[i]) next
            ## add element to output list
            ## ww = index to rows in the data, selecting one of each unique
            ##        predictor value
            if (is.fac[i]) {
                ## PR#15344
                xx <- mf[, nmt[i]]
                if (!is.null(use.rows)) xx <- xx[use.rows]
                ## "nomatch' in case there is a level not in the data
                ww <- match(levels(xx), xx, nomatch = 0L)
            }
            else {
                xx <- carrier(cn[[i]], transform.x[i])
                if (!is.null(use.rows)) xx <- xx[use.rows]
                ww <- match(sort(unique(xx)), xx)
            }
            outlist[[i]] <- if (se)
                data.frame(x = xx[ww], y = tms[ww, i],
                           se = terms$se.fit[ww, i], row.names = NULL)
            else data.frame(x = xx[ww], y = tms[ww, i], row.names = NULL)
        }
        attr(outlist, "constant") <- attr(terms, "constant")
        ## might be on the fit component.
        if (se && is.null(attr(outlist, "constant")))
            attr(outlist, "constant") <- attr(terms$fit, "constant")
        names(outlist) <- sapply(cn, carrier.name)[in.mf]
        return(outlist)
    }

    ## Defaults:
    if (!is.null(smooth))
        smooth <- match.fun(smooth)
    if (is.null(ylabs))
	ylabs <- paste("Partial for", nmt)
    if (is.null(main))
        main <- ""
    else if(is.logical(main))
        main <- if(main) deparse(model$call, 500) else ""
    else if(!is.character(main))
        stop("'main' must be TRUE, FALSE, NULL or character (vector).")
    main <- rep_len(main, n.tms) # recycling

    if (is.null(xlabs)){
        xlabs <- unlist(lapply(cn,carrier.name))
	if(any(transform.x)) xlabs <- ifelse(transform.x, lapply(cn, deparse), xlabs)
    }
    if (partial.resid || !is.null(smooth)){
	pres <- residuals(model, "partial")
        if (!is.null(which.terms)) pres <- pres[, which.terms, drop = FALSE]
    }

    se.lines <- function(x, iy, i, ff = 2) {
        tt <- ff * terms$se.fit[iy, i]
        lines(x, tms[iy, i] + tt, lty = lty.se, lwd = lwd.se, col = col.se)
        lines(x, tms[iy, i] - tt, lty = lty.se, lwd = lwd.se, col = col.se)
    }

    nb.fig <- prod(par("mfcol"))
    if (ask) {
	oask <- devAskNewPage(TRUE)
	on.exit(devAskNewPage(oask))
    }

    ylims <- ylim
    if(identical(ylims, "common")) {
        ylims <- if(!se) range(tms, na.rm = TRUE)
        else range(tms + 1.05*2*terms$se.fit,
                   tms - 1.05*2*terms$se.fit,
                   na.rm = TRUE)
        if (partial.resid) ylims <- range(ylims, pres, na.rm = TRUE)
        if (rug) ylims[1L] <- ylims[1L] - 0.07*diff(ylims)
    }

    ##---------- Do the individual plots : ----------

    for (i in 1L:n.tms) {
        if(identical(ylim, "free")) {
            ylims <- range(tms[, i], na.rm = TRUE)
            if (se)
                ylims <- range(ylims,
                               tms[, i] + 1.05*2*terms$se.fit[, i],
                               tms[, i] - 1.05*2*terms$se.fit[, i],
                               na.rm = TRUE)
            if (partial.resid)
                ylims <- range(ylims, pres[, i], na.rm = TRUE)
            if (rug)
                ylims[1L] <- ylims[1L] - 0.07*diff(ylims)
        }
        if (!in.mf[i]) next
	if (is.fac[i]) {
	    ff <- mf[, nmt[i]]
            if (!is.null(model$na.action))
                ff <- naresid(model$na.action, ff)
	    ll <- levels(ff)
	    xlims <- range(seq_along(ll)) + c(-.5, .5)
            xx <- as.numeric(ff) ## needed if rug or partial
	    if(rug) {
		xlims[1L] <- xlims[1L] - 0.07*diff(xlims)
		xlims[2L] <- xlims[2L] + 0.03*diff(xlims)
	    }
	    plot(1, 0, type = "n", xlab = xlabs[i], ylab = ylabs[i],
                 xlim = xlims, ylim = ylims, main = main[i], xaxt="n", ...)
            if (use.factor.levels)
                axis(1, at = seq_along(ll), labels = ll, ...)
            else
                axis(1)
	    for(j in seq_along(ll)) {
		ww <- which(ff == ll[j])[c(1, 1)]
		jf <- j + c(-0.4, 0.4)
		lines(jf, tms[ww, i], col = col.term, lwd = lwd.term, ...)
		if(se) se.lines(jf, iy = ww, i = i)
	    }
	}
	else { ## continuous carrier
	    xx <- carrier(cn[[i]], transform.x[i])
            if (!is.null(use.rows)) xx <- xx[use.rows]
	    xlims <- range(xx, na.rm = TRUE)
	    if(rug)
		xlims[1L] <- xlims[1L] - 0.07*diff(xlims)
	    oo <- order(xx)
	    plot(xx[oo], tms[oo, i], type = "l",
                 xlab = xlabs[i], ylab = ylabs[i],
		 xlim = xlims, ylim = ylims, main = main[i],
                 col = col.term, lwd = lwd.term, ...)
            if(se) se.lines(xx[oo], iy = oo, i = i)
	}
	if (partial.resid){
          if (!is.fac[i] && !is.null(smooth)){
            smooth(xx,pres[, i], lty = lty.smth,
                   cex = cex, pch = pch, col = col.res,
                   col.smooth = col.smth, span = span.smth)
          }
          else
              points(xx, pres[, i], cex = cex, pch = pch, col = col.res)
        }
	if (rug) {
            n <- length(xx)
            ## Fixme: Isn't this a kludge for segments() ?
	    lines(rep.int(jitter(xx), rep.int(3, n)),
                  rep.int(ylims[1L] + c(0, 0.05, NA)*diff(ylims), n))
	    if (partial.resid)
		lines(rep.int(xlims[1L] + c(0, 0.05, NA)*diff(xlims), n),
                      rep.int(pres[, i], rep.int(3, n)))
	}
    }
    invisible(n.tms)
}
