R/plot_fs_interaction_1D.R

In mfasiolo/mgcViz: Visualisations for Generalized Additive Models

#'
#' Plotting one dimensional smooth factor interactions
#' 
#' @description This method should be used to plot smooth effects 
#'              of class \code{"fs.interaction.1D"}, that is smooth constructed 
#'              using the basis \code{bs="tp"}. See [mgcv::s].
#' @param x a smooth effect object.
#' @param n number of grid points used to compute main effect and c.i. lines. 
#'          For a nice smooth plot this needs to be several times the estimated degrees of 
#'          freedom for the smooth.
#' @param xlim if supplied then this pair of numbers are used as the x limits for the plot.
#' @param trans monotonic function to apply to the smooth and residuals, before plotting.
#'              Monotonicity is not checked. 
#' @param ... currently unused.
#' @return An object of class \code{c("plotSmooth", "gg")}.
#' @name plot.fs.interaction.1D
#' @examples 
#' library(mgcViz)
#' set.seed(0)
#' ## simulate data...
#' f0 <- function(x) 2 * sin(pi * x)
#' f1 <- function(x, a = 2, b = -1) exp(a * x) + b
#' f2 <- function(x) 0.2 * x^11 * (10 * (1 - x))^6 + 10 *
#'   (10 * x)^3 * (1 - x)^10
#' n <- 500; nf <- 25
#' fac <- sample(1:nf, n, replace = TRUE)
#' x0 <- runif(n); x1 <- runif(n); x2 <- runif(n)
#' a <- rnorm(nf) * .2 + 2; b <- rnorm(nf) * .5
#' f <- f0(x0) + f1(x1, a[fac], b[fac]) + f2(x2)
#' fac <- factor(fac)
#' y <- f + rnorm(n) * 2
#' ## so response depends on global smooths of x0 and
#' ## x2, and a smooth of x1 for each level of fac.
#' 
#' ## fit model (note p-values not available when fit
#' ## using gamm)...
#' bm <- gamm(y ~ s(x0)+ s(x1, fac, bs = "fs", k = 5) + s(x2, k = 20))
#' v <- getViz(bm$gam)
#' 
#' # Plot with fitted effects and changing title 
#' plot(sm(v, 2)) + l_fitLine(alpha = 0.6) + labs(title = "Smooth factor interactions")
#' 
#' # Changing plotting limits
#' plot(sm(v, 2)) + l_fitLine() + ylim(-0.5, 0.5) + xlim(0.25, 0.75)
#' 
#' # Change line type and remove legend
#' plot(sm(v, 2)) + l_fitLine(size = 1.3, linetype="dotted") + 
#'                  theme(legend.position="none")
#'                  
#' # Clustering smooth effects in 3 groups
#' plot(sm(v, 2)) + l_fitLine(colour = "grey") + 
#'                  l_clusterLine(centers = 3, a.clu = list(nstart = 100))
#' @importFrom mgcv PredictMat
#' @rdname plot.fs.interaction.1D
#' @export plot.fs.interaction.1D
#' @export
#' 
plot.fs.interaction.1D <- function(x, n = 100, xlim = NULL, trans = identity, ...) {
  
  # 1) Prepare data
  P <- .prepareP(o = x, unconditional = FALSE, residuals = FALSE, 
                 resDen = "none", se = TRUE, se.mult = 1, n = n, n2 = NULL,  
                 xlab = NULL, ylab = NULL, main = NULL, ylim = NULL, xlim = xlim,
                 too.far = NULL, seWithMean = FALSE)
  
  # 2) Produce output object
  out <- .plot.fs.interaction.1D(x = P$smooth, P = P, trans = trans)

  class(out) <- c("plotSmooth", "gg")
  
  return(out)
}

########################
#' @noRd
.plot.fs.interaction.1D <- function(x, P, trans) {
  
  .dat <- list()
  # 1) Build dataset on fitted effect
  .dat$fit <- data.frame("x"  = rep(P$x, P$nf),
                         "y"  = P$fit,
                         "ty"  = trans(P$fit),
                         "id" = as.factor(rep(x$flev, each = P$n)))
  .dat$misc <- list("trans" = trans)
  
  .pl <- ggplot(data = .dat$fit, aes("x" = x, "y" = ty, "colour" = id, "group" = id)) +
    labs(title = P$main, x = P$xlab, y = P$ylab) + 
    theme_bw() +
    theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
  
  return( list("ggObj" = .pl, "data" = .dat, "type" = c("fs", "1D")) ) 
  
} ## end .plot.fs.interaction.1D