R/stat_smooth_func.r

In surh/wheelP: Designing synthetic bacterial communities for predictable plant phenotypes

#' Stat_smooth for coef of determination
#'
#' https://gist.github.com/kdauria/524eade46135f6348140
#'
#' @export
stat_smooth_func <- function(mapping = NULL, data = NULL,
                             geom = "smooth", position = "identity",
                             ...,
                             method = "auto",
                             formula = y ~ x,
                             se = TRUE,
                             n = 80,
                             span = 0.75,
                             fullrange = FALSE,
                             level = 0.95,
                             method.args = list(),
                             na.rm = FALSE,
                             show.legend = NA,
                             inherit.aes = TRUE,
                             xpos = NULL,
                             ypos = NULL) {
  layer(
    data = data,
    mapping = mapping,
    stat = StatSmoothFunc,
    geom = geom,
    position = position,
    show.legend = show.legend,
    inherit.aes = inherit.aes,
    params = list(
      method = method,
      formula = formula,
      se = se,
      n = n,
      fullrange = fullrange,
      level = level,
      na.rm = na.rm,
      method.args = method.args,
      span = span,
      xpos = xpos,
      ypos = ypos,
      ...
    )
  )
}


StatSmoothFunc <- ggplot2::ggproto("StatSmooth", ggplot2::Stat,

                          setup_params = function(data, params) {
                            # Figure out what type of smoothing to do: loess for small datasets,
                            # gam with a cubic regression basis for large data
                            # This is based on the size of the _largest_ group.
                            if (identical(params$method, "auto")) {
                              max_group <- max(table(data$group))

                              if (max_group < 1000) {
                                params$method <- "loess"
                              } else {
                                params$method <- "gam"
                                params$formula <- y ~ s(x, bs = "cs")
                              }
                            }
                            if (identical(params$method, "gam")) {
                              params$method <- mgcv::gam
                            }

                            params
                          },

                          compute_group = function(data, scales, method = "auto", formula = y~x,
                                                   se = TRUE, n = 80, span = 0.75, fullrange = FALSE,
                                                   xseq = NULL, level = 0.95, method.args = list(),
                                                   na.rm = FALSE, xpos=NULL, ypos=NULL) {
                            if (length(unique(data$x)) < 2) {
                              # Not enough data to perform fit
                              return(data.frame())
                            }

                            if (is.null(data$weight)) data$weight <- 1

                            if (is.null(xseq)) {
                              if (is.integer(data$x)) {
                                if (fullrange) {
                                  xseq <- scales$x$dimension()
                                } else {
                                  xseq <- sort(unique(data$x))
                                }
                              } else {
                                if (fullrange) {
                                  range <- scales$x$dimension()
                                } else {
                                  range <- range(data$x, na.rm = TRUE)
                                }
                                xseq <- seq(range[1], range[2], length.out = n)
                              }
                            }
                            # Special case span because it's the most commonly used model argument
                            if (identical(method, "loess")) {
                              method.args$span <- span
                            }

                            if (is.character(method)) method <- match.fun(method)

                            base.args <- list(quote(formula), data = quote(data), weights = quote(weight))
                            model <- do.call(method, c(base.args, method.args))

                            m = model
                            eq <- substitute(italic(y) == a + b %.% italic(x)*","~~italic(r)^2~"="~r2,
                                             list(a = format(coef(m)[1], digits = 3),
                                                  b = format(coef(m)[2], digits = 3),
                                                  r2 = format(summary(m)$r.squared, digits = 3)))
                            func_string = as.character(as.expression(eq))

                            if(is.null(xpos)) xpos = min(data$x)*0.9
                            if(is.null(ypos)) ypos = max(data$y)*0.9
                            data.frame(x=xpos, y=ypos, label=func_string)

                          },

                          required_aes = c("x", "y")
)