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####**********************************************************************
####
#### ----------------------------------------------------------------
#### Written by:
#### ----------------------------------------------------------------
#### John Ehrlinger, Ph.D.
####
#### email: john.ehrlinger@gmail.com
#### URL: https://github.com/ehrlinger/ggRandomForests
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####
####**********************************************************************
####**********************************************************************
#' Predicted response plot from a \code{\link{gg_rfsrc}} object.
#'
#' Plot the predicted response from a \code{\link{gg_rfsrc}} object, the
#' \code{\link[randomForestSRC]{rfsrc}} prediction, using the OOB prediction
#' from the forest.
#'
#' @param x \code{\link{gg_rfsrc}} object created from a
#' \code{\link[randomForestSRC]{rfsrc}} object
#' @param ... arguments passed to \code{\link{gg_rfsrc}}.
#'
#' @return \code{ggplot} object
#'
#' @seealso \code{\link{gg_rfsrc}} \code{\link[randomForestSRC]{rfsrc}}
#'
#' @references
#' Breiman L. (2001). Random forests, Machine Learning, 45:5-32.
#'
#' Ishwaran H. and Kogalur U.B. (2007). Random survival forests for
#' R, Rnews, 7(2):25-31.
#'
#' Ishwaran H. and Kogalur U.B. (2013). Random Forests for Survival, Regression
#' and Classification (RF-SRC), R package version 1.4.
#'
#' @examples
#' \dontrun{
#' ## ------------------------------------------------------------
#' ## classification example
#' ## ------------------------------------------------------------
#' ## -------- iris data
#' # rfsrc_iris <- rfsrc(Species ~ ., data = iris)
#' data(rfsrc_iris, package="ggRandomForests")
#' gg_dta<- gg_rfsrc(rfsrc_iris)
#'
#' plot(gg_dta)
#'
#' ## ------------------------------------------------------------
#' ## Regression example
#' ## ------------------------------------------------------------
#' ## -------- air quality data
#' rfsrc_airq <- rfsrc(Ozone ~ ., data = airquality, na.action = "na.impute")
#' gg_dta<- gg_rfsrc(rfsrc_airq)
#'
#' plot(gg_dta)
#'
#' ## -------- Boston data
#' data(Boston, package = "MASS")
#' rfsrc_boston <- randomForestSRC::rfsrc(medv~., Boston)
#'
#' plot(rfsrc_boston)
#'
#' ## -------- mtcars data
#' rfsrc_mtcars <- rfsrc(mpg ~ ., data = mtcars)
#' gg_dta<- gg_rfsrc(rfsrc_mtcars)
#'
#' plot(gg_dta)
#'
#' ## ------------------------------------------------------------
#' ## Survival example
#' ## ------------------------------------------------------------
#' ## -------- veteran data
#' ## randomized trial of two treatment regimens for lung cancer
#' data(veteran, package = "randomForestSRC")
#' rfsrc_veteran <- rfsrc(Surv(time, status) ~ ., data = veteran, ntree = 100)
#' gg_dta <- gg_rfsrc(rfsrc_veteran)
#' plot(gg_dta)
#'
#' gg_dta <- gg_rfsrc(rfsrc_veteran, conf.int=.95)
#' plot(gg_dta)
#'
#' gg_dta <- gg_rfsrc(rfsrc_veteran, by="trt")
#' plot(gg_dta)
#'
#' ## -------- pbc data
#' #' # We need to create this dataset
#' data(pbc, package = "randomForestSRC",)
#' # For whatever reason, the age variable is in days... makes no sense to me
#' for (ind in seq_len(dim(pbc)[2])) {
#' if (!is.factor(pbc[, ind])) {
#' if (length(unique(pbc[which(!is.na(pbc[, ind])), ind])) <= 2) {
#' if (sum(range(pbc[, ind], na.rm = TRUE) == c(0, 1)) == 2) {
#' pbc[, ind] <- as.logical(pbc[, ind])
#' }
#' }
#' } else {
#' if (length(unique(pbc[which(!is.na(pbc[, ind])), ind])) <= 2) {
#' if (sum(sort(unique(pbc[, ind])) == c(0, 1)) == 2) {
#' pbc[, ind] <- as.logical(pbc[, ind])
#' }
#' if (sum(sort(unique(pbc[, ind])) == c(FALSE, TRUE)) == 2) {
#' pbc[, ind] <- as.logical(pbc[, ind])
#' }
#' }
#' }
#' if (!is.logical(pbc[, ind]) &
#' length(unique(pbc[which(!is.na(pbc[, ind])), ind])) <= 5) {
#' pbc[, ind] <- factor(pbc[, ind])
#' }
#' }
#' #Convert age to years
#' pbc$age <- pbc$age / 364.24
#'
#' pbc$years <- pbc$days / 364.24
#' pbc <- pbc[, -which(colnames(pbc) == "days")]
#' pbc$treatment <- as.numeric(pbc$treatment)
#' pbc$treatment[which(pbc$treatment == 1)] <- "DPCA"
#' pbc$treatment[which(pbc$treatment == 2)] <- "placebo"
#' pbc$treatment <- factor(pbc$treatment)
#' dta_train <- pbc[-which(is.na(pbc$treatment)), ]
#' # Create a test set from the remaining patients
#' pbc_test <- pbc[which(is.na(pbc$treatment)), ]
#'
#' #========
#' # build the forest:
#' rfsrc_pbc <- randomForestSRC::rfsrc(
#' Surv(years, status) ~ .,
#' dta_train,
#' nsplit = 10,
#' na.action = "na.impute",
#' forest = TRUE,
#' importance = TRUE,
#' save.memory = TRUE
#' )
#'
#' gg_dta <- gg_rfsrc(rfsrc_pbc)
#' plot(gg_dta)
#'
#' gg_dta <- gg_rfsrc(rfsrc_pbc, conf.int=.95)
#' plot(gg_dta)
#'
#' gg_dta <- gg_rfsrc(rfsrc_pbc, by="treatment")
#' plot(gg_dta)
#'
#'
#' }
#' @importFrom ggplot2 ggplot aes_string geom_step geom_ribbon labs
#' geom_point geom_jitter geom_boxplot theme element_blank
#' @importFrom tidyr gather
#'
#' @export
plot.gg_rfsrc <- function(x, ...) {
gg_dta <- x
# Unpack argument list
arg_set <- list(...)
## rfsrc places the class in position 1.
if (inherits(gg_dta, "rfsrc"))
gg_dta <- gg_rfsrc(gg_dta)
## Classification forest?
if (inherits(gg_dta, "class") ||
inherits(gg_dta, "classification")) {
if (ncol(gg_dta) < 3) {
gg_plt <- ggplot(gg_dta) +
geom_jitter(aes_string(
x = 1,
y = colnames(gg_dta)[1],
color = colnames(gg_dta)[2],
shape = colnames(gg_dta)[2]
),
...) +
geom_boxplot(
aes_string(x = 1, y = colnames(gg_dta)[1]),
outlier.colour = "transparent",
fill = "transparent",
notch = TRUE,
...
) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank())
} else {
gathercols <- colnames(gg_dta)[-which(colnames(gg_dta) == "y")]
gg_dta_mlt <-
tidyr::gather(gg_dta, "variable", "value", gathercols)
gg_plt <-
ggplot(gg_dta_mlt, aes_string(x = "variable", y = "value")) +
geom_jitter(aes_string(color = "y", shape = "y"), alpha = .5)
}
gg_plt <- gg_plt + labs(y = "Predicted (%)", x = "")
} else if (inherits(gg_dta, "surv")) {
# Check for conf.int calculations
if ("lower" %in% colnames(gg_dta)) {
if (is.null(arg_set$alpha)) {
alph <- .3
} else {
alph <- arg_set$alpha * .5
arg_set$alpha <- NULL
}
if ("group" %in% colnames(gg_dta)) {
gg_plt <- ggplot(gg_dta) +
geom_ribbon(
aes_string(
x = "value",
ymin = "lower",
ymax = "upper",
fill = "group"
),
alpha = alph,
...
) +
geom_step(aes_string(
x = "value",
y = "median",
color = "group"
), ...)
} else {
gg_plt <- ggplot(gg_dta) +
geom_ribbon(aes_string(
x = "value",
ymin = "lower",
ymax = "upper"
),
alpha = alph) +
geom_step(aes_string(x = "value", y = "median"), ...)
}
} else {
# Lines by observation
gg_plt <- ggplot(gg_dta,
aes_string(
x = "variable",
y = "value",
col = "event",
by = "obs_id"
)) +
geom_step(...)
}
gg_plt <- gg_plt +
labs(x = "time (years)", y = "Survival (%)")
} else if (inherits(gg_dta, "regr") ||
inherits(gg_dta, "regression")) {
if ("group" %in% colnames(gg_dta)) {
gg_plt <- ggplot(gg_dta, aes_string(x = "group", y = "yhat"))
} else {
gg_plt <- ggplot(gg_dta, aes_string(x = 1, y = "yhat"))
}
gg_plt <- gg_plt +
geom_jitter(, ...) +
geom_boxplot(outlier.colour = "transparent",
fill = "transparent",
notch = TRUE,
...) +
labs(y = "Predicted Value", x = colnames(gg_dta)[2]) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank())
} else {
stop(paste(
"Plotting for ",
class(gg_dta)[2],
" randomForestSRC is not yet implemented."
))
}
return(gg_plt)
}
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