gg_variable: Marginal variable dependence data object.

Description

plot.variable generates a data.frame containing the marginal variable dependence or the partial variable dependence. The gg_variable function creates a data.frame of containing the full set of covariate data (predictor variables) and the predicted response for each observation. Marginal dependence figures are created using the plot.gg_variable function.

Optional arguments time point (or vector of points) of interest (for survival forests only) time_labels If more than one time is specified, a vector of time labels for differentiating the time points (for survival forests only) oob indicate if predicted results should include oob or full data set.

Usage

gg_variable(object, ...)

Value

gg_variable object

Arguments

object: a rfsrc object
...: optional arguments

Details

The marginal variable dependence is determined by comparing relation between the predicted response from the randomForest and a covariate of interest.

The gg_variable function operates on a rfsrc object, or the output from the plot.variable function.

Examples

Run this code

## ------------------------------------------------------------
## classification
## ------------------------------------------------------------
## -------- iris data
## iris
rfsrc_iris <- rfsrc(Species ~., data = iris)

gg_dta <- gg_variable(rfsrc_iris)
plot(gg_dta, xvar="Sepal.Width")
plot(gg_dta, xvar="Sepal.Length")

plot(gg_dta, xvar=rfsrc_iris$xvar.names,
     panel=TRUE) # , se=FALSE)

## ------------------------------------------------------------
## regression
## ------------------------------------------------------------
if (FALSE) {
## -------- air quality data
rfsrc_airq <- rfsrc(Ozone ~ ., data = airquality)
gg_dta <- gg_variable(rfsrc_airq)

# an ordinal variable
gg_dta[,"Month"] <- factor(gg_dta[,"Month"])

plot(gg_dta, xvar="Wind")
plot(gg_dta, xvar="Temp")
plot(gg_dta, xvar="Solar.R")


plot(gg_dta, xvar=c("Solar.R", "Wind", "Temp", "Day"), panel=TRUE)

plot(gg_dta, xvar="Month", notch=TRUE)
}
if (FALSE) {
## -------- motor trend cars data
rfsrc_mtcars <- rfsrc(mpg ~ ., data = mtcars)

gg_dta <- gg_variable(rfsrc_mtcars)

# mtcars$cyl is an ordinal variable
gg_dta$cyl <- factor(gg_dta$cyl)
gg_dta$am <- factor(gg_dta$am)
gg_dta$vs <- factor(gg_dta$vs)
gg_dta$gear <- factor(gg_dta$gear)
gg_dta$carb <- factor(gg_dta$carb)

plot(gg_dta, xvar="cyl")

# Others are continuous
plot(gg_dta, xvar="disp")
plot(gg_dta, xvar="hp")
plot(gg_dta, xvar="wt")

# panels
plot(gg_dta,xvar=c("disp","hp", "drat", "wt", "qsec"),  panel=TRUE)
plot(gg_dta, xvar=c("cyl", "vs", "am", "gear", "carb"), panel=TRUE,
     notch=TRUE)
}
## -------- Boston data
data(Boston, package="MASS")

rf_boston <- randomForest::randomForest(medv~., data=Boston)
gg_dta <- gg_variable(rf_boston)
plot(gg_dta)
plot(gg_dta, panel = TRUE)
## ------------------------------------------------------------
## survival examples
## ------------------------------------------------------------
if (FALSE) {
## -------- veteran data
## survival
data(veteran, package = "randomForestSRC")
rfsrc_veteran <- rfsrc(Surv(time,status)~., veteran, nsplit = 10,
                        ntree = 100)
                        
# get the 1 year survival time.
gg_dta <- gg_variable(rfsrc_veteran, time=90)

# Generate variable dependence plots for age and diagtime
plot(gg_dta, xvar = "age")
plot(gg_dta, xvar = "diagtime", )

# Generate coplots
plot(gg_dta, xvar = c("age", "diagtime"), panel=TRUE, se=FALSE)

# If we want to compare survival at different time points, say 30, 90 day
# and 1 year
gg_dta <- gg_variable(rfsrc_veteran, time=c(30, 90, 365))

# Generate variable dependence plots for age and diagtime
plot(gg_dta, xvar = "age")
}
if (FALSE) {
## -------- pbc data
## We don't run this because of bootstrap confidence limits
# 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_variable(rfsrc_pbc, time=c(.5, 1, 3))
plot(gg_dta, xvar = "age")
plot(gg_dta, xvar = "trig")

# Generate coplots
plot(gg_dta, xvar = c("age", "trig"), panel=TRUE, se=FALSE)

}