gg_partial: Partial variable dependence object

In ehrlinger/ggRandomForests: Visually Exploring Random Forests

Partial variable dependence object

Description

The plot.variable function returns a list of either marginal variable dependence or partial variable dependence data from a rfsrc object. The gg_partial function formulates the plot.variable output for partial plots (where partial=TRUE) into a data object for creation of partial dependence plots using the plot.gg_partial function.

Partial variable dependence plots are the risk adjusted estimates of the specified response as a function of a single covariate, possibly subsetted on other covariates.

An option named argument can name a column for merging multiple plots together

Usage

gg_partial(object, ...)

Arguments

object	the partial variable dependence data object from plot.variable function
...	optional arguments

Value

gg_partial object. A data.frame or list of data.frames corresponding the variables contained within the plot.variable output.

References

Friedman, Jerome H. 2000. "Greedy Function Approximation: A Gradient Boosting Machine." Annals of Statistics 29: 1189-1232."

See Also

plot.gg_partial

plot.variable

Examples

## ------------------------------------------------------------
## classification
## ------------------------------------------------------------
## -------- iris data
## iris "Petal.Width" partial dependence plot
##
rfsrc_iris <- rfsrc(Species ~., data = iris)
partial_iris <- plot.variable(rfsrc_iris, 
                              xvar.names = "Petal.Width",
                              partial=TRUE)

gg_dta <- gg_partial(partial_iris)
plot(gg_dta)

## ------------------------------------------------------------
## regression
## ------------------------------------------------------------
## Not run: 
## -------- air quality data
## airquality "Wind" partial dependence plot
##
rfsrc_airq <- rfsrc(Ozone ~ ., data = airquality)
partial_airq <- plot.variable(rfsrc_airq, 
                              xvar.names = "Wind",
                             partial=TRUE, show.plot=FALSE)

gg_dta <- gg_partial(partial_airq)
plot(gg_dta)


## End(Not run)
## Not run: 
## -------- Boston data
data(Boston, package = "MASS")
Boston$chas <- as.logical(Boston$chas)
rfsrc_boston <- rfsrc(medv ~ .,
   data = Boston,
   forest = TRUE,
   importance = TRUE,
   tree.err = TRUE,
   save.memory = TRUE)
   
varsel_boston <- var.select(rfsrc_boston)

partial_boston <- plot.variable(rfsrc_boston, 
  xvar.names = varsel_boston$topvars,
  sorted = FALSE,
  partial = TRUE, 
  show.plots = FALSE)
gg_dta <- gg_partial(partial_boston)
plot(gg_dta, panel=TRUE)

## End(Not run)
## Not run: 
## -------- mtcars data
rfsrc_mtcars <- rfsrc(mpg ~ ., data = mtcars)
varsel_mtcars <- var.select(rfsrc_mtcars)

partial_mtcars <- plot.variable(rfsrc_mtcars, 
  xvar.names = varsel_mtcars$topvars,
  sorted = FALSE,
  partial = TRUE, 
  show.plots = FALSE)

gg_dta <- gg_partial(partial_mtcars)

gg_dta.cat <- gg_dta
gg_dta.cat[["disp"]] <- gg_dta.cat[["wt"]] <- gg_dta.cat[["hp"]] <- NULL
gg_dta.cat[["drat"]] <- gg_dta.cat[["carb"]] <- gg_dta.cat[["qsec"]] <- NULL

plot(gg_dta.cat, panel=TRUE, notch=TRUE)

gg_dta[["cyl"]] <- gg_dta[["vs"]] <- gg_dta[["am"]] <- NULL
gg_dta[["gear"]] <- NULL
plot(gg_dta, panel=TRUE)

## End(Not run)

## ------------------------------------------------------------
## survival examples
## ------------------------------------------------------------
## Not run: 
## -------- veteran data
## survival "age" partial variable dependence plot
##
 data(veteran, package = "randomForestSRC")
 rfsrc_veteran <- rfsrc(Surv(time,status)~., veteran, nsplit = 10,
     ntree = 100)

varsel_rfsrc <- var.select(rfsrc_veteran)

## 30 day partial plot for age
partial_veteran <- plot.variable(rfsrc_veteran, surv.type = "surv",
                               partial = TRUE, time=30,
                               show.plots=FALSE)

gg_dta <- gg_partial(partial_veteran)
plot(gg_dta, panel=TRUE)

gg_dta.cat <- gg_dta
gg_dta[["celltype"]] <- gg_dta[["trt"]] <- gg_dta[["prior"]] <- NULL
plot(gg_dta, panel=TRUE)

gg_dta.cat[["karno"]] <- gg_dta.cat[["diagtime"]] <-
    gg_dta.cat[["age"]] <- NULL
plot(gg_dta.cat, panel=TRUE, notch=TRUE)

gg_dta <- lapply(partial_veteran, gg_partial)
gg_dta <- combine.gg_partial(gg_dta[[1]], gg_dta[[2]] )

plot(gg_dta[["karno"]])
plot(gg_dta[["celltype"]])

gg_dta.cat <- gg_dta
gg_dta[["celltype"]] <- gg_dta[["trt"]] <- gg_dta[["prior"]] <- NULL
plot(gg_dta, panel=TRUE)

gg_dta.cat[["karno"]] <- gg_dta.cat[["diagtime"]] <-
    gg_dta.cat[["age"]] <- NULL
plot(gg_dta.cat, panel=TRUE, notch=TRUE)

## ------------------------------------------------------------
## -------- 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
)

varsel_pbc <- var.select(rfsrc_pbc)

xvar <- varsel_pbc$topvars

# Convert all partial plots to gg_partial objects
gg_dta <- lapply(partial_pbc, gg_partial)

# Combine the objects to get multiple time curves
# along variables on a single figure.
pbc_ggpart <- combine.gg_partial(gg_dta[[1]], gg_dta[[2]],
                                 lbls = c("1 Year", "3 Years"))

summary(pbc_ggpart)
class(pbc_ggpart[["bili"]])

# Plot the highest ranked variable, by name.
#plot(pbc_ggpart[["bili"]])

# Create a temporary holder and remove the stage and edema data
ggpart <- pbc_ggpart
ggpart$edema <- NULL

# Panel plot the remainder.
plot(ggpart, panel = TRUE)

plot(pbc_ggpart[["edema"]])

## End(Not run)

ehrlinger/ggRandomForests documentation built on Sept. 9, 2022, 6:55 p.m.