combine.gg_partial: combine two gg_partial objects

In ggRandomForests: Visually Exploring Random Forests

combine two gg_partial objects

Description

The combine.gg_partial function assumes the two gg_partial objects were generated from the same rfsrc object. So, the function joins along the gg_partial list item names (one per partial plot variable). Further, we combine the two gg_partial objects along the group variable.

Hence, to join three gg_partial objects together (i.e. for three different time points from a survival random forest) would require two combine.gg_partial calls: One to join the first two gg_partial object, and one to append the third gg_partial object to the output from the first call. The second call will append a single lbls label to the gg_partial object.

Usage

combine.gg_partial(x, y, lbls, ...)

Arguments

x	gg_partial object
y	gg_partial object
lbls	vector of 2 strings to label the combined data.
...	not used

Value

gg_partial or gg_partial_list based on class of x and y.

Examples

## Not run: 
# 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
)
xvar <- c("bili", "albumin", "copper", "prothrombin", "age")
xvar_cat <- c("edema")
xvar <- c(xvar, xvar_cat)

time_index <- c(which(rfsrc_pbc$time.interest > 1)[1] - 1,  
                which(rfsrc_pbc$time.interest > 3)[1] - 1,  
                which(rfsrc_pbc$time.interest > 5)[1] - 1)
                
partial_pbc <- mclapply(rfsrc_pbc$time.interest[time_index],
                  function(tm) {
                    plot.variable(rfsrc_pbc, surv.type = "surv", 
                                       time = tm, xvar.names = xvar,
                                       partial = TRUE, 
                                       show.plots = FALSE)
                       })
                       
# A list of 2 plot.variable objects
length(partial_pbc)
class(partial_pbc)

class(partial_pbc[[1]])
class(partial_pbc[[2]])

# Create gg_partial objects
ggPrtl.1 <- gg_partial(partial_pbc[[1]])
ggPrtl.2 <- gg_partial(partial_pbc[[2]])

# Combine the objects to get multiple time curves
# along variables on a single figure.
ggpart <- combine.gg_partial(ggPrtl.1, ggPrtl.2,
                             lbls = c("1 year", "3 years"))

# Plot each figure separately
plot(ggpart)

# Get the continuous data for a panel of continuous plots.
ggcont <- ggpart
ggcont$edema <- ggcont$ascites <- ggcont$stage <- NULL
plot(ggcont, panel=TRUE)

# And the categorical for a panel of categorical plots.
nms <- colnames(sapply(ggcont, function(st) {st}))
for(ind in nms) {
   ggpart[[ind]] <- NULL
}
plot(ggpart, panel=TRUE)


## End(Not run)

ggRandomForests documentation built on Sept. 1, 2022, 5:07 p.m.