combine two gg_partial objects

# Load a set of plot.variable partial plot data
data(partial_pbc)

# A list of 2 plot.variable objects
length(partial_pbc)

## [1] 3

class(partial_pbc)

## [1] "list"

class(partial_pbc[[1]])

## [1] "rfsrc"         "plot.variable" "surv"

class(partial_pbc[[2]])

## [1] "rfsrc"         "plot.variable" "surv"

# 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)

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

## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.

# 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)