getTree {DynForest}

Extract some information about the split for a tree by user

Description

Extract some information about the split for a tree by user

Usage

getTree(DynForest_obj, tree)

Arguments

DynForest_obj

DynForest object containing the dynamic random forest used on train data

tree

Integer indicating the tree identifier

Value

A table sorted by the node/leaf identifier with each row representing a node/leaf. Each column provides information about the splits:

type	The nature of the predictor (Longitudinal for longitudinal predictor, Numeric for continuous predictor or Factor for categorical predictor) if the node was split, Leaf otherwise
var_split	The predictor used for the split defined by its order in timeData and fixedData
feature	The feature used for the split defined by its position in random statistic
threshold	The threshold used for the split (only with Longitudinal and Numeric). No information is returned for Factor
N	The number of subjects in the node/leaf
Nevent	The number of events of interest in the node/leaf (only with survival outcome)
depth	the depth level of the node/leaf

See Also

DynForest summary.DynForest

Examples

data(pbc2)

# Get Gaussian distribution for longitudinal predictors
pbc2$serBilir <- log(pbc2$serBilir)
pbc2$SGOT <- log(pbc2$SGOT)
pbc2$albumin <- log(pbc2$albumin)
pbc2$alkaline <- log(pbc2$alkaline)

# Sample 100 subjects
set.seed(1234)
id <- unique(pbc2$id)
id_sample <- sample(id, 100)
id_row <- which(pbc2$id%in%id_sample)

pbc2_train <- pbc2[id_row,]

timeData_train <- pbc2_train[,c("id","time",
                                "serBilir","SGOT",
                                "albumin","alkaline")]

# Create object with longitudinal association for each predictor
timeVarModel <- list(serBilir = list(fixed = serBilir ~ time,
                                     random = ~ time),
                     SGOT = list(fixed = SGOT ~ time + I(time^2),
                                 random = ~ time + I(time^2)),
                     albumin = list(fixed = albumin ~ time,
                                    random = ~ time),
                     alkaline = list(fixed = alkaline ~ time,
                                     random = ~ time))

# Build fixed data
fixedData_train <- unique(pbc2_train[,c("id","age","drug","sex")])

# Build outcome data
Y <- list(type = "surv",
          Y = unique(pbc2_train[,c("id","years","event")]))

# Run DynForest function
res_dyn <- DynForest(timeData = timeData_train, fixedData = fixedData_train,
                     timeVar = "time", idVar = "id",
                     timeVarModel = timeVarModel, Y = Y,
                     ntree = 50, nodesize = 5, minsplit = 5,
                     cause = 2, ncores = 2, seed = 1234)

# Extract split information from tree 4
res_tree4 <- getTree(DynForest_obj = res_dyn, tree = 4)