var_depth {DynForest}R Documentation

Extract characteristics from the trees building process

Description

Extract characteristics from the trees building process

Usage

var_depth(DynForest_obj)

Arguments

DynForest_obj

DynForest object

Value

var_depth function return a list with the following elements:

min_depth A table providing for each feature in row: the average depth and the rank
var_node_depth A table providing for each tree in column the minimal depth for each feature in row. NA indicates that the feature was not used for the corresponding tree
var_count A table providing for each tree in column the number of times where the feature is used (in row). 0 value indicates that the feature was not used for the corresponding tree

See Also

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)

# Run var_depth function
res_varDepth <- var_depth(res_dyn)




[Package DynForest version 1.1.0 Index]