predict.MetaForest: MetaForest prediction

In metaforest: Exploring Heterogeneity in Meta-Analysis using Random Forests

Description

MetaForest prediction

Usage

## S3 method for class 'MetaForest'
predict(object, data = NULL, type = "response",
  ...)

Arguments

object	MetaForest object.
data	New test data of class data.frame.
type	Type of prediction. One of 'response', 'se', 'terminalNodes' with default 'response'. See below for details.
...	further arguments passed to or from other methods.

Value

Object of class MetaForest.prediction with elements

predictions	Predicted classes/values (only for classification and regression)
num.trees	Number of trees.
num.independent.variables	Number of independent variables.
treetype	Type of forest/tree. Classification, regression or survival.
num.samples	Number of samples.

See Also

ranger

Examples

set.seed(56)
data <- SimulateSMD(k_train = 100, model = es * x[,1] * x[,2])
#Conduct fixed-effects MetaForest analysis
mf.fixed <- MetaForest(formula = yi ~ ., data = data$training,
                      whichweights = "fixed", method = "DL")
predicted <- predict(mf.fixed, data = data$testing)$predictions
r2_cv <- sum((predicted - mean(data$training$yi)) ^ 2)/
         sum((data$testing$yi - mean(data$training$yi)) ^ 2)

Example output

Loading required package: ggplot2
Loading required package: metafor
Loading required package: Matrix
Loading 'metafor' package (version 2.4-0). For an overview 
and introduction to the package please type: help(metafor).
Loading required package: ranger
Loading required package: data.table

metaforest documentation built on Jan. 8, 2020, 9:06 a.m.