ClusterMF-deprecated: Clustered MetaForest analysis for dependent data.

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

Description

This function conducts a clustered MetaForest analysis for dependent data. Using clustered random sampling, the dataset is split into two cross-validation samples by study. All dependent effect sizes from each study are thus included in the same cross-validation sample. Then, two random forests are grown on these cross-validation samples, and for each random forest, the other sample is used to calculate prediction error and variable importance (see Janitza, Celik, & Boulesteix, 2016). The predict.MetaForest method uses all trees from both forests.

Usage

ClusterMF(...)

Arguments

formula	Formula. Specify a formula for the MetaForest model, for example, yi ~ . to predict the outcome yi from all moderators in the data.
data	Data.frame. Provide a data.frame containing the effect size, moderators, and the variance of the effect size. Defaults to 100.
vi	Character. Specify the name of the column in the data that contains the variances of the effect sizes. This column will be removed from the data prior to analysis. Defaults to "vi".
study	Character. Specify the name of the column in the data that contains the study id. This column can be a vector of integers, or a factor. This column will be removed from the data prior to analysis.
whichweights	Character. Indicate what time of weights are required. A random-effects MetaForest is grown by specifying whichweights = "random". A fixed-effects MetaForest is grown by specifying whichweights = "fixed". An unweighted MetaForest is grown by specifying whichweights = "unif". Defaults to "random".
num.trees	Atomic integer. Specify the number of trees in the forest. Defaults to 500.
mtry	Atomic integer. Number of candidate moderators available for each split. Defaults to the square root of the number moderators (rounded down).
method	Character. Specify the method by which to estimate the residual variance. Can be set to one of the following: "DL", "HE", "SJ", "ML", "REML", "EB", "HS", or "GENQ". Default is "REML". See the metafor package for more information.
tau2	Numeric. Specify a predetermined value for the residual heterogeneity. Entering a value here supersedes the estimated tau2 value. Defaults to NULL.
...	Additional arguments are passed directly to ranger. It is recommended not to use additional arguments.

Value

List of length 3. The "forest" element of this list is an object of class "ranger", containing the results of the random forests analysis. The "rma_before" element is an object of class "rma.uni", containing the results of a random-effects meta-analysis on the raw data, without moderators. The "rma_after" element is an object of class "rma.uni", containing the results of a random-effects meta-analysis on the residual heterogeneity, or the difference between the effect sizes predicted by MetaForest and the observed effect sizes.

See Also

metaforest-deprecated

Examples

#Load and clean data from metafor
data <- get(data(dat.bourassa1996))
data <- escalc(measure = "OR", ai = lh.le, bi = lh.re, ci = rh.le, di= rh.re,
               data = data, add = 1/2, to = "all")
data$mage[is.na(data$mage)] <- median(data$mage, na.rm = TRUE)
data[c(5:8)] <- lapply(data[c(5:8)], factor)
data$yi <- as.numeric(data$yi)
mf.cluster.b1996 <- MetaForest(formula = yi~ selection + investigator +
                              hand_assess + eye_assess + mage +sex,
                              data, study = "sample",
                              whichweights = "unif", num.trees = 300)
#Print MetaForest object
mf.cluster.b1996
#Check convergence plot
plot(mf.cluster.b1996)
#Check summary
summary(mf.cluster.b1996, digits = 4)
#Check variable importance plot
VarImpPlot(mf.cluster.b1996)
#Univariate partial dependence plot
PartialDependence(mf.cluster.b1996, vars = "eye_assess")
#Interpolated partial dependence plot for a bivariate interaction

PartialDependence(mf.cluster.b1996, vars = c("mage", "eye_assess"), interaction = TRUE)

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