Description Usage Arguments Value Examples

MetaForest uses a weighted random forest to explore heterogeneity in
meta-analytic data. MetaForest is a wrapper for ranger
(Wright & Ziegler, 2015). As input, MetaForest takes the study effect sizes
and their variances (these can be computed, for example, using the
metafor package), as well as the moderators that are to be
included in the model. By default, MetaForest uses random-effects weights,
and estimates the between-studies variance using a restricted
maximum-likelihood estimator. However, it may be beneficial to first conduct
an unweighted MetaForest, and then use the estimated residual heterogeneity
from this model as the estimate of `tau2`

for a random-effects weighted
MetaForest.

1 2 |

`formula` |
Formula. Specify a formula for the MetaForest model, for
example, |

`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 |

`whichweights` |
Character. Indicate what time of weights are required.
A random-effects MetaForest is grown by specifying |

`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 about these estimators. |

`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. |

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ```
#Example 1:
#Simulate data with a univariate linear model
set.seed(42)
data <- SimulateSMD()
#Conduct unweighted MetaForest analysis
mf.unif <- MetaForest(formula = yi ~ ., data = data$training,
whichweights = "unif", method = "DL")
#Print model
mf.unif
#Conduct random-effects weighted MetaForest analysis
mf.random <- MetaForest(formula = yi ~ ., data = data$training,
whichweights = "random", method = "DL",
tau2 = 0.0116)
#Print summary
summary(mf.random)
#Example 2: Real data from metafor
#Load and clean data
data <- dat.bangertdrowns2004
data[, c(4:12)] <- apply(data[ , c(4:12)], 2, function(x){
x[is.na(x)] <- median(x, na.rm = TRUE)
x})
data$subject <- factor(data$subject)
data$yi <- as.numeric(data$yi)
#Conduct MetaForest analysis
mf.bd2004 <- MetaForest(formula = yi~ grade + length + minutes + wic+
meta, data, whichweights = "unif")
#Print MetaForest object
mf.bd2004
#Check convergence plot
plot(mf.bd2004)
#Check summary
summary(mf.bd2004, digits = 4)
#Examine variable importance plot
VarImpPlot(mf.bd2004)
``` |

metaforest documentation built on May 31, 2018, 9:03 a.m.

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