PartialDependence: Partial dependence plots

Partial dependence plots

PartialDependence(
  x,
  vars = NULL,
  pi = NULL,
  rawdata = FALSE,
  bw = FALSE,
  resolution = NULL,
  moderator = NULL,
  mod_levels = NULL,
  output = "plot",
  ...
)

Arguments

x

Model object.

vars

Character vector containing the moderator names for which to plot partial dependence plots. If empty, all moderators are plotted.

pi

Numeric (0-1). What percentile interval should be plotted for the partial dependence predictions? Defaults to NULL. To obtain a 95% interval, set to .95.

rawdata

Logical, indicating whether to plot weighted raw data. Defaults to FALSE. Uses the same weights as the model object passed to the x argument.

bw

Logical, indicating whether the plot should be black and white, or color.

resolution

Integer vector of length two, giving the resolution of the partial predictions. The first element indicates the resolution of the partial predictions; for Monte-Carlo integration, the second element gives the number of rows of the data to be sampled without replacement when averaging over values of the other predictors.

moderator

Atomic character vector, referencing the name of one variable in the model. Results in partial prediction plots, conditional on the moderator. If moderator references a factor variable, separate lines/boxplots are plotted for each factor level. If moderator references a numeric variable, heatmaps are plotted - unless the moderator is categorized using the mod_levels argument.

mod_levels

Vector. If moderator is continuous, specify thresholds for the cut function. The continuous moderator is categorized, and predictions are based on the median moderator value within each category. You can call quantile to cut the moderator at specific quantiles. If moderator is a factor variable, you can use mod_levels to specify a character vector with the factor levels to retain in the plot (i.e., dropping the other factor levels).

output

Character. What type of output should be returned? Defaults to "plot", which returns and plots a gtable object. To obtain a list of ggplot objects instead, provide the argument "list".

...

Additional arguments to be passed to and from functions.

Value

A gtable object.

Details

Plots partial dependence plots (predicted effect size as a function of the value of each predictor variable) for a MetaForest- or rma model object. For rma models, it is advisable to mean-center numeric predictors, and to not include plot_int effects, except when the rma model is bivariate, and the plot_int argument is set to TRUE.

Examples

if (FALSE) {
#' # Partial dependence plot for MetaForest() model:
set.seed(42)
data <- SimulateSMD(k_train = 200, model = es * x[, 1] + es * x[, 2] + es *
                                           x[, 1] * x[, 2])$training
data$X2 <- cut(data$X2, breaks = 2, labels = c("Low", "High"))
mf.random <- MetaForest(formula = yi ~ ., data = data,
                        whichweights = "random", method = "DL",
                        tau2 = 0.2450)
# Examine univariate partial dependence plot for all variables in the model:
PartialDependence(mf.random, pi = .8)
# Examine bivariate partial dependence plot the plot_int between X1 and X2:
pd.plot <- PartialDependence(mf.random, vars = c("X1", "X2"), plot_int = TRUE)
# Save to pdf file
pdf("pd_plot.pdf")
grid.draw(pd.plot)
dev.off()
# Partial dependence plot for metafor rma() model:
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)
dat$yi <- as.numeric(dat$yi)
dat$alloc <- factor(dat$alloc)
dat$ablat_d <- cut(dat$ablat, breaks = 2, labels = c("low", "high"))
# Demonstrate partial dependence plot for a bivariate plot_int
rma.model.int <- rma(yi, vi, mods=cbind(ablat, tpos),
                     data=dat, method="REML")
PartialDependence(rma.model.int, rawdata = TRUE, pi = .95,
                  plot_int = TRUE)

# Compare partial dependence for metaforest and rma
dat2 <- dat
dat2[3:7] <- lapply(dat2[3:7],
                    function(x){as.numeric(scale(x, scale = FALSE))})
mf.model.all <- MetaForest(yi ~ ., dat2[, c(3:11)])
rma.model.all <- rma(dat$yi, dat2$vi,
                  mods = model.matrix(yi~., dat2[, c(3:10)])[, -1],
                  method="REML")
PartialDependence(mf.model.all, rawdata = TRUE, pi = .95)
PartialDependence(rma.model.all, rawdata = TRUE, pi = .95)
}