# PartialDependence: Partial dependence plots Partial dependence plots ```r 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. ## Returns 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 ```r ## Not run: #' # 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) ## End(Not run) ```

PartialDependence function