perf.metboost: #' Marginal plots for metboost objects #' #' The fitted...
In patr1ckm/mvtboost: Tree Boosting for Multivariate Outcomes

#' Marginal plots for metboost objects #' #' The fitted values are plotted against one or two predictors. Note that #' that this is not a partial dependence plot. #' #' @param x metboost object #' @param X matrix of predictors #' @param id name or index of grouping variable #' @param i.var index or names of variables to plot over (can include id index) #' @param n.trees nubmer of trees (default min(x$best.trees)) #' @param ... unused #' @export #' @importFrom ggplot2 ggplot geom_line geom_point aes_string xlab ylab geom_tile facet_wrap plot.metboost <- function(x, X, id, i.var, n.trees=min(x$best.trees), ...)

if(all(is.character(i.var))) i.var <- match(i.var, colnames(X)) if(is.character(id)) id <- match(id, colnames(X))

# use yhat from x at n.trees? yhat <- x$yhat[, n.trees] Xnew <- X[, i.var] var.names <- colnames(Xnew)[i.var] d <- data.frame(y=yhat, Xnew) f.factor <- sapply(Xnew, is.factor)

if(length(i.var) == 1) g <- ggplot(d, aes_string(y="y", x=var.names)) + geom_point() + geom_line() else if(length(i.var) == 2) if(!f.factor[1] && !f.factor[2]) g <- ggplot(d, aes_string(x=var.names[1], y=var.names[2], z="y")) + geom_tile(aes_string(fill="y")) + xlab(var.names[i.var[1]]) + ylab(var.names[i.var[2]]) if(f.factor[2]) g <- ggplot(d, aes_string(y="y", x=var.names[1])) + geom_point() + geom_line() + facet_wrap(var.names[2]) + ylab(paste("f(", var.names[i.var[1]], ",",var.names[i.var[2]], ")", sep = "")) if(f.factor[1]) g <- ggplot(d, aes_string(y="y", x=var.names[2])) + geom_point() + geom_line() + facet_wrap(var.names[1]) + ylab(paste("f(", var.names[i.var[1]], ",",var.names[i.var[2]], ")", sep = "")) else stop("set return.grid=TRUE to make a custom graph") print(g) return(g) plot metboost performance

1	perf.metboost(x, threshold = 0, lag = 1, ...)

`x`	metboost object
`threshold`	absolute differences in error less than this threshold is optimal
`lag`	lag of the differences in error across iterations
`...`	arguments passed to plot