plot.aggTrees: Plot Method for aggTrees Objects

In aggTrees: Aggregation Trees

Plot Method for aggTrees Objects

Description

Plots an aggTrees object.

Usage

## S3 method for class 'aggTrees'
plot(x, leaves = get_leaves(x$tree), sequence = FALSE, ...)

Arguments

x	An aggTrees object.
leaves	Number of leaves of the desired tree. This can be used to plot subtrees.
sequence	If TRUE, the whole sequence of optimal groupings is displayed in a short animation.
...	Further arguments from prp.

Details

Nodes are colored using a diverging palette. Nodes with predictions smaller than the ATE (i.e., the root prediction) are colored in blue shades, and nodes with predictions larger than the ATE are colored in red shades. Moreover, predictions that are more distant in absolute value from the ATE get darker shades. This way, we have an immediate understanding of the groups with extreme GATEs.

Value

Plots an aggTrees object.

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2022). Aggregation Trees. CEIS Research Paper, 546. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2139/ssrn.4304256")}.

See Also

build_aggtree, inference_aggtree

Examples

## Generate data.
set.seed(1986)

n <- 1000
k <- 3

X <- matrix(rnorm(n * k), ncol = k)
colnames(X) <- paste0("x", seq_len(k))
D <- rbinom(n, size = 1, prob = 0.5)
mu0 <- 0.5 * X[, 1]
mu1 <- 0.5 * X[, 1] + X[, 2]
Y <- mu0 + D * (mu1 - mu0) + rnorm(n)

## Training-honest sample split.
honest_frac <- 0.5
splits <- sample_split(length(Y), training_frac = (1 - honest_frac))
training_idx <- splits$training_idx
honest_idx <- splits$honest_idx

Y_tr <- Y[training_idx]
D_tr <- D[training_idx]
X_tr <- X[training_idx, ]

Y_hon <- Y[honest_idx]
D_hon <- D[honest_idx]
X_hon <- X[honest_idx, ]

## Construct sequence of groupings. CATEs estimated internally.
groupings <- build_aggtree(Y_tr, D_tr, X_tr,
                           Y_hon, D_hon, X_hon)

## Plot.
plot(groupings)
plot(groupings, leaves = 3)
plot(groupings, sequence = TRUE)

aggTrees documentation built on Sept. 11, 2024, 9:22 p.m.