GenericML (version 0.2.2)

plot.GenericML: Plot method for a "GenericML" object

Description

Visualizes the estimates of the generic targets of interest: plots the point estimates as well as the corresponding confidence intervals. The generic targets of interest can be (subsets of) the parameters of the BLP, GATES, or CLAN analysis.

Usage

# S3 method for GenericML
plot(
  x,
  type = "GATES",
  learner = "best",
  CLAN_variable = NULL,
  groups = "all",
  ATE = TRUE,
  limits = NULL,
  title = NULL,
  ...
)

Arguments

x

An object of the class "GenericML", as returned by the function GenericML().

type

The analysis whose parameters shall be plotted. Either "GATES", "BLP", or "CLAN". Default is "GATES".

learner

The learner whose results are to be returned. Default is "best" for the best learner as measured by the \(\Lambda\) parameters.

CLAN_variable

Name of the CLAN variable to be plotted. Only applicable if type = "CLAN".

groups

Character vector indicating the per-group parameter estimates that shall be plotted in GATES and CLAN analyses. Default is "all" for all parameters. If there are \(K\) groups, this variable is a subset of c("G1", "G2",...,"GK", "G1-G2", "G1-G2",..., "G1-GK", "GK-G1", "GK-G2",...), where Gk denotes the k-th group. Note that this set depends on the choices of the arguments "diff_GATES" and "diff_CLAN" of the "GenericML" object.

ATE

Logical. If TRUE (default), then the BLP estimate of the average treatment effect along with confidence intervals will be added to the plot. Only applicable if type is "CLAN" or "GATES".

limits

A numeric vector of length two holding the limits of the y-axis of the plot.

title

The title of the plot.

...

Additional arguments to be passed down.

Value

An object of class "ggplot".

Details

If you wish to retrieve the data frame that this plot method visualizes, please use setup_plot().

See Also

setup_plot(), GenericML(), get_BLP(), get_GATES(), get_CLAN(), setup_diff()

Examples

# NOT RUN {
if(require("ranger")) {

## generate data
set.seed(1)
n  <- 150                                  # number of observations
p  <- 5                                    # number of covariates
D  <- rbinom(n, 1, 0.5)                    # random treatment assignment
Z  <- matrix(runif(n*p), n, p)             # design matrix
Y0 <- as.numeric(Z %*% rexp(p) + rnorm(n)) # potential outcome without treatment
Y1 <- 2 + Y0                               # potential outcome under treatment
Y  <- ifelse(D == 1, Y1, Y0)               # observed outcome

## name the columns of Z
colnames(Z) <- paste0("V", 1:p)

## specify learners
learners <- c("random_forest")

## specify quantile cutoffs (the 4 quartile groups here)
quantile_cutoffs <- c(0.25, 0.5, 0.75)

## specify the differenced generic targets of GATES and CLAN
diff_GATES <- setup_diff(subtract_from = "most",
                         subtracted = c(1,2,3))
diff_CLAN  <- setup_diff(subtract_from = "least",
                         subtracted = c(3,2))

## perform generic ML inference
# small number of splits to keep computation time low
x <- GenericML(Z, D, Y, learners, num_splits = 2,
               quantile_cutoffs = quantile_cutoffs,
               diff_GATES = diff_GATES,
               diff_CLAN = diff_CLAN,
               parallel = FALSE)

## plot BLP parameters
plot(x, type = "BLP")

## plot GATES parameters "G1", "G4", "G4-G1"
plot(x, type = "GATES", groups = c("G1", "G4", "G4-G1"))

## plot CLAN parameters "G1", "G2", "G2-G1" of variable "V1":
plot(x, type = "CLAN", CLAN_variable = "V1",
     groups = c("G1", "G2", "G1-G3"))
}

# }