DPI_curve: The DPI curve analysis.

In DPI: The Directed Prediction Index

The DPI curve analysis.

Description

The DPI curve analysis.

Usage

DPI_curve(
  model,
  y,
  x,
  data = NULL,
  k.covs = 1:10,
  n.sim = 1000,
  seed = NULL,
  file = NULL,
  width = 6,
  height = 4,
  dpi = 500
)

Arguments

model	Model object (lm).
y	Dependent (outcome) variable.
x	Independent (predictor) variable.
data	[Optional] Defaults to NULL. If data is specified, then model will be ignored and a linear model lm({y} ~ {x} + .) will be fitted inside. This is helpful for exploring all variables in a dataset.
k.covs	An integer vector of number of random covariates (simulating potential omitted variables) added to each simulation sample. Defaults to 1:10 (producing DPI results for k.cov=1~10). For details, see DPI.
n.sim	Number of simulation samples. Defaults to 1000.
seed	Random seed for replicable results. Defaults to NULL.
file	File name of saved plot (".png" or ".pdf").
width, height	Width and height (in inches) of saved plot. Defaults to 6 and 4.
dpi	Dots per inch (figure resolution). Defaults to 500.

Value

Return a data.frame of DPI curve results.

Examples

model = lm(Ozone ~ ., data=airquality)
DPIs = DPI_curve(model, y="Ozone", x="Solar.R", seed=1)
plot(DPIs)  # ggplot object

DPI documentation built on June 18, 2025, 9:08 a.m.