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)
#> ⠙ Simulation k.covs: 1/10 ███████████████████████████████   10% [00:00:3.4]
#> ⠹ Simulation k.covs: 2/10 ███████████████████████████████   20% [00:00:07]
#> ⠸ Simulation k.covs: 3/10 ███████████████████████████████   30% [00:00:10.6]
#> ⠼ Simulation k.covs: 4/10 ███████████████████████████████   40% [00:00:14.4]
#> ⠴ Simulation k.covs: 5/10 ███████████████████████████████   50% [00:00:18.4]
#> ⠦ Simulation k.covs: 6/10 ███████████████████████████████   60% [00:00:22.5]
#> ⠧ Simulation k.covs: 7/10 ███████████████████████████████   70% [00:00:26.7]
#> ⠇ Simulation k.covs: 8/10 ███████████████████████████████   80% [00:00:31.2]
#> ⠏ Simulation k.covs: 9/10 ███████████████████████████████   90% [00:00:35.6]
#> ✔ 10 * 1000 simulation samples estimated in 40.3s
#> 
plot(DPIs)  # ggplot object