DPI curve analysis across multiple random covariates.

DPI curve analysis across multiple random covariates.

Usage

DPI_curve(
  model,
  y,
  x,
  data = NULL,
  k.covs = 1:10,
  n.sim = 1000,
  alpha = 0.05,
  seed = NULL,
  progress,
  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.

alpha

Significance level for computing the Strength score (0~1) based on p value of partial correlation between X and Y. Defaults to 0.05.

• Direction = R2.Y - R2.X

• Strength = 1 - tanh(p.beta.xy/alpha/2)

seed

Random seed for replicable results. Defaults to NULL.

progress

Show progress bar. Defaults to TRUE (if length(k.covs) >= 5).

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.

See also

S3method.dpi

DPI()

DPI_dag()

BNs_dag()

cor_net()

Examples

model = lm(Ozone ~ ., data=airquality)
DPIs = DPI_curve(model, y="Ozone", x="Solar.R", seed=1)
#> ⠙ Simulation k.covs: 1/10 ███████████████████████████████   10% [00:00:3.5]
#> ⠹ Simulation k.covs: 2/10 ███████████████████████████████   20% [00:00:7.2]
#> ⠸ Simulation k.covs: 3/10 ███████████████████████████████   30% [00:00:11]
#> ⠼ Simulation k.covs: 4/10 ███████████████████████████████   40% [00:00:14.9]
#> ⠴ Simulation k.covs: 5/10 ███████████████████████████████   50% [00:00:19]
#> ⠦ Simulation k.covs: 6/10 ███████████████████████████████   60% [00:00:23.2]
#> ⠧ Simulation k.covs: 7/10 ███████████████████████████████   70% [00:00:27.7]
#> ⠇ Simulation k.covs: 8/10 ███████████████████████████████   80% [00:00:32.3]
#> ⠏ Simulation k.covs: 9/10 ███████████████████████████████   90% [00:00:36.9]
#> ✔ 10 * 1000 simulation samples estimated in 41.8s
#> 
plot(DPIs)  # ggplot object