Directed acyclic graphs (DAGs) via DPI exploratory analysis (causal discovery) for all significant partial rs.

Usage

DPI_dag(
  data,
  k.covs = 1:5,
  n.sim = 1000,
  alpha = 0.05,
  seed = NULL,
  progress,
  file = NULL,
  width = 6,
  height = 4,
  dpi = 500
)

Arguments

data

A dataset with at least 3 variables.

k.covs

An integer vector of number of random covariates (simulating potential omitted variables) added to each simulation sample. Defaults to 1:5 (producing DPI results for k.cov=1~5). 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 (class dpi.dag) of DPI exploration results.

Examples

# partial correlation networks (undirected)
cor_net(airquality, "pcor")
#> Displaying Partial Correlation Network


# directed acyclic graphs
dpi.dag = DPI_dag(airquality, k.covs=c(1,3,5), seed=1)
#> Simulation sample settings: k.covs = 1, 3, and 5, n.sim = 1000, seed = 1
#> Exploring [1/5]:
#> r.partial = 0.560, p = 4e-10 ***
#> --------- DPI["Temp"->"Ozone"](1) = 0.023, p = 5e-05 ***
#> --------- DPI["Temp"->"Ozone"](3) = 0.023, p = 0.020 *
#> --------- DPI["Temp"->"Ozone"](5) = 0.022, p = 0.085 .
#> Exploring [2/5]:
#> r.partial = 0.438, p = 2e-06 ***
#> --------- DPI["Month"->"Temp"](1) = 0.364, p = <1e-99 ***
#> --------- DPI["Month"->"Temp"](3) = 0.357, p = 8e-87 ***
#> --------- DPI["Month"->"Temp"](5) = 0.350, p = 1e-52 ***
#> Exploring [3/5]:
#> r.partial = 0.205, p = 0.034 *
#> --------- DPI["Solar.R"->"Ozone"](1) = 0.297, p = 3e-21 ***
#> --------- DPI["Solar.R"->"Ozone"](3) = 0.281, p = 4e-07 ***
#> --------- DPI["Solar.R"->"Ozone"](5) = 0.265, p = 2e-04 ***
#> Exploring [4/5]:
#> r.partial = -0.192, p = 0.047 *
#> --------- DPI["Month"->"Ozone"](1) = 0.214, p = 6e-12 ***
#> --------- DPI["Month"->"Ozone"](3) = 0.204, p = 1e-04 ***
#> --------- DPI["Month"->"Ozone"](5) = 0.193, p = 0.003 **
#> Exploring [5/5]:
#> r.partial = -0.449, p = 1e-06 ***
#> --------- DPI["Wind"->"Ozone"](1) = 0.223, p = <1e-99 ***
#> --------- DPI["Wind"->"Ozone"](3) = 0.219, p = 3e-50 ***
#> --------- DPI["Wind"->"Ozone"](5) = 0.214, p = 8e-28 ***
print(dpi.dag, k=1)  # DAG with DPI(k=1)
#> Displaying DAG with DPI algorithm (k.cov = 1)

print(dpi.dag, k=3)  # DAG with DPI(k=3)
#> Displaying DAG with DPI algorithm (k.cov = 3)

print(dpi.dag, k=5)  # DAG with DPI(k=5)
#> Displaying DAG with DPI algorithm (k.cov = 5)


# modify ggplot attributes
gg = plot(dpi.dag, k=5, show.label=FALSE)
gg + labs(title="DAG with DPI(k=5)")


# visualize DPIs of multiple paths
ggplot(dpi.dag$DPI, aes(x=k.cov, y=DPI)) +
  geom_ribbon(aes(ymin=Sim.LLCI, ymax=Sim.ULCI, fill=path),
              alpha=0.1) +
  geom_line(aes(color=path), linewidth=0.7) +
  geom_point(aes(color=path)) +
  geom_hline(yintercept=0, color="red", linetype="dashed") +
  scale_y_continuous(limits=c(NA, 0.5)) +
  labs(color="Directed Prediction",
       fill="Directed Prediction") +
  theme_classic()

Directed acyclic graphs (DAGs) via DPI exploratory analysis (causal discovery) for all significant partial rs.

Usage

Arguments

Value

See also

Examples