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

Source: R/DPI.R
DPI_dag.Rd

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

Usage

DPI_dag(
  data,
  k.covs = 1,
  n.sim = 1000,
  alpha = 0.05,
  bonf = FALSE,
  pseudoBF = FALSE,
  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 (e.g., 1:10) of number of random covariates (simulating potential omitted variables) added to each simulation sample. Defaults to 1. For details, see DPI().

n.sim

Number of simulation samples. Defaults to 1000.

alpha

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

  • Direction = R2.Y - R2.X

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

bonf

Bonferroni correction to control for false positive rates: alpha is divided by, and p values are multiplied by, the number of comparisons.

  • Defaults to FALSE: No correction, suitable if you plan to test only one pair of variables.

  • TRUE: Using k * (k - 1) / 2 (number of all combinations of variable pairs) where k = length(data).

  • A user-specified number of comparisons.

pseudoBF

Use normalized pseudo Bayes Factors sigmoid(log(PseudoBF10)) alternatively as the Significance score (0~1). Pseudo Bayes Factors are computed from p value of X-Y partial relationship and total sample size, using the transformation rules proposed by Wagenmakers (2022) doi:10.31234/osf.io/egydq .

Defaults to FALSE because it makes less penalties for insignificant partial relationships between X and Y, see Examples in DPI() and online documentation.

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.

See also

S3method.network

DPI()

DPI_curve()

BNs_dag()

cor_net()

p_to_bf()

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)
#> Sample size: N.valid = 111
#> Significance score method: Sigmoid(p/alpha) = 1 - tanh(p.xy/alpha/2)
#> Simulation sample setting: k.covs = 1, 3, and 5, n.sim = 1000, seed = 1
#> False positive rates (FPR) control: Alpha = 0.05 (Bonferroni correction = 1)
#> 
#> Exploring [1/5]:
#> r.partial = 0.560, p = 4e-10 *** (PseudoBF10 = 8.650e+07)
#> --------- 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 *** (PseudoBF10 = 13070.747)
#> --------- 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 *  (PseudoBF10 = 0.927)
#> --------- 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 *  (PseudoBF10 = 0.671)
#> --------- 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 *** (PseudoBF10 = 25695.789)
#> --------- 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()