micd (version 1.1.1)

skeletonMI: Estimate (Initial) Skeleton of a DAG using the PC Algorithm for Multiple Imputed Data Sets of Continuous Data

Description

This function is a modification of pcalg::skeleton() to be used for multiple imputation.

Usage

skeletonMI(
  data,
  alpha,
  labels,
  p,
  method = c("stable", "original"),
  m.max = Inf,
  fixedGaps = NULL,
  fixedEdges = NULL,
  NAdelete = TRUE,
  verbose = FALSE
)

Value

See pcalg::skeleton() for more details.

Arguments

data

An object of type mids, which stands for 'multiply imputed data set', typically created by a call to function mice()

alpha

Significance level

labels

(Optional) character vector of variable (or "node") names. Typically preferred to specifying p

p

(Optional) number of variables (or nodes). May be specified if labels are not, in which case labels is set to 1:p.

method

Character string specifying method; the default, "stable" provides an order-independent skeleton, see pcalg::pc() for details.

m.max

Maximal size of the conditioning sets that are considered in the conditional independence tests.

fixedGaps

Logical symmetric matrix of dimension p*p. If entry [i,j] is true, the edge i-j is removed before starting the algorithm. Therefore, this edge is guaranteed to be absent in the resulting graph.

fixedEdges

A logical symmetric matrix of dimension p*p. If entry [i,j] is true, the edge i-j is never considered for removal. Therefore, this edge is guaranteed to be present in the resulting graph.

NAdelete

Logical needed for the case indepTest(*) returns NA. If it is true, the corresponding edge is deleted, otherwise not.

verbose

If TRUE, detailed output is provided.

Author

Original code by Markus Kalisch, Martin Maechler, Alain Hauser, and Diego Colombo. Modifications by Ronja Foraita.

Examples

data(gmG)
n <- nrow(gmG8$x)
V <- colnames(gmG8$x) # labels aka node names
## estimate Skeleton
data_mids <- mice(gmG8$x, printFlag = FALSE)
(skel.fit <- skeletonMI(data = data_mids, alpha = 0.01, labels = V, verbose = FALSE))