pcalg (version 2.7-0)

GaussL0penIntScore-class: Class "GaussL0penIntScore"

Description

This class represents a score for causal inference from jointly interventional and observational Gaussian data; it is used in the causal inference functions gies and simy.

Extends

Class "'>Score", directly.

All reference classes extend and inherit methods from "'>envRefClass".

Fields

The class GaussL0penIntScore has the same fields as '>Score. They need not be accessed by the user.

Constructor

new("GaussL0penIntScore",
  data = matrix(1, 1, 1),
  targets = list(integer(0)), 
  target.index = rep(as.integer(1), nrow(data)), 
  lambda = 0.5*log(nrow(data)), 
  intercept = FALSE, 
  use.cpp = TRUE, 
  ...)

data

Data matrix with $n$ rows and $p$ columns. Each row corresponds to one realization, either interventional or observational.

targets

List of mutually exclusive intervention targets that have been used for data generation.

target.index

Vector of length $n$; the $i$-th entry specifies the index of the intervention target in targets under which the $i$-th row of data was measured.

lambda

Penalization constant (cf. details)

intercept

Indicates whether an intercept is allowed in the linear structural equations, or, equivalently, if a mean different from zero is allowed for the observational distribution.

use.cpp

Indicates whether the calculation of the score should be done by the C++ library of the package, which speeds up calculation. This parameter should only be set to FALSE in the case of problems.

Methods

local.score(vertex, parents, ...)

Calculates the local score of a vertex and its parents. Since this score has no obvious interpretation, it is rather for internal use.

global.score.int(edges, ...)

Calculates the global score of a DAG, represented as a list of in-edges: for each vertex in the DAG, this list contains a vector of parents.

global.score(dag, ...)

Calculates the global score of a DAG, represented as an object of a class derived from '>ParDAG.

local.mle(vertex, parents, ...)

Calculates the local MLE of a vertex and its parents. The result is a vector of parameters encoded as follows:

• First element: variance of the Gaussian error term

• Second element: intercept

• Following elements: regression coefficients; one per parent vertex

global.mle(dag, ...)

Calculates the global MLE of a DAG, represented by an object of a class derived from '>ParDAG. The result is a list of vectors, one per vertex, each in the same format as the result vector of local.mle.

Details

The class implements an ${\ell }_0$-penalized Gaussian maximum likelihood estimator. The penalization is a constant (specified by the argument lambda in the constructor) times the number of parameters of the DAG model. By default, the constant $\lambda$ is chosen as $\log (n)/2$, which corresponds to the BIC score.

See Also

gies, simy, '>GaussL0penObsScore, '>Score

Examples

# NOT RUN {
##################################################
## Using Gaussian Data
##################################################
## Load predefined data
data(gmInt)

## Define the score object
score <- new("GaussL0penIntScore", gmInt$x, gmInt$targets, gmInt$target.index) 

## Score of the true underlying DAG
score$global.score(as(gmInt$g, "GaussParDAG"))

## Score of the DAG that has only one edge from 1 to 2
A <- matrix(0, ncol(gmInt$x), ncol(gmInt$x))
A[1, 2] <- 1
score$global.score(as(A, "GaussParDAG"))
## (Note: this is lower than the score of the true DAG.)
# }