pcalg: disCItest – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

disCItest

G square Test for (Conditional) Independence of Discrete Variables

Description

G^2 test for (conditional) independence of discrete (each with a finite number of “levels”) variables X and Y given the (possibly empty) set of discrete variables S.

disCItest() is a wrapper of gSquareDis(), to be easily used in skeleton, pc and fci.

Usage

gSquareDis(x, y, S, dm, nlev, adaptDF = FALSE, n.min = 10*df, verbose = FALSE)
disCItest (x, y, S, suffStat)

Arguments

`x,y`	(integer) position of variable X and Y, respectively, in the adjacency matrix.
`S`	(integer) positions of zero or more conditioning variables in the adjacency matrix.
`dm`	data matrix (rows: samples, columns: variables) with integer entries; the k levels for a given column must be coded by the integers 0,1,...,k-1. (see example)
`nlev`	optional vector with numbers of levels for each variable in `dm`.
`adaptDF`	logical specifying if the degrees of freedom should be lowered by one for each zero count. The value for the degrees of freedom cannot go below 1.
`n.min`	the smallest n (number of observations, `nrow(dm)`) for which the G^2 test is computed; for smaller n, independence is assumed (G^2 := 1) with a warning. The default is 10 m, where m is the degrees of freedom assuming no structural zeros, here, the product of all the number of levels `(nlev[x]-1) * (nlev[y]-1) * prod(nlev[S])`.
`verbose`	logical or integer indicating that increased diagnostic output is to be provided.
`suffStat`	a `list` with three elements, `"dm"`, `"nlev"`, `"adaptDF"`; each corresponding to the above arguments of `gSquareDis()`.

Details

The G^2 statistic is used to test for (conditional) independence of X and Y given a set S (can be NULL). If only binary variables are involved, gSquareBin is a specialized (a bit more efficient) alternative to gSquareDis().

Value

The p-value of the test.

Author(s)

Nicoletta Andri and Markus Kalisch (kalisch@stat.math.ethz.ch).

References

R.E. Neapolitan (2004). Learning Bayesian Networks. Prentice Hall Series in Artificial Intelligence. Chapter 10.3.1

Examples

## Simulate data
n <- 100
set.seed(123)
x <- sample(0:2, n, TRUE) ## three levels
y <- sample(0:3, n, TRUE) ## four levels
z <- sample(0:1, n, TRUE) ## two levels
dat <- cbind(x,y,z)

## Analyze data
gSquareDis(1,3, S=2, dat, nlev = c(3,4,2)) # but nlev is optional:
gSquareDis(1,3, S=2, dat, verbose=TRUE, adaptDF=TRUE)
## with too little data, gives a warning (and p-value 1):
gSquareDis(1,3, S=2, dat[1:60,], nlev = c(3,4,2))

suffStat <- list(dm = dat, nlev = c(3,4,2), adaptDF = FALSE)
disCItest(1,3,2,suffStat)

pcalg

Methods for Graphical Models and Causal Inference

v2.7-2

GPL (>= 2)

Authors

Markus Kalisch [aut, cre], Alain Hauser [aut], Martin Maechler [aut], Diego Colombo [ctb], Doris Entner [ctb], Patrik Hoyer [ctb], Antti Hyttinen [ctb], Jonas Peters [ctb], Nicoletta Andri [ctb], Emilija Perkovic [ctb], Preetam Nandy [ctb], Philipp Ruetimann [ctb], Daniel Stekhoven [ctb], Manuel Schuerch [ctb], Marco Eigenmann [ctb], Leonard Henckel [ctb], Joris Mooij [ctb]

Initial release

2021-4-20