mixCItwd: Likelihood Ratio Test for (Conditional) Independence between...

In bips-hb/micd: Multiple Imputation in Causal Graph Discovery

Likelihood Ratio Test for (Conditional) Independence between Mixed Variables with Missings

Description

A version of mixCItest, to be used within pcalg::skeleton, pcalg::pc or pcalg::fci when the data contain missing values. Observations where at least one of the variables involved in the test is missing are deleted prior to performing the test (test-wise deletion).

Usage

mixCItwd(x, y, S = NULL, suffStat)

Arguments

x, y, S	(Integer) position of variable X, Y and set of variables S, respectively, in suffStat. It is tested whether X and Y are conditionally independent given the subset S of the remaining variables.
suffStat	data.frame. Discrete variables must be coded as factors.

Details

See mixCItest for details on the assumptions of the Conditional Gaussian likelihood ratio test. Test-wise deletion is valid if missingness does not jointly depend on X and Y.

Value

A p-value.

See Also

mixCItest() for complete data, mixMItest() for multiply imputed data

Examples

## load data (numeric and factor variables)
data(toenail2)
dat <- toenail2[, -1]

## delete some observations
set.seed(123)
dat[sample(2000, 20), 1] <- NA
dat[sample(2000, 30), 3] <- NA

## analyse data 
# complete data: ==========
mixCItest(1, 2, 4, suffStat=toenail2)

# test-wise deletion: ==========
mixCItwd(1, 2, 4, suffStat = dat)

# list-wise deletion: ==========
dat2 <- dat[complete.cases(dat), ]
mixCItest(1, 2, 4, suffStat = dat2)

## use mixCItwd within pcalg::pc
pc.fit <- pc(suffStat = dat, indepTest = mixCItwd, alpha = 0.01, p = 4)

bips-hb/micd documentation built on Feb. 22, 2023, 7:59 p.m.