lin.mle.test: Lin and Stivers’s MLE-based test under heteroscedasticity

In samvanderpoel/partial-pairs: Statistics for Partially Matched Samples

Description

lin.mle.test uses the Lin and Stivers’s MLE-based test under heteroscedasticity to obtain a p-value for a partially matched pairs test.

Usage

lin.mle.test(x, y, alternative = c("two.sided", "greater", "less"))

Arguments

x	a non-empty numeric vector containing some NA values
y	a non-empty numeric vector containing some NA values
alternative	specification of the alternative hypothesis. Takes values: "two.sided", "greater", or "less".

Details

Lin and Stivers’s test makes use of a modified maximum likelihood estimator and assumption of heteroscedasticity. Under the null hypothesis, the resulting test statistic Z_LS, follows an approximate t distribution with n1 degrees of freedom. Mathematical details are provided in [Kuan & Huang, 2013].

If proper sample size conditions are not met, then lin.mle.test may exit or perform a paired or unpaired two-sample t.test, depending on the nature of the sample size issue.

If the variance of input data is close to zero, lin.mle.test will return an error message.

Value

p-value associated with the hypothesis test

References

Kuan, Pei Fen, and Bo Huang. "A simple and robust method for partially matched samples using the p‐values pooling approach." Statistics in medicine 32.19 (2013): 3247-3259.

Examples

In the following, the true means are not equal:

x = rnorm(400, 0, 1)
x[sample(1:400, size=75, replace=FALSE)] = NA
y = rnorm(400, 0.4, 3)
y[sample(1:400, size=75, replace=FALSE)] = NA
lin.mle.test(x, y, alternative = 'two.sided')

samvanderpoel/partial-pairs documentation built on Dec. 22, 2021, 10:14 p.m.