NAPBF_onet: Bayes factor in favor of the NAP in one-sample t tests
In NAP: Non-Local Alternative Priors in Psychology

Description Usage Arguments Details Value Author(s) References Examples

In a N(μ,σ^2) population with unknown variance σ^2, consider the two-sided one-sample t-test for testing the point null hypothesis H_0 : μ = 0 against H_1 : μ \neq 0. Based on an observed data, this function calculates the Bayes factor in favor of H_1 when a normal moment prior is assumed on the standardized effect size μ/σ under the alternative. Under both hypotheses, the Jeffrey's prior π(σ^2) \propto 1/σ^2 is assumed on σ^2.

1 2	NAPBF_onet(obs, nObs, mean.obs, sd.obs, test.statistic, tau.NAP = 0.3/sqrt(2))

`obs`	Numeric vector. Observed vector of data.
`nObs`	Numeric or numeric vector. Sample size(s). Same as `length(obs)` when numeric.
`mean.obs`	Numeric or numeric vector. Sample mean(s). Same as `mean(obs)` when numeric.
`sd.obs`	Positive numeric or numeric vector. Sample standard deviation(s). Same as `sd(obs)` when numeric.
`test.statistic`	Numeric or numeric vector. Test-statistic value(s).
`tau.NAP`	Positive numeric. Parameter in the moment prior. Default: 0.3/√2. This places the prior modes of the standardized effect size μ/σ at 0.3 and -0.3.

Users can either specify obs, or nObs, mean.obs and sd.obs, or nObs and test.statistic.
If obs is provided, it returns the corresponding Bayes factor value.
If nObs, mean.obs and sd.obs are provided, the function is vectorized over the arguments. Bayes factor values corresponding to the values therein are returned.
If nObs and test.statistic are provided, the function is vectorized over the arguments. Bayes factor values corresponding to the values therein are returned.

Positive numeric or numeric vector. The Bayes factor value(s).

Sandipan Pramanik and Valen E. Johnson

Pramanik, S. and Johnson, V. (2022). Efficient Alternatives for Bayesian Hypothesis Tests in Psychology. Psychological Methods. Just accepted.

Johnson, V. and Rossell, R. (2010). On the use of non-local prior densities in Bayesian hypothesis tests. Journal of the Royal Statistical Society: Series B, 72:143-170. [Article]