# bain_sensitivity: Sensitivity analysis for bain In bain: Bayes Factors for Informative Hypotheses

## Description

Conducts a sensitivity analysis for bain.

## Usage

 1 bain_sensitivity(x, hypothesis, fractions = 1, ...)

## Arguments

 x An R object containing the outcome of a statistical analysis. Currently, the following objects can be processed: lm(), t_test(), lavaan objects created with the sem(), cfa(), and growth() functions, and named vector objects. See the vignette for elaborations. hypothesis A character string containing the informative hypotheses to evaluate. See the vignette for elaborations. fractions A number representing the fraction of information in the data used to construct the prior distribution. The default value 1 denotes the minimal fraction, 2 denotes twice the minimal fraction, etc. See the vignette for elaborations. ... Additional arguments passed to bain.

## Details

The Bayes factor for equality constraints is sensitive to a scaling factor applied to the prior distribution. The argument fraction adjusts this scaling factor. The function bain_sensitivity is a wrapper for bain, which accepts a vector for the fractions argument, and returns a list of bain results objects. A table with a sensitivity analysis for specific statistics can be obtained using the summary() function, which accepts the argument summary(which_stat = ...). The available statistics are elements of the \$fit table (Fit_eq, Com_eq, Fit_in, Com_in, Fit, Com, BF, PMPa, and PMPb), and elements of the BFmatrix, which can be accessed by matrix notation, e.g.: summary(bain_sens, which_stat = "BFmatrix[1,2]").

## Value

A data.frame of class "bain_sensitivity".

## Examples

 1 2 3 4 5 6 7 8 sesamesim\$site <- as.factor(sesamesim\$site) res <- lm(sesamesim\$postnumb~sesamesim\$site-1) set.seed(4583) bain_sens <- bain_sensitivity(res, "site1=site2; site2>site5", fractions = c(1,2,3)) summary(bain_sens, which_stat = "BF.c") summary(bain_sens, which_stat = "BFmatrix[1,2]")

bain documentation built on Nov. 27, 2021, 1:06 a.m.