p_to_bf | R Documentation |

Convert p-values to (pseudo) Bayes Factors. This transformation has been
suggested by Wagenmakers (2022), but is based on a vast amount of assumptions.
It might therefore be not reliable. Use at your own risks. For more accurate
approximate Bayes factors, use `bic_to_bf()`

instead.

```
p_to_bf(x, ...)
## S3 method for class 'numeric'
p_to_bf(x, log = FALSE, n_obs = NULL, ...)
## Default S3 method:
p_to_bf(x, log = FALSE, ...)
```

`x` |
A (frequentist) model object, or a (numeric) vector of p-values. |

`...` |
Other arguments to be passed (not used for now). |

`log` |
Wether to return log Bayes Factors. |

`n_obs` |
Number of observations. Either length 1, or same length as |

A data frame with the p-values and pseudo-Bayes factors (against the null).

Wagenmakers, E.J. (2022). Approximate objective Bayes factors from p-values and sample size: The 3p(sqrt(n)) rule. Preprint available on ArXiv: https://psyarxiv.com/egydq

`bic_to_bf()`

for more accurate approximate Bayes factors.

```
data(iris)
model <- lm(Petal.Length ~ Sepal.Length + Species, data = iris)
p_to_bf(model)
# Examples that demonstrate comparison between
# BIC-approximated and pseudo BF
# --------------------------------------------
m0 <- lm(mpg ~ 1, mtcars)
m1 <- lm(mpg ~ am, mtcars)
m2 <- lm(mpg ~ factor(cyl), mtcars)
# In this first example, BIC-approximated BF and
# pseudo-BF based on p-values are close...
# BIC-approximated BF, m1 against null model
bic_to_bf(BIC(m1), denominator = BIC(m0))
# pseudo-BF based on p-values - dropping intercept
p_to_bf(m1)[-1, ]
# The second example shows that results from pseudo-BF are less accurate
# and should be handled wit caution!
bic_to_bf(BIC(m2), denominator = BIC(m0))
p_to_bf(anova(m2), n_obs = nrow(mtcars))
```

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