p_map {bayestestR} | R Documentation |

Compute a Bayesian equivalent of the *p*-value, related to the odds that a parameter (described by its posterior distribution) has against the null hypothesis (*h0*) using Mills' (2014, 2017) *Objective Bayesian Hypothesis Testing* framework. It corresponds to the density value at 0 divided by the density at the Maximum A Posteriori (MAP).

```
p_map(x, precision = 2^10, method = "kernel", ...)
p_pointnull(x, precision = 2^10, method = "kernel", ...)
## S3 method for class 'stanreg'
p_map(
x,
precision = 2^10,
method = "kernel",
effects = c("fixed", "random", "all"),
component = c("location", "all", "conditional", "smooth_terms", "sigma",
"distributional", "auxiliary"),
parameters = NULL,
...
)
## S3 method for class 'brmsfit'
p_map(
x,
precision = 2^10,
method = "kernel",
effects = c("fixed", "random", "all"),
component = c("conditional", "zi", "zero_inflated", "all"),
parameters = NULL,
...
)
```

`x` |
Vector representing a posterior distribution, or a data frame of such
vectors. Can also be a Bayesian model. |

`precision` |
Number of points of density data. See the |

`method` |
Density estimation method. Can be |

`...` |
Currently not used. |

`effects` |
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated. |

`component` |
Should results for all parameters, parameters for the conditional model or the zero-inflated part of the model be returned? May be abbreviated. Only applies to brms-models. |

`parameters` |
Regular expression pattern that describes the parameters
that should be returned. Meta-parameters (like |

Note that this method is sensitive to the density estimation `method`

(see the section in the examples below).

**Strengths:** Straightforward computation. Objective property of the posterior distribution.

**Limitations:** Limited information favoring the null hypothesis. Relates on density approximation. Indirect relationship between mathematical definition and interpretation. Only suitable for weak / very diffused priors.

Makowski D, Ben-Shachar MS, Chen SHA, Lüdecke D (2019) Indices of Effect Existence and Significance in the Bayesian Framework. Frontiers in Psychology 2019;10:2767. doi:10.3389/fpsyg.2019.02767

Mills, J. A. (2018). Objective Bayesian Precise Hypothesis Testing. University of Cincinnati.

```
library(bayestestR)
p_map(rnorm(1000, 0, 1))
p_map(rnorm(1000, 10, 1))
## Not run:
library(rstanarm)
model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
p_map(model)
library(emmeans)
p_map(emtrends(model, ~1, "wt"))
library(brms)
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
p_map(model)
library(BayesFactor)
bf <- ttestBF(x = rnorm(100, 1, 1))
p_map(bf)
# ---------------------------------------
# Robustness to density estimation method
set.seed(333)
data <- data.frame()
for (iteration in 1:250) {
x <- rnorm(1000, 1, 1)
result <- data.frame(
"Kernel" = p_map(x, method = "kernel"),
"KernSmooth" = p_map(x, method = "KernSmooth"),
"logspline" = p_map(x, method = "logspline")
)
data <- rbind(data, result)
}
data$KernSmooth <- data$Kernel - data$KernSmooth
data$logspline <- data$Kernel - data$logspline
summary(data$KernSmooth)
summary(data$logspline)
boxplot(data[c("KernSmooth", "logspline")])
## End(Not run)
```

[Package *bayestestR* version 0.13.0 Index]