p_direction | R Documentation |

Compute the **Probability of Direction** (* pd*, also known as the Maximum
Probability of Effect -

```
p_direction(x, ...)
pd(x, ...)
## S3 method for class 'numeric'
p_direction(
x,
method = "direct",
null = 0,
as_p = FALSE,
remove_na = TRUE,
...
)
## S3 method for class 'data.frame'
p_direction(
x,
method = "direct",
null = 0,
as_p = FALSE,
remove_na = TRUE,
rvar_col = NULL,
...
)
## S3 method for class 'MCMCglmm'
p_direction(
x,
method = "direct",
null = 0,
as_p = FALSE,
remove_na = TRUE,
...
)
## S3 method for class 'emmGrid'
p_direction(
x,
method = "direct",
null = 0,
as_p = FALSE,
remove_na = TRUE,
...
)
## S3 method for class 'slopes'
p_direction(
x,
method = "direct",
null = 0,
as_p = FALSE,
remove_na = TRUE,
...
)
## S3 method for class 'stanreg'
p_direction(
x,
effects = c("fixed", "random", "all"),
component = c("location", "all", "conditional", "smooth_terms", "sigma",
"distributional", "auxiliary"),
parameters = NULL,
method = "direct",
null = 0,
as_p = FALSE,
remove_na = TRUE,
...
)
## S3 method for class 'brmsfit'
p_direction(
x,
effects = c("fixed", "random", "all"),
component = c("conditional", "zi", "zero_inflated", "all"),
parameters = NULL,
method = "direct",
null = 0,
as_p = FALSE,
remove_na = TRUE,
...
)
## S3 method for class 'BFBayesFactor'
p_direction(
x,
method = "direct",
null = 0,
as_p = FALSE,
remove_na = TRUE,
...
)
## S3 method for class 'get_predicted'
p_direction(
x,
method = "direct",
null = 0,
as_p = FALSE,
remove_na = TRUE,
use_iterations = FALSE,
verbose = TRUE,
...
)
```

`x` |
A vector representing a posterior distribution, a data frame of posterior draws (samples be parameter). Can also be a Bayesian model. |

`...` |
Currently not used. |

`method` |
Can be |

`null` |
The value considered as a "null" effect. Traditionally 0, but could also be 1 in the case of ratios of change (OR, IRR, ...). |

`as_p` |
If |

`remove_na` |
Should missing values be removed before computation? Note
that |

`rvar_col` |
A single character - the name of an |

`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 |

`use_iterations` |
Logical, if |

`verbose` |
Toggle off warnings. |

Values between 0.5 and 1 *or* between 0 and 1 (see above) corresponding to
the probability of direction (pd).

The Probability of Direction (pd) is an index of effect existence, representing the certainty with which an effect goes in a particular direction (i.e., is positive or negative / has a sign), typically ranging from 0.5 to 1 (but see next section for cases where it can range between 0 and 1). Beyond its simplicity of interpretation, understanding and computation, this index also presents other interesting properties:

Like other posterior-based indices,

*pd*is solely based on the posterior distributions and does not require any additional information from the data or the model (e.g., such as priors, as in the case of Bayes factors).It is robust to the scale of both the response variable and the predictors.

It is strongly correlated with the frequentist p-value, and can thus be used to draw parallels and give some reference to readers non-familiar with Bayesian statistics (Makowski et al., 2019).

In most cases, it seems that the *pd* has a direct correspondence with the
frequentist one-sided *p*-value through the formula (for two-sided *p*):
p = 2 * (1 - p_{d})
Thus, a two-sided p-value of respectively `.1`

, `.05`

, `.01`

and `.001`

would
correspond approximately to a *pd* of `95%`

, `97.5%`

, `99.5%`

and `99.95%`

.
See `pd_to_p()`

for details.

The largest value *pd* can take is 1 - the posterior is strictly directional.
However, the smallest value *pd* can take depends on the parameter space
represented by the posterior.

**For a continuous parameter space**, exact values of 0 (or any point null
value) are not possible, and so 100% of the posterior has *some* sign, some
positive, some negative. Therefore, the smallest the *pd* can be is 0.5 -
with an equal posterior mass of positive and negative values. Values close to
0.5 *cannot* be used to support the null hypothesis (that the parameter does
*not* have a direction) is a similar why to how large p-values cannot be used
to support the null hypothesis (see `pd_to_p()`

; Makowski et al., 2019).

**For a discrete parameter space or a parameter space that is a mixture
between discrete and continuous spaces**, exact values of 0 (or any point
null value) *are* possible! Therefore, the smallest the *pd* can be is 0 -
with 100% of the posterior mass on 0. Thus values close to 0 can be used to
support the null hypothesis (see van den Bergh et al., 2021).

Examples of posteriors representing discrete parameter space:

When a parameter can only take discrete values.

When a mixture prior/posterior is used (such as the spike-and-slab prior; see van den Bergh et al., 2021).

When conducting Bayesian model averaging (e.g.,

`weighted_posteriors()`

or`brms::posterior_average`

).

The *pd* is defined as:

`p_d = max({Pr(\hat{\theta} < \theta_{null}), Pr(\hat{\theta} > \theta_{null})})`

The most simple and direct way to compute the *pd* is to compute the
proportion of positive (or larger than `null`

) posterior samples, the
proportion of negative (or smaller than `null`

) posterior samples, and take
the larger of the two. This "simple" method is the most straightforward, but
its precision is directly tied to the number of posterior draws.

The second approach relies on density estimation: It starts by
estimating the continuous-smooth density function (for which many methods are
available), and then computing the area under the curve
(AUC) of the density curve on either side of `null`

and taking the maximum
between them. Note the this approach assumes a continuous density function,
and so **when the posterior represents a (partially) discrete parameter
space, only the direct method must be used** (see above).

There is also a `plot()`

-method implemented in the see-package.

Makowski, D., Ben-Shachar, M. S., Chen, S. A., & Lüdecke, D. (2019). Indices of effect existence and significance in the Bayesian framework. Frontiers in psychology, 10, 2767. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3389/fpsyg.2019.02767")}

van den Bergh, D., Haaf, J. M., Ly, A., Rouder, J. N., & Wagenmakers, E. J. (2021). A cautionary note on estimating effect size. Advances in Methods and Practices in Psychological Science, 4(1). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/2515245921992035")}

`pd_to_p()`

to convert between Probability of Direction (pd) and p-value.

```
library(bayestestR)
# Simulate a posterior distribution of mean 1 and SD 1
# ----------------------------------------------------
posterior <- rnorm(1000, mean = 1, sd = 1)
p_direction(posterior)
p_direction(posterior, method = "kernel")
# Simulate a dataframe of posterior distributions
# -----------------------------------------------
df <- data.frame(replicate(4, rnorm(100)))
p_direction(df)
p_direction(df, method = "kernel")
# rstanarm models
# -----------------------------------------------
model <- rstanarm::stan_glm(mpg ~ wt + cyl,
data = mtcars,
chains = 2, refresh = 0
)
p_direction(model)
p_direction(model, method = "kernel")
# emmeans
# -----------------------------------------------
p_direction(emmeans::emtrends(model, ~1, "wt", data = mtcars))
# brms models
# -----------------------------------------------
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
p_direction(model)
p_direction(model, method = "kernel")
# BayesFactor objects
# -----------------------------------------------
bf <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
p_direction(bf)
p_direction(bf, method = "kernel")
# Using "rvar_col"
x <- data.frame(mu = c(0, 0.5, 1), sigma = c(1, 0.5, 0.25))
x$my_rvar <- posterior::rvar_rng(rnorm, 3, mean = x$mu, sd = x$sigma)
x
p_direction(x, rvar_col = "my_rvar")
```

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