swh:1:snp:2c68a6c5a8af2f06ac2c0225927f25b54fd1f9d0
Tip revision: 092b63c552bdf3196413c25583520dc23033769b authored by Dominique Makowski on 30 October 2021, 13:00:02 UTC
version 0.11.5
version 0.11.5
Tip revision: 092b63c
p_direction.R
#' Probability of Direction (pd)
#'
#' Compute the **Probability of Direction** (***pd***, also known
#' as the Maximum Probability of Effect - *MPE*). It varies between `50%`
#' and `100%` (*i.e.*, `0.5` and `1`) and can be interpreted as
#' the probability (expressed in percentage) that a parameter (described by its
#' posterior distribution) is strictly positive or negative (whichever is the
#' most probable). It is mathematically defined as the proportion of the
#' posterior distribution that is of the median's sign. Although differently
#' expressed, this index is fairly similar (*i.e.*, is strongly correlated)
#' to the frequentist **p-value**.
#' \cr\cr
#' Note that in some (rare) cases, especially when used with model averaged
#' posteriors (see [weighted_posteriors()] or
#' `brms::posterior_average`), `pd` can be smaller than `0.5`,
#' reflecting high credibility of `0`.
#'
#' @param x Vector representing a posterior distribution. Can also be a Bayesian model (`stanreg`, `brmsfit` or `BayesFactor`).
#' @param method Can be `"direct"` or one of methods of [density estimation][estimate_density], such as `"kernel"`, `"logspline"` or `"KernSmooth"`. If `"direct"` (default), the computation is based on the raw ratio of samples superior and inferior to 0. Else, the result is based on the [Area under the Curve (AUC)][auc] of the estimated [density][estimate_density] function.
#' @param null The value considered as a "null" effect. Traditionally 0, but could also be 1 in the case of ratios.
#' @inheritParams hdi
#'
#' @details
#' \subsection{What is the *pd*?}{
#' The Probability of Direction (pd) is an index of effect existence, ranging
#' from `50%` to `100%`, representing the certainty with which an effect goes in
#' a particular direction (*i.e.*, is positive or negative). Beyond its
#' simplicity of interpretation, understanding and computation, this index also
#' presents other interesting properties:
#' \itemize{
#' \item It is independent from the model: It is solely based on the posterior
#' distributions and does not require any additional information from the data
#' or the model.
#' \item It is robust to the scale of both the response variable and the predictors.
#' \item 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.
#' }
#' }
#' \subsection{Relationship with the p-value}{
#' In most cases, it seems that the *pd* has a direct correspondence with the frequentist one-sided *p*-value through the formula \ifelse{html}{\out{p<sub>one sided</sub> = 1 - <sup>p(<em>d</em>)</sup>/<sub>100</sub>}}{\eqn{p_{one sided}=1-\frac{p_{d}}{100}}} and to the two-sided p-value (the most commonly reported one) through the formula \ifelse{html}{\out{p<sub>two sided</sub> = 2 * (1 - <sup>p(<em>d</em>)</sup>/<sub>100</sub>)}}{\eqn{p_{two sided}=2*(1-\frac{p_{d}}{100})}}. 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 also [pd_to_p()].
#' }
#' \subsection{Methods of computation}{
#' The most simple and direct way to compute the *pd* is to 1) look at the
#' median's sign, 2) select the portion of the posterior of the same sign and
#' 3) compute the percentage that this portion represents. 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][estimate_density]. It starts by estimating the density function
#' (for which many methods are available), and then computing the [area under
#' the curve][area_under_curve] (AUC) of the density curve on the other side of
#' 0.
#' }
#' \subsection{Strengths and Limitations}{
#' **Strengths:** Straightforward computation and interpretation. Objective
#' property of the posterior distribution. 1:1 correspondence with the
#' frequentist p-value.
#' \cr \cr
#' **Limitations:** Limited information favoring the null hypothesis.
#' }
#'
#' @return
#' Values between 0.5 and 1 corresponding to the probability of direction (pd).
#' \cr\cr
#' Note that in some (rare) cases, especially when used with model averaged
#' posteriors (see [weighted_posteriors()] or
#' `brms::posterior_average`), `pd` can be smaller than `0.5`,
#' reflecting high credibility of `0`. To detect such cases, the
#' `method = "direct"` must be used.
#'
#' @seealso [pd_to_p()] to convert between Probability of Direction (pd) and p-value.
#'
#' @note There is also a [`plot()`-method](https://easystats.github.io/see/articles/bayestestR.html) implemented in the \href{https://easystats.github.io/see/}{\pkg{see}-package}.
#'
#' @references
#' 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}
#'
#' @examples
#' 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")
#' \dontrun{
#' # rstanarm models
#' # -----------------------------------------------
#' if (require("rstanarm")) {
#' model <- rstanarm::stan_glm(mpg ~ wt + cyl,
#' data = mtcars,
#' chains = 2, refresh = 0
#' )
#' p_direction(model)
#' p_direction(model, method = "kernel")
#' }
#'
#' # emmeans
#' # -----------------------------------------------
#' if (require("emmeans")) {
#' p_direction(emtrends(model, ~1, "wt"))
#' }
#'
#' # brms models
#' # -----------------------------------------------
#' if (require("brms")) {
#' model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
#' p_direction(model)
#' p_direction(model, method = "kernel")
#' }
#'
#' # BayesFactor objects
#' # -----------------------------------------------
#' if (require("BayesFactor")) {
#' bf <- ttestBF(x = rnorm(100, 1, 1))
#' p_direction(bf)
#' p_direction(bf, method = "kernel")
#' }
#' }
#' @export
p_direction <- function(x, ...) {
UseMethod("p_direction")
}
#' @rdname p_direction
#' @export
pd <- p_direction
#' @rdname p_direction
#' @export
p_direction.numeric <- function(x, method = "direct", null = 0, ...) {
if (method == "direct") {
pdir <- max(
c(
length(x[x > null]) / length(x), # pd positive
length(x[x < null]) / length(x) # pd negative
)
)
} else {
dens <- estimate_density(x, method = method, precision = 2^10, extend = TRUE, ...)
if (length(x[x > null]) > length(x[x < null])) {
dens <- dens[dens$x > null, ]
} else {
dens <- dens[dens$x < null, ]
}
pdir <- area_under_curve(dens$x, dens$y, method = "spline")
if (pdir >= 1) pdir <- 1 # Enforce bounds
}
attr(pdir, "method") <- method
attr(pdir, "data") <- x
class(pdir) <- unique(c("p_direction", "see_p_direction", class(pdir)))
pdir
}
#' @rdname p_direction
#' @export
p_direction.data.frame <- function(x, method = "direct", null = 0, ...) {
obj_name <- .safe_deparse(substitute(x))
x <- .select_nums(x)
if (ncol(x) == 1) {
pd <- p_direction(x[, 1], method = method, null = null, ...)
} else {
pd <- sapply(x, p_direction, method = method, null = null, simplify = TRUE, ...)
}
out <- data.frame(
"Parameter" = names(x),
"pd" = pd,
row.names = NULL,
stringsAsFactors = FALSE
)
attr(out, "object_name") <- obj_name
class(out) <- unique(c("p_direction", "see_p_direction", class(out)))
out
}
#' @rdname p_direction
#' @export
p_direction.MCMCglmm <- function(x, method = "direct", null = 0, ...) {
nF <- x$Fixed$nfl
out <- p_direction(as.data.frame(x$Sol[, 1:nF, drop = FALSE]),
method = method,
null = null,
...
)
attr(out, "object_name") <- .safe_deparse(substitute(x))
out
}
#' @export
p_direction.mcmc <- function(x, method = "direct", null = 0, ...) {
p_direction(as.data.frame(x), method = method, null = null, ...)
}
#' @export
p_direction.BGGM <- function(x, method = "direct", null = 0, ...) {
p_direction(as.data.frame(x), method = method, null = null, ...)
}
#' @export
p_direction.bcplm <- function(x, method = "direct", null = 0, ...) {
p_direction(insight::get_parameters(x), method = method, null = null, ...)
}
#' @export
p_direction.mcmc.list <- p_direction.bcplm
#' @export
p_direction.blrm <- p_direction.bcplm
#' @export
p_direction.bayesQR <- p_direction.bcplm
#' @export
p_direction.bamlss <- function(x,
method = "direct",
null = 0,
component = c("all", "conditional", "location"),
...) {
component <- match.arg(component)
out <- p_direction(
insight::get_parameters(x, component = component),
method = method,
null = null,
...
)
out <- .add_clean_parameters_attribute(out, x)
out
}
#' @rdname p_direction
#' @export
p_direction.emmGrid <- function(x, method = "direct", null = 0, ...) {
xdf <- insight::get_parameters(x)
out <- p_direction(xdf, method = method, null = null, ...)
attr(out, "object_name") <- deparse(substitute(x), width.cutoff = 500)
out
}
#' @export
p_direction.emm_list <- p_direction.emmGrid
#' @keywords internal
.p_direction_models <- function(x,
effects,
component,
parameters,
method = "direct",
null = 0,
...) {
p_direction(
insight::get_parameters(
x,
effects = effects,
component = component,
parameters = parameters
),
method = method,
null = null,
...
)
}
#' @export
p_direction.sim.merMod <- function(x,
effects = c("fixed", "random", "all"),
parameters = NULL,
method = "direct",
null = 0,
...) {
effects <- match.arg(effects)
out <- .p_direction_models(
x = x,
effects = effects,
component = "conditional",
parameters = parameters,
method = method,
null = null,
...
)
attr(out, "data") <- insight::get_parameters(x, effects = effects, parameters = parameters)
out
}
#' @export
p_direction.sim <- function(x,
parameters = NULL,
method = "direct",
null = 0,
...) {
out <- .p_direction_models(
x = x,
effects = "fixed",
component = "conditional",
parameters = parameters,
method = method,
null = null,
...
)
attr(out, "data") <- insight::get_parameters(x, parameters = parameters)
out
}
#' @rdname p_direction
#' @export
p_direction.stanreg <- function(x,
effects = c("fixed", "random", "all"),
component = c("location", "all", "conditional", "smooth_terms", "sigma", "distributional", "auxiliary"),
parameters = NULL,
method = "direct",
null = 0,
...) {
effects <- match.arg(effects)
component <- match.arg(component)
cleaned_parameters <- insight::clean_parameters(x)
out <- .prepare_output(
p_direction(
insight::get_parameters(
x,
effects = effects,
component = component,
parameters = parameters
),
method = method,
null = null,
...
),
cleaned_parameters,
inherits(x, "stanmvreg")
)
attr(out, "clean_parameters") <- cleaned_parameters
class(out) <- unique(c("p_direction", "see_p_direction", class(out)))
attr(out, "object_name") <- .safe_deparse(substitute(x))
out
}
#' @export
p_direction.stanfit <- p_direction.stanreg
#' @export
p_direction.blavaan <- p_direction.stanreg
#' @rdname p_direction
#' @export
p_direction.brmsfit <- function(x,
effects = c("fixed", "random", "all"),
component = c("conditional", "zi", "zero_inflated", "all"),
parameters = NULL,
method = "direct",
null = 0,
...) {
effects <- match.arg(effects)
component <- match.arg(component)
cleaned_parameters <- insight::clean_parameters(x)
out <- .prepare_output(
p_direction(
insight::get_parameters(
x,
effects = effects,
component = component,
parameters = parameters
),
method = method,
null = null,
...
),
cleaned_parameters
)
attr(out, "clean_parameters") <- cleaned_parameters
class(out) <- unique(c("p_direction", "see_p_direction", class(out)))
attr(out, "object_name") <- .safe_deparse(substitute(x))
out
}
#' @rdname p_direction
#' @export
p_direction.BFBayesFactor <- function(x, method = "direct", null = 0, ...) {
out <- p_direction(insight::get_parameters(x), method = method, null = null, ...)
attr(out, "object_name") <- .safe_deparse(substitute(x))
out
}
#' @export
p_direction.get_predicted <- function(x, ...) {
if ("iterations" %in% names(attributes(x))) {
out <- p_direction(as.data.frame(t(attributes(x)$iterations)), ...)
} else {
stop("No iterations present in the output.")
}
attr(out, "object_name") <- .safe_deparse(substitute(x))
out
}
# Methods -----------------------------------------------------------------
#' Convert to Numeric
#'
#' @inheritParams base::as.numeric
#' @method as.numeric p_direction
#' @export
as.numeric.p_direction <- function(x, ...) {
if ("data.frame" %in% class(x)) {
return(as.numeric(as.vector(x$pd)))
} else {
return(as.vector(x))
}
}
#' @method as.double p_direction
#' @export
as.double.p_direction <- as.numeric.p_direction