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| #' @importFrom bayestestR p_direction | ||
| #' @export | ||
| bayestestR::p_direction | ||
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| #' @title Probability of Direction (pd) | ||
| #' @name p_direction.lm | ||
| #' | ||
| #' @description Compute the **Probability of Direction** (*pd*, also known as | ||
| #' the Maximum Probability of Effect - *MPE*). This can be interpreted as the | ||
| #' probability that a parameter (described by its full confidence, or | ||
| #' "compatibility" interval) is strictly positive or negative (whichever is the | ||
| #' most probable). Although differently expressed, this index is fairly similar | ||
| #' (i.e., is strongly correlated) to the frequentist *p-value* (see 'Details'). | ||
| #' | ||
| #' @param x A statistical model. | ||
| #' @inheritParams bayestestR::p_direction | ||
| #' @inheritParams model_parameters.default | ||
| #' @param ... Arguments passed to other methods, e.g. `ci()`. | ||
| #' | ||
| #' @seealso See also [`equivalence_test()`], [`p_function()`] and | ||
| #' [`p_significance()`] for functions related to checking effect existence and | ||
| #' significance. | ||
| #' | ||
| #' @inheritSection bayestestR::p_direction What is the *pd*? | ||
| #' | ||
| #' @inheritSection bayestestR::p_direction Relationship with the p-value | ||
| #' | ||
| #' @inheritSection bayestestR::p_direction Possible Range of Values | ||
| #' | ||
| #' @inheritSection model_parameters Statistical inference - how to quantify evidence | ||
| #' | ||
| #' @references | ||
| #' | ||
| #' - Amrhein, V., Korner-Nievergelt, F., and Roth, T. (2017). The earth is | ||
| #' flat (p > 0.05): Significance thresholds and the crisis of unreplicable | ||
| #' research. PeerJ, 5, e3544. \doi{10.7717/peerj.3544} | ||
| #' | ||
| #' - Greenland S, Rafi Z, Matthews R, Higgs M. To Aid Scientific Inference, | ||
| #' Emphasize Unconditional Compatibility Descriptions of Statistics. (2022) | ||
| #' https://arxiv.org/abs/1909.08583v7 (Accessed November 10, 2022) | ||
| #' | ||
| #' - Lakens, D. (2024). Improving Your Statistical Inferences (Version v1.5.1). | ||
| #' Retrieved from https://lakens.github.io/statistical_inferences/. | ||
| #' \doi{10.5281/ZENODO.6409077} | ||
| #' | ||
| #' - Lakens, D., Scheel, A. M., and Isager, P. M. (2018). Equivalence Testing | ||
| #' for Psychological Research: A Tutorial. Advances in Methods and Practices | ||
| #' in Psychological Science, 1(2), 259–269. \doi{10.1177/2515245918770963} | ||
| #' | ||
| #' - Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., and Lüdecke, D. (2019). | ||
| #' Indices of Effect Existence and Significance in the Bayesian Framework. | ||
| #' Frontiers in Psychology, 10, 2767. \doi{10.3389/fpsyg.2019.02767} | ||
| #' | ||
| #' - Rafi Z, Greenland S. Semantic and cognitive tools to aid statistical | ||
| #' science: replace confidence and significance by compatibility and surprise. | ||
| #' BMC Medical Research Methodology (2020) 20:244. | ||
| #' | ||
| #' - Schweder T. Confidence is epistemic probability for empirical science. | ||
| #' Journal of Statistical Planning and Inference (2018) 195:116–125. | ||
| #' \doi{10.1016/j.jspi.2017.09.016} | ||
| #' | ||
| #' - Schweder T, Hjort NL. Frequentist analogues of priors and posteriors. | ||
| #' In Stigum, B. (ed.), Econometrics and the Philosophy of Economics: Theory | ||
| #' Data Confrontation in Economics, pp. 285-217. Princeton University Press, | ||
| #' Princeton, NJ, 2003 | ||
| #' | ||
| #' - Vos P, Holbert D. Frequentist statistical inference without repeated sampling. | ||
| #' Synthese 200, 89 (2022). \doi{10.1007/s11229-022-03560-x} | ||
| #' | ||
| #' @return A data frame. | ||
| #' | ||
| #' @examplesIf requireNamespace("bayestestR") && require("see", quietly = TRUE) | ||
| #' data(qol_cancer) | ||
| #' model <- lm(QoL ~ time + age + education, data = qol_cancer) | ||
| #' p_direction(model) | ||
| #' | ||
| #' result <- p_direction(model) | ||
| #' plot(result) | ||
| #' @export | ||
| p_direction.lm <- function(x, | ||
| ci = 0.95, | ||
| method = "direct", | ||
| null = 0, | ||
| ...) { | ||
| # first, we need CIs | ||
| out <- ci(x, ci = ci, ...) | ||
| # we now iterate all confidence intervals and create an approximate normal | ||
| # distribution that covers the CI-range. | ||
| posterior <- as.data.frame(lapply(seq_len(nrow(out)), function(i) { | ||
| ci_range <- as.numeric(out[i, c("CI_low", "CI_high")]) | ||
| .generate_posterior_from_ci(ci, ci_range) | ||
| })) | ||
| colnames(posterior) <- out$Parameter | ||
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| out$pd <- as.numeric(bayestestR::p_direction( | ||
| posterior, | ||
| method = method, | ||
| null = null, | ||
| ... | ||
| )) | ||
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| # check we don't have duplicated columns in "posterior" we need this for | ||
| # plotting | ||
| if (anyDuplicated(colnames(posterior)) > 0 && !is.null(out$Component)) { | ||
| comps <- .rename_values(out$Component, "zero_inflated", "zi") | ||
| comps <- .rename_values(comps, "conditional", "cond") | ||
| colnames(posterior) <- paste0(out$Parameter, "_", comps) | ||
| out$Parameter <- paste0(out$Parameter, "_", comps) | ||
| } | ||
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| # we need these for plotting | ||
| if (!"Effects" %in% colnames(out)) { | ||
| out$Effects <- "fixed" | ||
| } | ||
| if (!"Component" %in% colnames(out)) { | ||
| out$Component <- "conditional" | ||
| } | ||
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| # reorder | ||
| out <- out[intersect(c("Parameter", "CI", "CI_low", "CI_high", "pd", "Effects", "Component"), colnames(out))] | ||
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| attr(out, "data") <- posterior | ||
| attr(out, "null") <- null | ||
| class(out) <- c("p_direction_lm", "p_direction", "see_p_direction", "data.frame") | ||
| out | ||
| } | ||
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| # methods --------------------------------------------------------------------- | ||
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| #' @export | ||
| print.p_direction_lm <- function(x, digits = 2, p_digits = 3, ...) { | ||
| null <- attributes(x)$null | ||
| caption <- sprintf( | ||
| "Probability of Direction (null: %s)", | ||
| insight::format_value(null, digits = digits, protect_integer = TRUE) | ||
| ) | ||
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| # deal with Effects and Component columns | ||
| if ("Effects" %in% colnames(x) && insight::n_unique(x$Effects) == 1) { | ||
| x$Effects <- NULL | ||
| } | ||
| if ("Component" %in% colnames(x) && insight::n_unique(x$Component) == 1) { | ||
| x$Component <- NULL | ||
| } | ||
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| x <- insight::format_table(x, digits = digits, p_digits = p_digits) | ||
| cat(insight::export_table(x, title = caption, ...)) | ||
| } | ||
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| # other classes -------------------------------------------------------------- | ||
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| #' @export | ||
| p_direction.glm <- p_direction.lm | ||
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| #' @export | ||
| p_direction.coxph <- p_direction.lm | ||
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| #' @export | ||
| p_direction.svyglm <- p_direction.lm | ||
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| #' @export | ||
| p_direction.glmmTMB <- p_direction.lm | ||
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| #' @export | ||
| p_direction.merMod <- p_direction.lm | ||
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| #' @export | ||
| p_direction.wbm <- p_direction.lm | ||
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| #' @export | ||
| p_direction.lme <- p_direction.lm | ||
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| #' @export | ||
| p_direction.gee <- p_direction.lm | ||
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| #' @export | ||
| p_direction.gls <- p_direction.lm | ||
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| #' @export | ||
| p_direction.feis <- p_direction.lm | ||
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| #' @export | ||
| p_direction.felm <- p_direction.lm | ||
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| #' @export | ||
| p_direction.mixed <- p_direction.lm | ||
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| #' @export | ||
| p_direction.hurdle <- p_direction.lm | ||
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| #' @export | ||
| p_direction.zeroinfl <- p_direction.lm | ||
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| #' @export | ||
| p_direction.rma <- p_direction.lm |
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