OR_to_RR for GLM

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: effectsize
 Title: Indices of Effect Size and Standardized Parameters
-Version: 0.5-1
+Version: 0.5-2
 Authors@R: 
     c(person(given = "Mattan S.",
              family = "Ben-Shachar",

NAMESPACE

@@ -21,6 +21,8 @@ S3method(interpret,performance_lavaan)
 S3method(interpret_parameters,lm)
 S3method(odds_to_probs,data.frame)
 S3method(odds_to_probs,numeric)
+S3method(oddsratio_to_riskratio,default)
+S3method(oddsratio_to_riskratio,glm)
 S3method(plot,effectsize_table)
 S3method(plot,equivalence_test_effectsize)
 S3method(print,effectsize_anova)
@@ -215,6 +217,7 @@ importFrom(stats,aov)
 importFrom(stats,as.formula)
 importFrom(stats,ave)
 importFrom(stats,chisq.test)
+importFrom(stats,coef)
 importFrom(stats,complete.cases)
 importFrom(stats,contrasts)
 importFrom(stats,delete.response)

NEWS.md

@@ -7,6 +7,7 @@
 
 ## New features
 
+- `oddsratio_to_riskratio()` can now convert OR coefficients to RR coefficients from a logistic GLM. 
 - All effect-size functions gain an `alternative` argument which can be used to make one- or two-sided CIs.
 - `interpret()` now accepts as input the results from `cohens_d()`, `eta_squared()`, `rank_biserial()`, etc.
 - `interpret_pd()` for the interpretation of the [*Probability of Direction*](https://easystats.github.io/bayestestR/reference/p_direction.html).

R/convert_between_OR_to_RR.R

@@ -1,11 +1,13 @@
 #' Convert between Odds ratios and Risk ratios
 #'
 #' @param OR,RR Risk ratio of `p1/p0` or Odds ratio of `odds(p1)/odds(p0)`,
-#'   possibly log-ed.
+#'   possibly log-ed. `OR` can also be a logistic regression model.
 #' @param p0 Baseline risk
+#' @param ... Arguments passed to and from other methods.
 #' @inheritParams oddsratio_to_d
 #'
-#' @return Converted index.
+#' @return Converted index, or if `OR` is a logistic regression model, a
+#'   parameter table with the converted indices.
 #'
 #' @family convert between effect sizes
 #'
@@ -18,14 +20,23 @@
 #'
 #' riskratio_to_oddsratio(RR, p0 = p0)
 #' oddsratio_to_riskratio(OR, p0 = p0)
+#'
+#' m <- glm(am ~ factor(cyl), data = mtcars,
+#'          family = binomial())
+#' oddsratio_to_riskratio(m)
 #' @references
 #'
 #' Grant, R. L. (2014). Converting an odds ratio to a range of plausible
 #' relative risks for better communication of research findings. Bmj, 348,
 #' f7450.
 #'
 #' @export
-oddsratio_to_riskratio <- function(OR, p0, log = FALSE) {
+oddsratio_to_riskratio <- function(OR, p0, log = FALSE, ...) {
+  UseMethod("oddsratio_to_riskratio")
+}
+
+#' @export
+oddsratio_to_riskratio.default <- function(OR, p0, log = FALSE, ...) {
   if (log) OR <- exp(OR)
 
   RR <- OR / (1 - p0 + (p0 * OR))
@@ -34,6 +45,41 @@ oddsratio_to_riskratio <- function(OR, p0, log = FALSE) {
   return(RR)
 }
 
+#' @export
+#' @importFrom stats coef
+oddsratio_to_riskratio.glm <- function(OR, p0, log = FALSE, ...) {
+  mi <- insight::model_info(OR)
+  if (!mi$is_binomial || !mi$is_logit) stop("Model must a binomial model with logit-link (logistic regression)")
+
+  if (used_intercept <- missing(p0)) {
+    p0 <- plogis(stats::coef(OR)["(Intercept)"])
+    warning("'p0' not provided.",
+            "RR is relative to the intercept (p0 = ",
+            insight::format_value(p0),
+            ") - make sure your intercept is meaningful.")
+  }
+
+  RR <- parameters::model_parameters(OR, exponentiate = !log, ...)
+  RR$SE <- NULL
+  RR$z <- NULL
+  RR$df_error <- NULL
+  RR$p <- NULL
+
+  RR[,colnames(RR) %in% c("Coefficient", "CI_low", "CI_high")] <-
+    lapply(RR[,colnames(RR) %in% c("Coefficient", "CI_low", "CI_high")],
+           oddsratio_to_riskratio, p0 = p0, log = log)
+
+  RR[RR$Parameter=="(Intercept)", "Coefficient"] <- p0
+  RR[RR$Parameter=="(Intercept)", c("CI_low", "CI_high")] <- NA
+
+  if (!used_intercept) {
+    RR[RR$Parameter=="(Intercept)", "Parameter"] <- "(p0)"
+  }
+
+  attr(RR, "coefficient_name") <- if (log) "Log-RR" else "Risk Ratio"
+  return(RR)
+}
+
 
 
 #' @rdname oddsratio_to_riskratio
@@ -46,3 +92,4 @@ riskratio_to_oddsratio <- function(RR, p0, log = FALSE) {
   if (log) OR <- log(OR)
   return(OR)
 }
+

tests/testthat/test-convert_between.R

@@ -28,6 +28,20 @@ if (require("testthat") && require("effectsize")) {
 
     expect_equal(riskratio_to_oddsratio(log(RR), p0 = p0, log = TRUE), log(OR))
     expect_equal(oddsratio_to_riskratio(log(OR), p0 = p0, log = TRUE), log(RR))
+
+
+    m <- glm(am ~ factor(cyl), data = mtcars,
+             family = binomial())
+
+    expect_warning(RR <- oddsratio_to_riskratio(m))
+    expect_true("(Intercept)" %in% RR$Parameter)
+    expect_false("(p0)" %in% RR$Parameter)
+    expect_equal(RR$Coefficient, c(0.7272, 0.5892, 0.1964), tolerance = 0.001)
+
+    RR <- oddsratio_to_riskratio(m, p0 = 0.05)
+    expect_true("(p0)" %in% RR$Parameter)
+    expect_false("(Intercept)" %in% RR$Parameter)
+    expect_equal(RR$Coefficient, c(0.05, 0.29173, 0.06557), tolerance = 0.001)
   })
 
   test_that("odds_to_probs", {