Add leo_regresult()

DESCRIPTION

 Package: leo
 Type: Package
 Title: Useful functions for LEO surveys
-Version: 0.2.6
+Version: 0.2.7
 Author: Gregor Dutz
 Maintainer: Gregor Dutz <gregor.dutz@uni-hamburg.de>
 Description: Implements simple functions to handle data from two German surveys 
@@ -15,9 +15,11 @@ RoxygenNote: 7.1.1
 Imports: 
     dplyr,
     labelled,
+    mitml,
     magrittr,
     mitools,
     purrr,
     rlang,
     survey,
-    tidyselect
+    tidyselect,
+    tibble

NAMESPACE

 # Generated by roxygen2: do not edit by hand
 
+export(leo_regresult)
 export(leo_svydesign)
 importFrom(rlang,.data)

R/leo_regresult.R

+#' Creates Tiny Data from regression results from svyglm/mitools.
+#'
+#' @param model A list of model estimates (fitted with with.imputationList).
+#' @param conf.int A logical flag indicating if the confidence intervall should be calculated. Default is `FALSE`.
+#' @param conf.level The confidence level. Default is `0.95`.
+#' @param exponentiate A logical flag indicating if coefficients should be exponentiated (only useful for logistic regression). Default is `FALSE`.
+#' @return A tibble with regression results as Tiny Data.
+#'
+#' @importFrom rlang .data
+#' @export
+leo_regresult <- function(model, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE) {
+
+  ret <- tibble::as_tibble(mitml::testEstimates(model)$estimates, rownames = "term")
+  ret <- dplyr::select(ret, -.data$df, -.data$RIV, -.data$FMI)
+  colnames(ret) <- c("term", "estimate", "std.error", "statistic", "p.value")
+
+  if (conf.int) {
+    ci <- tibble::as_tibble(mitml::confint.mitml.testEstimates(mitml::testEstimates(model, level = conf.level)), rownames = "term")
+    colnames(ci) <- c("term", "conf.low", "conf.high")
+    ret <- dplyr::left_join(ret, ci, by = "term")
+  }
+
+  if (exponentiate)
+    ret <- leo_exponentiate(ret)
+
+  ret
+}
+
+#' Exponentiates regression results from logistic regressions
+#'
+#' @param model A list of model estimates as Tiny Data.
+#' @return A tibble with expontiated regression results as Tiny Data.
+#'
+#' @importFrom rlang .data
+#' @keywords internal
+leo_exponentiate <- function(model) {
+
+  model <- model |> dplyr::mutate(estimate = exp(.data$estimate)) # exponentiate coefficients
+
+  if ("conf.low" %in% colnames(model)) # exponentiate confidence interval (if present)
+    model <- model |> dplyr::mutate(dplyr::across(c(.data$conf.low, .data$conf.high), exp))
+
+  model
+}