Support glmtoolbox::glmgee() (#1207)

* support glmtoolbox::glmgee()

* fixup test

* test fixup

* supported models

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: marginaleffects
 Title: Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests
-Version: 0.21.0.21
+Version: 0.21.0.22
 Authors@R: 
     c(person(given = "Vincent",
              family = "Arel-Bundock",
@@ -92,6 +92,7 @@ Suggests:
     ggplot2,
     ggrepel,
     glmmTMB,
+    glmtoolbox,
     glmx,
     haven,
     here,
@@ -237,6 +238,7 @@ Collate:
     'methods_flexsurv.R'
     'methods_gamlss.R'
     'methods_glmmTMB.R'
+    'methods_glmtoolbox.R'
     'methods_glmx.R'
     'methods_lme4.R'
     'methods_mclogit.R'

NAMESPACE

@@ -14,6 +14,7 @@ S3method(get_coef,data.frame)
 S3method(get_coef,default)
 S3method(get_coef,gam)
 S3method(get_coef,gamlss)
+S3method(get_coef,glmgee)
 S3method(get_coef,glmmTMB)
 S3method(get_coef,lmerMod)
 S3method(get_coef,lmerModLmerTest)
@@ -62,6 +63,7 @@ S3method(get_predict,flexsurvreg)
 S3method(get_predict,gamlss)
 S3method(get_predict,glimML)
 S3method(get_predict,glm)
+S3method(get_predict,glmgee)
 S3method(get_predict,glmmPQL)
 S3method(get_predict,glmmTMB)
 S3method(get_predict,gsm)
@@ -143,6 +145,7 @@ S3method(set_coef,flexsurvreg)
 S3method(set_coef,gamlss)
 S3method(set_coef,glimML)
 S3method(set_coef,glm)
+S3method(set_coef,glmgee)
 S3method(set_coef,glmmPQL)
 S3method(set_coef,glmmTMB)
 S3method(set_coef,glmx)

NEWS.md

@@ -7,13 +7,17 @@ Breaking changes:
 * `type="invlink(link)"` is no longer default in `avg_predictions()` or when calling `predictions()` with the `by` argument. It is still default in `predictions()` without the `by` argument. The backtransform strategy is still available with by setting `type="invlink(link)"` explicitly.
 * The `type` argument in `plot_comparisons()` now defaults to `NULL`, which is now consistent with `comparisons()` and `avg_comparisons()`. Before, the default was `type="response"`. Thanks to @giakhang1906 for report #1202.
 
-New:
+New models supported:
+
+* `stpm2`, `pstpm2`, `gsm`, and `aft` models from `rstpm2`. Thanks to @aghaynes and @mclements.
+* `glm_weightit`, `coxph_weightit`, `multinom_weightit`, and `ordinal_weightit` models from `Weightit`. Thanks to @ngreifer.
+* `glmmgee` from the `glmtoolbox` package. Thanks to @adrianolszewski for the request and @lhvanegasp for help with implementation.
+
+New feautres:
 
-* `hypotheses(joint=TRUE)`: do not call `stats::nobs()` unless necessary.
-* Added support for `stpm2`, `pstpm2`, `gsm`, and `aft` models from `rstpm2`. Thanks to @aghaynes and @mclements.
-* Added support for `glm_weightit`, `coxph_weightit`, `multinom_weightit`, and `ordinal_weightit` models from `Weightit`. Thanks to @ngreifer.
 * Parallel computation with `future` is more efficient by chunking tasks to avoid passing large objects to every worker for every future. Issue #1158.
 * All columns of `newdata` are passed to the `hypothesis` function when `newdata` is supplied explicitly. Thanks to @gravesti for report #1175.
+* `hypotheses(joint=TRUE)`: do not call `stats::nobs()` unless necessary.
 * `hypotheses()` supports formulas in the `hypothesis` argument: `hypotheses(model, hypothesis = ratio ~ reference)`
 * Global option: `options("marginaleffects_print_omit" = "s.value")`
 * Round significant digits for labels in `plot_predictions(mod, condition = list(x = "fivenum"))`

R/methods_glmtoolbox.R

@@ -0,0 +1,29 @@
+#' @include get_coef.R
+#' @rdname get_coef
+#' @export
+get_coef.glmgee <- function(model, ...) {
+    b <- model$coefficients
+    b <- setNames(as.vector(b), row.names(b))
+    return(b)
+}
+
+#' @include set_coef.R
+#' @rdname set_coef
+#' @export
+set_coef.glmgee <- function(model, coefs, ...) {
+    out <- model
+    idx <- match(row.names(out$coefficients), names(coefs))
+    out$coefficients[, 1] <- coefs[idx]
+    return(out)
+}
+
+#' @include get_predict.R
+#' @rdname get_predict
+#' @export
+get_predict.glmgee <- function(model, newdata, ...) {
+    Yhat <- predict(model, newdata = newdata, type = "response")
+    out <- data.frame(
+        rowid = seq_len(nrow(Yhat)),
+        estimate = as.vector(Yhat))
+    return(out)
+}

R/sanity_model.R

@@ -58,6 +58,7 @@ sanity_model_supported_class <- function(model) {
         "glmerMod",
         "glmrob",
         "glmmTMB",
+        "glmgee",
         c("glmmPQL", "lme"),
         "glimML",
         "glmx",

R/type_dictionary.R

@@ -85,6 +85,7 @@ glm,response
 glm,link
 glmerMod,response
 glmerMod,link
+glmgee,response
 glmrob,response
 glmrob,link
 glmmTMB,response

data-raw/supported_models.csv

@@ -42,6 +42,7 @@ gam,gam,TRUE,TRUE,,,U,U,TRUE,TRUE
 gamlss,gamlss,TRUE,TRUE,,,U,U,TRUE,TRUE
 geepack,geeglm,TRUE,TRUE,,,U,U,TRUE,TRUE
 glmmTMB,glmmTMB,TRUE,FALSE,,,U,U,TRUE,TRUE
+glmtoolbox,glmgee,TRUE,TRUE,,, ,,,
 glmx,glmx,TRUE,TRUE,,,TRUE,U,U,U
 ivreg,ivreg,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,U,U
 lme4,glmer,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE

inst/tinytest/test-pkg-glmtoolbox.R

@@ -0,0 +1,14 @@
+source("helpers.R")
+using("marginaleffects")
+requiet("glmtoolbox")
+
+data(spruces)
+mod <- size ~ poly(days,4) + treat
+fit <- glmgee(mod, id=tree, family=Gamma(log), corstr="AR-M-dependent(1)", data=spruces)
+
+s <- avg_slopes(fit)
+expect_inherits(s, "slopes")
+p <- avg_predictions(fit, by = "treat")
+expect_inherits(p, "predictions")
+k <- avg_comparisons(fit)
+expect_inherits(k, "comparisons")