Updated gvcov to vcovG (#52)

NAMESPACE

@@ -12,7 +12,6 @@ S3method(treatment_effect,prediction_cf)
 S3method(vcovHC,prediction_cf)
 export(bias)
 export(find_data)
-export(gvcov)
 export(h_diff)
 export(h_jac_diff)
 export(h_jac_odds_ratio)
@@ -22,6 +21,7 @@ export(h_ratio)
 export(predict_counterfactual)
 export(robin_glm)
 export(treatment_effect)
+export(vcovG)
 import(checkmate)
 importFrom(MASS,negative.binomial)
 importFrom(numDeriv,jacobian)

R/robin_glm.R

@@ -8,7 +8,7 @@
 #' @param contrast_jac (`function`) A function to calculate the Jacobian of the contrast function. Ignored if using
 #' default contrasts.
 #' @param vcov (`function`) A function to calculate the variance-covariance matrix of the treatment effect,
-#' including `vcovHC` and `gvcov`.
+#' including `vcovHC` and `vcovG`.
 #' @param family (`family`) A family object of the glm model.
 #' @param vcov_args (`list`) Additional arguments passed to `vcov`.
 #' @param ... Additional arguments passed to `glm` or `glm.nb`.
@@ -23,7 +23,7 @@
 #' )
 robin_glm <- function(
     formula, data, treatment, contrast = "difference",
-    contrast_jac = NULL, vcov = "gvcov", family = gaussian(), vcov_args = list(), ...) {
+    contrast_jac = NULL, vcov = "vcovG", family = gaussian(), vcov_args = list(), ...) {
   attr(formula, ".Environment") <- environment()
   # check if using negative.binomial family with NA as theta.
   # If so, use MASS::glm.nb instead of glm.

R/treatment_effect.R

@@ -16,7 +16,7 @@ treatment_effect <- function(object, pair, variance, eff_measure, eff_jacobian,
 
 #' @export
 treatment_effect.prediction_cf <- function(
-    object, pair = names(object), variance = "gvcov", eff_measure, eff_jacobian, vcov_args = list(), ...) {
+    object, pair = names(object), variance = "vcovG", eff_measure, eff_jacobian, vcov_args = list(), ...) {
   assert(
     test_string(variance),
     test_function(variance),
@@ -75,15 +75,15 @@ treatment_effect.prediction_cf <- function(
 #' @export
 #' @inheritParams predict_counterfactual
 treatment_effect.lm <- function(
-    object, pair, variance = "gvcov", eff_measure, eff_jacobian,
+    object, pair, variance = "vcovG", eff_measure, eff_jacobian,
     vcov_args = list(), treatment, data = find_data(object), ...) {
   pc <- predict_counterfactual(object, data = data, treatment)
   treatment_effect(pc, pair = pair, variance = variance, eff_measure = eff_measure, eff_jacobian = eff_jacobian, ...)
 }
 
 #' @export
 treatment_effect.glm <- function(
-    object, pair, variance = "gvcov", eff_measure, eff_jacobian,
+    object, pair, variance = "vcovG", eff_measure, eff_jacobian,
     vcov_args = list(), treatment, data = find_data(object), ...) {
   pc <- predict_counterfactual(object, treatment, data)
   treatment_effect(pc, pair = pair, variance = variance, eff_measure = eff_measure, eff_jacobian = eff_jacobian, ...)

R/variance_anhecova.R

@@ -6,7 +6,7 @@
 #'
 #' @return Named covariance matrix.
 #' @export
-gvcov <- function(x, decompose = TRUE, ...) { # nolint
+vcovG <- function(x, decompose = TRUE, ...) { # nolint
   assert_class(x, "prediction_cf")
   assert_flag(decompose)
   resi <- attr(x, "residual")

tests/testthat/_snaps/treatment_effect.md

@@ -18,7 +18,7 @@
       0.03359913 0.03441801 0.03401864 
       
       
-      Variance Type:  gvcov 
+      Variance Type:  vcovG 
                   Estimate Std.Err Z Value Pr(>|z|)    
       trt1 - pbo    0.2246  0.0477    4.71  2.5e-06 ***
       trt2 - pbo    0.2653  0.0475    5.58  2.4e-08 ***
@@ -47,7 +47,7 @@
       0.06768998 0.07592944 0.07654319 
       
       
-      Variance Type:  gvcov 
+      Variance Type:  vcovG 
                   Estimate Std.Err Z Value Pr(>|z|)    
       trt1 - pbo     0.564   0.101    5.60  2.2e-08 ***
       trt2 - pbo     0.771   0.101    7.61  2.8e-14 ***
@@ -98,7 +98,7 @@
       0.03359913 0.03441801 
       
       
-      Variance Type:  gvcov 
+      Variance Type:  vcovG 
                  Estimate Std.Err Z Value Pr(>|z|)    
       trt1 - pbo   0.2246  0.0477    4.71  2.5e-06 ***
       ---

tests/testthat/_snaps/variance.md

@@ -18,10 +18,10 @@
       trt1  4.523445e-07  1.164889e-03 -7.709031e-07
       trt2 -9.709004e-06 -7.709031e-07  1.170214e-03
 
-# gvcov works
+# vcovG works
 
     Code
-      gvcov(pc)
+      vcovG(pc)
     Output
                     pbo         trt1         trt2
       pbo  1.128902e-03 1.856234e-05 1.333885e-05
@@ -31,7 +31,7 @@
 ---
 
     Code
-      gvcov(pc)
+      vcovG(pc)
     Output
                     pbo         trt1         trt2
       pbo  1.128902e-03 1.856234e-05 1.333885e-05
@@ -41,7 +41,7 @@
 ---
 
     Code
-      gvcov(pc)
+      vcovG(pc)
     Output
                     pbo         trt1         trt2
       pbo  1.128902e-03 1.856234e-05 1.333885e-05
@@ -51,7 +51,7 @@
 ---
 
     Code
-      gvcov(pc, decompose = FALSE)
+      vcovG(pc, decompose = FALSE)
     Output
                     pbo         trt1         trt2
       pbo  1.127076e-03 1.856234e-05 1.333885e-05
@@ -61,7 +61,7 @@
 ---
 
     Code
-      gvcov(pc)
+      vcovG(pc)
     Output
                     pbo         trt1         trt2
       pbo  1.128902e-03 1.856234e-05 1.333885e-05

tests/testthat/test-variance.R

@@ -9,24 +9,24 @@ test_that("vcovHC works", {
   )
 })
 
-test_that("gvcov works", {
+test_that("vcovG works", {
   pc <- predict_counterfactual(fit_binom, treatment ~ s1)
   expect_snapshot(
-    gvcov(pc)
+    vcovG(pc)
   )
   pc <- predict_counterfactual(fit_binom, treatment ~ 1)
   expect_snapshot(
-    gvcov(pc)
+    vcovG(pc)
   )
   pc <- predict_counterfactual(fit_binom, treatment ~ pb(s1))
   expect_snapshot(
-    gvcov(pc)
+    vcovG(pc)
   )
   expect_snapshot(
-    gvcov(pc, decompose = FALSE)
+    vcovG(pc, decompose = FALSE)
   )
   pc <- predict_counterfactual(fit_binom, treatment ~ ps(s1))
   expect_snapshot(
-    gvcov(pc)
+    vcovG(pc)
   )
 })