update printing and clean deps (#51)

* update printing and clean deps

* [skip style] [skip vbump] Restyle files

* Empty

---------

Co-authored-by: github-actions <41898282+github-actions[bot]@users.noreply.github.com>

DESCRIPTION

@@ -22,7 +22,6 @@ Imports:
     checkmate,
     numDeriv,
     MASS,
-    prediction,
     sandwich,
     stats
 Suggests:

NAMESPACE

@@ -1,5 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(find_data,glm)
+S3method(find_data,lm)
 S3method(predict_counterfactual,glm)
 S3method(predict_counterfactual,lm)
 S3method(print,prediction_cf)
@@ -9,6 +11,7 @@ S3method(treatment_effect,lm)
 S3method(treatment_effect,prediction_cf)
 S3method(vcovHC,prediction_cf)
 export(bias)
+export(find_data)
 export(gvcov)
 export(h_diff)
 export(h_jac_diff)
@@ -22,7 +25,6 @@ export(treatment_effect)
 import(checkmate)
 importFrom(MASS,negative.binomial)
 importFrom(numDeriv,jacobian)
-importFrom(prediction,find_data)
 importFrom(sandwich,vcovHC)
 importFrom(stats,as.formula)
 importFrom(stats,coefficients)

R/RobinCar2-package.R

@@ -11,5 +11,4 @@
 #' gaussian terms glm var family pnorm var as.formula
 #' @importFrom sandwich vcovHC
 #' @importFrom MASS negative.binomial
-#' @importFrom prediction find_data
 NULL

R/find_data.R

@@ -0,0 +1,15 @@
+#' Find Data in a Fit
+#' @export
+#' @param fit A fit object.
+#' @param ... Additional arguments.
+find_data <- function(fit, ...) {
+  UseMethod("find_data")
+}
+#' @export
+find_data.glm <- function(fit, ...) {
+  fit$data
+}
+#' @export
+find_data.lm <- function(fit, ...) {
+  stop("data must be provided explicitly for lm objects")
+}

R/robin_glm.R

@@ -27,6 +27,8 @@ robin_glm <- function(
   attr(formula, ".Environment") <- environment()
   # check if using negative.binomial family with NA as theta.
   # If so, use MASS::glm.nb instead of glm.
+  assert_subset(all.vars(formula), names(data))
+  assert_subset(all.vars(treatment), names(data))
   if (identical(family$family, "Negative Binomial(NA)")) {
     fit <- MASS::glm.nb(formula, data = data, ...)
   } else {

R/treatment_effect.R

@@ -53,6 +53,7 @@ treatment_effect.prediction_cf <- function(
     }
     trt_var <- trt_jac %*% inner_variance %*% t(trt_jac)
   } else {
+    inner_variance <- NULL
     trt_var <- diag(NULL)
   }
 
@@ -63,6 +64,7 @@ treatment_effect.prediction_cf <- function(
     marginal_mean = object,
     fit = attr(object, "fit"),
     vartype = variance_name,
+    mmvariance = inner_variance,
     treatment = attr(object, "treatment_formula"),
     variance = diag(trt_var),
     class = "treatment_effect"
@@ -183,7 +185,12 @@ print.treatment_effect <- function(x, ...) {
   cat("Randomization: ", deparse(attr(x, "treatment")), "\n")
   cat("Marginal Mean: \n")
   print(attr(x, "marginal_mean"))
-
+  if (!identical(attr(x, "vartype"), "none")) {
+    v <- attr(x, "mmvariance")
+    cat("Marginal Mean Variance: \n")
+    print(sqrt(diag(v)))
+    cat("\n\n")
+  }
   cat("Variance Type: ", attr(x, "vartype"), "\n")
   if (identical(attr(x, "vartype"), "none")) {
     trt_sd <- rep(NA, length(x))

tests/testthat/_snaps/treatment_effect.md

@@ -13,6 +13,11 @@
             pbo      trt1      trt2 
       0.3560965 0.5806957 0.6213865 
       
+      Marginal Mean Variance: 
+             pbo       trt1       trt2 
+      0.03359913 0.03441801 0.03401864 
+      
+      
       Variance Type:  gvcov 
                   Estimate Std.Err Z Value Pr(>|z|)    
       trt1 - pbo    0.2246  0.0477    4.71  2.5e-06 ***
@@ -24,7 +29,8 @@
 ---
 
     Code
-      treatment_effect(fit_lm, treatment = treatment ~ s1, eff_measure = h_diff)
+      treatment_effect(fit_lm, treatment = treatment ~ s1, eff_measure = h_diff,
+      data = dummy_data)
     Output
       Treatment Effect
       -------------
@@ -36,6 +42,11 @@
             pbo      trt1      trt2 
       0.2003208 0.7639709 0.9712499 
       
+      Marginal Mean Variance: 
+             pbo       trt1       trt2 
+      0.06768998 0.07592944 0.07654319 
+      
+      
       Variance Type:  gvcov 
                   Estimate Std.Err Z Value Pr(>|z|)    
       trt1 - pbo     0.564   0.101    5.60  2.2e-08 ***
@@ -82,6 +93,11 @@
             pbo      trt1      trt2 
       0.3560965 0.5806957 0.6213865 
       
+      Marginal Mean Variance: 
+             pbo       trt1 
+      0.03359913 0.03441801 
+      
+      
       Variance Type:  gvcov 
                  Estimate Std.Err Z Value Pr(>|z|)    
       trt1 - pbo   0.2246  0.0477    4.71  2.5e-06 ***

tests/testthat/test-find_data.R

@@ -0,0 +1,13 @@
+test_that("find_data works for glm", {
+  expect_identical(
+    find_data(fit_glm),
+    fit_glm$data
+  )
+})
+
+test_that("find_data fails for lm", {
+  expect_error(
+    find_data(fit_lm),
+    "data must be provided explicitly for lm objects"
+  )
+})

tests/testthat/test-treatment_effect.R

@@ -147,7 +147,7 @@ test_that("treatment_effect works as expected for custom contrast", {
 
 test_that("treatment_effect works for lm/glm object", {
   expect_snapshot(treatment_effect(fit_binom, treatment = treatment ~ s1, eff_measure = h_diff))
-  expect_snapshot(treatment_effect(fit_lm, treatment = treatment ~ s1, eff_measure = h_diff))
+  expect_snapshot(treatment_effect(fit_lm, treatment = treatment ~ s1, eff_measure = h_diff, data = dummy_data))
 })
 
 test_that("treatment_effect works if variance is not used", {