Merge pull request #128 from s3alfisc/dev

Version 0.14.1

NAMESPACE

@@ -13,6 +13,8 @@ S3method(mboottest,lm)
 S3method(nobs,boottest)
 S3method(nobs,mboottest)
 S3method(plot,boottest)
+S3method(print,boottest)
+S3method(print,mboottest)
 S3method(pval,boottest)
 S3method(pval,mboottest)
 S3method(summary,boottest)

NEWS.md

@@ -1,3 +1,10 @@
+# fwildclusterboot 0.14.1
+
+- brings back the `print()` method, it had a use case after all
+- some fixes to the documentation
+
+
+
 # fwildclusterboot 0.14
 
 ## Breaking Changes

R/methods.R

@@ -751,68 +751,86 @@ nobs.mboottest <- function(object, ...) {
 }
 
 
-# S3 method to print key information for objects of type `boottest`
-# @param x object of type boottest
-# @param ... Further arguments passed to or from other methods.
-# @param digits Number of rounding digits
-# @export
-# @method print boottest
-# @return A scalar containing the effective number of observations
-# used in `mboottest`
-# @examples
-# #' requireNamespace("fwildclusterboot")
-# data(voters)
-# lm_fit <- lm(
-#   proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration,
-#   data = voters
-# )
-# boot <- boottest(lm_fit,
-#   B = 9999,
-#   param = "treatment",
-#   clustid = "group_id1"
-# )
-# print(boot)
-#
-# # print.boottest <- function(x, ..., digits = 4) {#   stopifnot(inherits(x, "boottest"))##   print(x$call)#   cat("", "\n")##   vals <- lapply(#     c("p_val", "conf_int", "t_stat"),#     function(y) {#       ifelse(is.null(x[[y]]),#         'not computed',#         round(x[[y]], digits = digits)#       )#     }#   )##   cat("p value:", vals[[1]], "\n")#   cat("confidence interval:", vals[[2]], "\n")#   cat("test statistic", vals[[3]], "\n")# }
-
-# S3 method to print key information for objects of type `mboottest`
-# @param x object of type mboottest
-# @param ... Further arguments passed to or from other methods.
-# @param digits Number of rounding digits
-# @export
-# @method print mboottest
-# @return A scalar containing the effective number of observations used
-# in `mboottest`
-# @examples
-# \dontrun{
-# requireNamespace("clubSandwich")
-# R <- clubSandwich::constrain_zero(2:3, coef(lm_fit))
-# wboottest <-
-#   mboottest(
-#     object = lm_fit,
-#     clustid = "group_id1",
-#     B = 999,
-#     R = R
-#   )
-# print(wboottest)
-# }
-#
-# print.mboottest <- function(x, ..., digits = 4) {
-#   stopifnot(inherits(x, "mboottest"))
-#
-#   print(x$call)
-#   cat("", "\n")
-#
-#   vals <- lapply(
-#     c("p_val", "teststat"),
-#     function(y) {
-#       round(x[[y]], digits = digits)
-#     }
-#   )
-#
-#   cat("p value:", vals[[1]], "\n")
-#   cat("test statistic", vals[[2]], "\n")
-# }
+
+#' S3 method to print key information for objects of type `bboottest`
+#' @param x object of type boottest
+#' @param ... Further arguments passed to or from other methods.
+#' @param digits Number of rounding digits
+#' @export
+#' @method print boottest
+#' @return A scalar containing the effective number of observations
+#' used in `mboottest`
+#' @examples
+#' #' requireNamespace("fwildclusterboot")
+#' data(voters)
+#' lm_fit <- lm(
+#'   proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration,
+#'   data = voters
+#' )
+#' boot <- boottest(lm_fit,
+#'   B = 9999,
+#'   param = "treatment",
+#'   clustid = "group_id1"
+#' )
+#' print(boot)
+#'
+print.boottest <- function(x, ..., digits = 4) {
+  stopifnot(inherits(x, "boottest"))
+
+  print(x$call)
+  cat("", "\n")
+
+  vals <- lapply(
+    c("p_val", "conf_int", "t_stat"),
+    function(y) {
+      round(x[[y]], digits = digits)
+    }
+  )
+
+  cat("p value:", vals[[1]], "\n")
+  cat("confidence interval:", vals[[2]], "\n")
+  cat("test statistic", vals[[3]], "\n")
+}
+
+
+#' S3 method to print key information for objects of type `mboottest`
+#' @param x object of type mboottest
+#' @param ... Further arguments passed to or from other methods.
+#' @param digits Number of rounding digits
+#' @export
+#' @method print mboottest
+#' @return A scalar containing the effective number of observations used
+#' in `mboottest`
+#' @examples
+#' \dontrun{
+#' requireNamespace("clubSandwich")
+#' R <- clubSandwich::constrain_zero(2:3, coef(lm_fit))
+#' wboottest <-
+#'   mboottest(
+#'     object = lm_fit,
+#'     clustid = "group_id1",
+#'     B = 999,
+#'     R = R
+#'   )
+#' print(wboottest)
+#' }
+#'
+print.mboottest <- function(x, ..., digits = 4) {
+  stopifnot(inherits(x, "mboottest"))
+
+  print(x$call)
+  cat("", "\n")
+
+  vals <- lapply(
+    c("p_val", "teststat"),
+    function(y) {
+      round(x[[y]], digits = digits)
+    }
+  )
+
+  cat("p value:", vals[[1]], "\n")
+  cat("test statistic", vals[[2]], "\n")
+}
 
 
 tidy.mboottest <- function(x, ...) {

vignettes/articles/Different-Variants-of-the-Wild-Cluster-Bootstrap.Rmd

@@ -17,7 +17,7 @@ Note that in a recent update of their working paper, MNW have renamed the new bo
 
 To run all bootstrap types with the null hypothesis imposed on the bootstrap data generating process, you would have to specify the `bootstrap_type` function argument: 
 
-```{r setup, warnings = FALSE}
+```{r setup, warnings = FALSE, message=FALSE}
 library(fwildclusterboot)
 library(modelsummary)
 options(modelsummary_factory_default = "gt")

vignettes/articles/Multiple-Estimations-with-fixest.Rmd

@@ -24,7 +24,7 @@ data(voters)
 
 ## Variables created via 'i()'
 
-```{r}
+```{r, warnings = FALSE, message=FALSE}
 feols_fit <- feols(proposition_vote ~ i(treatment, ideology1) ,
     data = voters
 )
@@ -42,7 +42,7 @@ boot1
 
 It is possible to loop through different regression specifications. For example, to run `boottest()` over a `fixest_multi` object created via the `sw()` function:
 
-```{r, warning = FALSE}
+```{r, warnings = FALSE, message=FALSE}
 feols_fits <- fixest::feols(
   proposition_vote ~ treatment | sw(Q1_immigration, Q2_defense),
   data = voters
@@ -62,7 +62,7 @@ boot2
 
 Similarly, it is possible to loop over objects of `fixest_multi` created by `subset` functionality: 
 
-```{r, warning = FALSE}
+```{r, warnings = FALSE, message=FALSE}
 voters$split <- sample(1:2, nrow(voters), TRUE)
 
 feols_fits <- fixest::feols(
@@ -86,14 +86,14 @@ boot3
 
 And of course it is also possible to combine multiple "syntactic sugar" features of `fixest`:
 
-```{r, warning = FALSE}
+```{r, warnings = FALSE, message=FALSE}
 feols_fits <- fixest::feols(
   proposition_vote ~ treatment | sw(Q1_immigration, Q2_defense), 
   split = ~split,
   data = voters
 )
 
-boot_4 <- lapply(
+boot4 <- lapply(
   feols_fits, 
   \(x) boottest(
     x, 
@@ -103,14 +103,16 @@ boot_4 <- lapply(
     )
 )  
 
-boot_4
+boot4
 ```
 
 ## What currently is not supported
 
+As a rule of thumb, every transformation of fixed effects is not supported. This includes interaction via `^` and varying slopes syntax via `var1[var2]`. If you try this, you should receive an error message, and if not, please open an issue on github =) .
+
 Fixed effect interactions via `^` lead to an error. Fixing this is on my backlog but currently not highest priortiy. Send me a message if this causes you large headaches! 
 
-```{r, warning = FALSE, eval = FALSE}
+```{r, warnings = FALSE, message=FALSE, eval = FALSE}
 feols_fit2 <- feols(proposition_vote ~ treatment | Q1_immigration^Q2_defense,
     data = voters
 )
@@ -121,8 +123,30 @@ boot1 <- boottest(feols_fit2,
     param = "treatment",
     clustid = "group_id1"
 )
+# Error in `check_boottest_args_plus()` at fwildclusterboot/R/boottest_fixest.R:514:2:
+# ! Advanced formula notation in fixest / fixest via ^ to interact
+# fixed effects is currently not supported in boottest().
+
 ```
 
+Relatedly, varying slopes fixed effects syntax is not supported: 
 
+```{r, warnings = FALSE, message=FALSE, eval = FALSE}
+feols_fit3 <- feols(proposition_vote ~ treatment | Q1_immigration[log_income],
+    data = voters
+)
+
+# leads to an error
+boot <- boottest(feols_fit3,
+    B = 9999,
+    param = "treatment",
+    clustid = "group_id1"
+)
+# Error in `check_boottest_args_plus()` at fwildclusterboot/R/boottest_fixest.R:514:2:
+# ! Varying slopes in fixest / fixest via [] to interact
+# fixed effects is currently not supported in boottest().
+
+
+```
 
 

vignettes/articles/wild_bootstrap.Rmd

@@ -209,14 +209,15 @@ While inverting bootstrap tests is computationally more demanding, in the case o
 
 #### Example: How to compute a CI by test inversion
 
-Suppose we have a parameter $\theta \in \Theta$ and a test statistic $T(\theta)$. We also have a critical value $c$ so that to test the null hypothesis $H_0: \theta = \theta_0$ against $H_1: \theta \neq \theta_0$ at a level $\alpha$, we reject the null hypothesis if $T(\theta_0) > c$.
+<!-- Suppose we have a parameter $\theta \in \Theta$ and a test statistic $T(\theta)$. We also have a critical value $c$ so that to test the null hypothesis $H_0: \theta = \theta_0$ against $H_1: \theta \neq \theta_0$ at a level $\alpha$, we reject the null hypothesis if $T(\theta_0) > c$. -->
 
-Let's define a set that includes all values of $\theta$ for which the decision rule "reject the null hypothesis if $T(\theta)\geq c$" leads to an acceptance of the null hypothesis:
+<!-- Let's define a set that includes all values of $\theta$ for which the decision rule "reject the null hypothesis if $T(\theta)\geq c$" leads to an acceptance of the null hypothesis: -->
 
-$$
-  C = \{\theta \in \Theta: T(\theta)\leq c \}.
-$$
-Based on the definition of the p-value, we can alternatively define a confidence interval at significance level $\alpha$ as 
+<!-- $$ -->
+<!--   C = \{\theta \in \Theta: T(\theta)\leq c \}. -->
+<!-- $$ -->
+
+Based on the definition of the p-value, we can define a confidence interval at significance level $\alpha$ as 
 
 $$
   C = \{\theta \in \Theta: p(\theta) \geq \alpha \}.