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@vincentarelbundock
vincentarelbundock / maringe_2020.R
Last active December 9, 2023 02:50
Maringe et al. 2020
# Downloads data here:
# https://academic.oup.com/ije/article/49/5/1719/5835351#226179197
#############################################################################
# TRIAL EMULATION: SURGERY WITHIN 6 MONTHS AMONG OLDER LUNG CANCER PATIENTS
# Author: Clemence Leyrat (from Camille Maringe's Stata code)
# Refactored by Vincent Arel-Bundock on 2023-12-08
# Example of examining a continuous x categorical interaction using emmeans,
# and an attempt at doing the same using marginaleffects.
# Author: Cameron Patrick <cameron.patrick@unimelb.edu.au>
library(tidyverse)
library(emmeans)
library(marginaleffects)
# use the mtcars data, set up am as a factor
data(mtcars)
``` r
library(marginaleffects)
library(modelsummary)
set.seed(20220722)
S <- diag(3)
S[1,2] <- S[2,1] <- 0.6
S[1,3] <- S[3,1] <- 0.6
data <- MASS::mvrnorm(1000, rep(0, 3), S) |>
as.data.frame() |>
@vincentarelbundock
vincentarelbundock / bboot_vab.R
Last active February 7, 2022 21:38
Bayesian bootstrap in `R` with `update()`
library(fixest)
library(data.table)
library(insight)
bboot_vab <- function(model, reps = 1e3, conf.level = .95, cluster = NULL) {
data <- insight::get_data(model)
setDT(data)
if (!is.null(cluster)) {
if (anyNA(data[[cluster]])) {
stop("The cluster variable cannot include missing values.")
# French Accents
name: french-finnish-accents
parent: default
matches:
- trigger: "e''"
replace: "é"
- trigger: "e``"
@vincentarelbundock
vincentarelbundock / modelsummary.ipynb
Last active April 20, 2021 14:28
`modelsummary` regression tables in a Jupyter notebook
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@vincentarelbundock
vincentarelbundock / rdrobus_modelsummary.R
Created September 21, 2020 16:58
rdrobust & modelsummary
library(rdrobust)
library(modelsummary)
tidy.rdrobust <- function(object, ...){
ret <- data.frame(term = row.names(object$coef),
estimate = object$coef[, 1],
std.error = object$se[, 1],
statistic = object$z[, 1],
p.value = object$pv[, 1],
conf.low = object$ci[,1],
library(data.table)
library(tidyverse)
library(microbenchmark)
make_tibble <- function(...) {
treated_units <- sample(1:20000, 4000)
# year fixed effects
year <- tibble(
year = 1981:2010,
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