StaffanBetner’s gists

StaffanBetner / to_clipboard.R

Created November 12, 2021 10:57

	to_clipboard <- function(x, row.names = F){
	write.table(x, "clipboard", row.names=row.names, sep='\t', na = "")
	}

StaffanBetner / ordered_beta_intercept_notebook.html

Created October 7, 2021 12:24

This file has been truncated, but you can view the full file.

	<!DOCTYPE html>

	<html>

	<head>

	<meta charset="utf-8" />
	<meta name="generator" content="pandoc" />
	<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />

StaffanBetner / induced_dirichlet_ordbeta.R

Last active October 7, 2021 12:04

	library(tidyverse)
	library(magrittr)
	library(brms)
	#library(betareg) # for example data
	#### Code declaring ordered beta family for brms ####
	# code from Robert Kubinec

	# custom family

	ord_beta_reg <- custom_family("ord_beta_reg",

StaffanBetner / names_born_sweden_2020.csv

Last active August 23, 2021 14:46

We can't make this file beautiful and searchable because it's too large.

	# Data source: Statistics Sweden
	# No data on county and municipality level if count < 3
	# No data on country level if count < 2
	"sex","county_code","county","municipality","municipality_code","name","count"
	"male",NA,NA,NA,NA,"Aadam",2
	"male",NA,NA,NA,NA,"Aadhiran",2
	"female",NA,NA,NA,NA,"Aadya",3
	"male",NA,NA,NA,NA,"Aahil",2
	"female",NA,NA,NA,NA,"Aaliya",2
	"female","12","Skåne län","Malmö","1280","Aaliyah",3

StaffanBetner / mgcv ocat dharma.R

Created June 23, 2021 19:53

	model.check.ocat <- function(model) {
	# Some R 4.1 syntax here!
	simulatedResponse = model %>%
	predict(type="response") %>%
	apply(1, \(x) sample(1:length(x), prob = x, size=1000, replace = TRUE)) %>%
	t()
	createDHARMa(
	simulatedResponse = simulatedResponse,
	observedResponse = model$y,
	fittedPredictedResponse = model %>% predict(), # the linear predictor

StaffanBetner / brms_dharma.R

Last active November 26, 2021 09:01

	#pacman::p_load(pacman, tidyverse, rio, brms, DHARMa)

	# inspired by https://frodriguezsanchez.net/post/using-dharma-to-check-bayesian-models-fitted-with-brms/


	# example code commented out, assumes that you have DHARMa and brms installed.
	# posterior_epred is more likely a better choice than posterior_linpred,
	# but the former does not work with ordinal models
	# due to being a multivariate model in disguise (which shares linear predictor)

StaffanBetner / sveriges_regenter.R

Created February 15, 2020 14:36

	library(pacman)

	p_load(tidyverse, gsheet, ggforce)
	timeline <- gsheet2tbl("https://docs.google.com/spreadsheets/d/1S6dWcSNKVQK3tZkt-PvA55LkM8ZuOJykDntECYjExhk/edit?usp=sharing")

	timeline %>% filter(Type=="Regentperiod") %>% select(Person, Tidsperiod) %>% distinct() -> tidsperioder

	tidsperioder %>% select(Tidsperiod) %>% distinct %>% pull(Tidsperiod) -> tidsperioder_vector

	timeline %>% filter(Type!="Regentperiod") %>% select(-Tidsperiod) %>% left_join(tidsperioder) %>%

StaffanBetner / meta-regression-sundell-2014.R

Last active February 11, 2020 09:24

	library(pacman)
	p_load(tidyverse, janitor, brms)
	tibble::tribble(~Year, ~OR, ~z,
	1765, 1.80, 1.69,
	1775, 2.41, 2.86,
	1790, 2.36, 2.67,
	1805, 2.58, 3.07,
	1820, 1.83, 2.63,
	1835, 1.55, 1.48,
	1850, 1.06, 0.14,

StaffanBetner / valresultat andel röstberättigade.R

Created August 20, 2019 21:04

	library(tidyverse)
	library(janitor)
	library(pxweb)
	library(scales)

	get_pxweb_data(url = "http://api.scb.se/OV0104/v1/doris/sv/ssd/ME/ME0104/ME0104C/ME0104T3",
	dims = list(Region = c('VR00'),
	Partimm = c('*'),
	ContentsCode = c('*'),
	Tid = c('*')),

StaffanBetner / mgcv_splines_simultanous.R

Last active August 22, 2019 15:06

	library(tidyverse)
	library(mgcv)

	splines_and_derivative <- function(gam_object, n_eval = 200, n_sim = 100, eps = 0.0000001) {
	number_of_smooths <- gam_object$smooth %>% length()
	data <- model.frame(gam_object)

	Vc <- vcov(gam_object,
	unconditional = TRUE
	)

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