jirilukavsky’s gists

jirilukavsky / recovery_pois_binomial.R

Created June 26, 2024 07:23

Testing how accurate is parameter recovery for Poisson and binomial models for a large N=1000. Combinations of zeros (0 hits of 0 trials) do not pose a problem.

	library(tidyverse)
	library(brms)
	library(tidybayes)

	n <- 1000
	lambda <- 2
	p <- 0.50

	x <- rpois(n, lambda)
	y <- rbinom(n, x, p)

jirilukavsky / murdock.R

Last active June 17, 2024 07:18

Reconstruction of the distinctiveness measure from Murdock 1960 paper

	murdock_D <- function(x, pc = T) {
	n <- length(x)
	mr <- matrix(x, nrow = n, ncol = n)
	mc <- matrix(x, nrow = n, ncol = n, byrow = T)
	md <- abs(mr - mc)
	td <- rowSums(md)
	dpc <- td / sum(td)
	if (pc) {
	# percentages or D%
	return(dpc)

jirilukavsky / new_package.R

Created February 16, 2024 08:36

New R package

	# adapted from https://usethis.r-lib.org/
	library(usethis)

	# Create a new package -------------------------------------------------
	path <- file.path(getwd(), "jatosR")
	create_package(path)
	# only needed since this session isn't interactive
	proj_activate(path)

	use_mit_license("Jiri Lukavsky")

jirilukavsky / motrack_planets.R

Last active April 7, 2021 17:55

Accelerating circular movements in motrack. Example of custom step_function and variable speed.


	library(tidyverse)
	library(motrack)

	f1 <- function(time, moment) {
	# base speed = 3, acceleration (increasing by) 1 deg/s^2
	3 + time
	}

	position <- tibble(object = 1:4, x = c(-7, 5, 7, -5), y = c(5, 7, -5, -7))

jirilukavsky / make_grid.R

Created August 6, 2020 11:30

Processing images for memory experiments

	# make the grid file

	grid_size <- 900
	n_square <- 6
	ss <- 900 / n_square # square size

	d <- array(0, c(grid_size, grid_size, 4))
	fillcolor <- c(235, 220, 36, 128)

	for (i in 1:n_square) {

jirilukavsky / variance_estimate.R

Created May 5, 2020 09:55

Single variance estimate

	# When we calculate variance (SD) from single observation (one group),
	# how good/bad estimate this can be of the underlying SD?

	B <- 100
	n <- 6
	m <- 50
	s <- 15
	estim_sd <- numeric(B)
	set.seed(1010)
	for (i in 1:B) {

jirilukavsky / correlation_ci.Rmd

Created December 20, 2019 10:03

	---
	title: "Sample estimation for correlation coefficients"
	author: "Jiri Lukavsky"
	date: "12/20/2019"
	output: html_document
	---

	```{r setup, include=FALSE}
	knitr::opts_chunk$set(echo = TRUE)

jirilukavsky / quickpsy_example.R

Created February 6, 2019 09:49

Sample data and minimalistic code to show analysis of psychophysics data. Data are from Muller-Lyer illusion experiment, where people compare a length of the line with a standard (featuring Muller-Lyer endings).

	# example of fitting psychophysic curve to data are from one subject
	# data from Muller-Lyer illusion, subjects are supposed to compare stimulus to
	# standard (length = 100)
	# columns:
	# - length - length of stimulus line
	# - pp - proportion of saying "longer"
	# - id
	# - N - is 10 for each value of length
	# - k - count of saying "longer" (k = pp * N)

jirilukavsky / rtips.R

Last active June 24, 2019 19:54

Useful R code snippets that I often look for

	# --------- RStudio -------------------
	# knit from CLI
	rmarkdown::render("test.Rmd", "html_document")

	# --------- data manipulation ---------
	# wide-to-long = gather, long-to-wide = spread
	wide %>%
	gather(key = column_name_of_column_names,
	value = column_name_of_column_data,
	-allvariables, -whichwewant, -ineachrow)

jirilukavsky / wilcox_effectsize.R

Created February 15, 2018 09:58

How to report effect size in Wilcoxon signed-rank test

	# How to report effect size in Wilcoxon signed-rank test

	# links
	# [1] https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test
	# [2] https://stats.stackexchange.com/questions/229760/wilcoxon-signed-rank-test-in-r/229761
	# [3] https://stats.stackexchange.com/questions/41620/output-of-one-tailed-wilcoxon-sign-rank-test-in-r
	# [4] https://stats.stackexchange.com/questions/133077/effect-size-to-wilcoxon-signed-rank-test
	# [5] Acion, L., Peterson, J. J., Temple, S., & Arndt, S. (2006). Probabilistic index: an intuitive non-parametric approach to measuring the size of treatment effects. Statistics in Medicine, 25(4), 591–602. https://doi.org/10.1002/sim.2256

	# How Wilcoxon signed-rank test works + what it reports

Jiri Lukavsky jirilukavsky