jirilukavsky

# 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

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 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

# 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

---
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

# 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

# --------- 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

# 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

jirilukavsky

# data from
# https://en.wikipedia.org/wiki/Simpson's_paradox#Kidney_stone_treatment
# in all tabs: row 1 successful, row 2 failed treatment

# small stones
mk = matrix(c(81, 87-81, 234, 270-234), ncol=2)
chisq.test(mk)
# large stones
vk = matrix(c(192, 263-192, 55, 80-55), ncol=2)
chisq.test(vk)

jirilukavsky

import numpy as np
from scipy.optimize import curve_fit
import scipy as sy
import matplotlib.pyplot as plt

d = np.array([75, 80, 90, 95, 100, 105, 110, 115, 120, 125], dtype=float)
p1 = np.array([6, 13, 25, 29, 29, 29, 30, 29, 30, 30], dtype=float) / 30. # scale to 0..1

# psychometric function
def pf(x, alpha, beta):