EoinTravers

from psychopy import visual
from psychopy.tools.colorspacetools import hsv2rgb
import numpy as np

win = visual.Window(
    size=[800, 600], fullscr=False, screen=0,
    gammaErrorPolicy='ignore',
    color=(1, 1, 1), colorSpace='rgb', units='height')

class CompoundStim():

EoinTravers

library(tidyverse)

# Arguments: Sigma of old predictor, Sigma of new predictor, N cases
compare_methods = function(s0, s1, n){
  print(c(s0, s1, n))
  y = rnorm(n, 0, 1)
  x0 = y + rnorm(n, 0, s0) # Old predictor
  x1 = y + rnorm(n, 0, s1) # New predictor to evaluate

  m0 = lm(y ~ x0)

EoinTravers

library(brms)
library(rstan)

## Simulate data
bxy = 2
bxz = 3
byz = 4
e = .1
n = 1000

EoinTravers

import numpy as np
import matplotlib.pyplot as plt

  
def plot_top(names, values, n_top=10, 
             order=None,
             alpha=.25,
             ax=None, figsize=None):
    '''Plot the top N values, then squeeze in the rest
    

EoinTravers

library(tidyverse)

# Simulate data under 1-cluster solution:
# Two Gaussians, with different means, same SD.
m1 = 10
m2 = 20
sd = 10
n1 = 50
n2 = 50
x1 = rnorm(n1, m1, sd)

EoinTravers

# Coin Flip MAP Bootstrap
library(tidyverse)
theme_set(theme_bw(base_size=18))

# Data
outcomes = c(rep(0, 5), rep(1, 15)) # True probability = 0.75
n_trials = length(outcomes)


# Beta Prior

EoinTravers

#' Plot matrix as heatmap
#' @description
#' `plot_covariance_matrix()` adds an appropriate `fill_label`
#' `plot_correlation_matrix()` also sets limits to ±1
#' 
#' Make sure to import dplyr and ggplot (or tidyverse)!
#' @param mat Matrix to plot
#' @param labeller Function or list used to rename rows/cols
#' @param digits Digits to round values to (default 2)
#' @param limit Limit (±) of colour scale. Defaults to `abs(max(value))`

EoinTravers

---
title: "glm_prevelance"
author: "Eoin Travers"
output: html_document
---

```{r}
library(tidyverse)
binom_smooth = function (link = "probit", ...) {
    geom_smooth(method = "glm", 

EoinTravers

plot_measurement_invariance = function(model){
  factor_loadings = parameterEstimates(model) %>%
    rename(estimate = est, low = ci.lower, high = ci.upper) %>% 
    mutate(rhs = factor(rhs, levels = unique(rhs)),
           lhs = factor(lhs, levels = unique(lhs)),
           label = paste(lhs, op, rhs),
           type = case_when(
             op == '=~' ~ 'Loading',
             op == '~~' ~ 'Covariance',
             op == '~1' ~ 'Mean',

EoinTravers

generate_spaced_dates = function(n_samples, gap = 21, 
                                 total_period = 365,
                                 n_iter=1e12){
  x = runif(n_samples, 0, total_period) %>% sort() %>% round()
  for(i in 1:1e12){
    is_acceptable = min(diff(x)) >= 21
    if(is_acceptable) {
      break
    } else {
      x = runif(n_samples, 0, total_period) %>% sort() %>% round()