drbenvincent

drbenvincent

drbenvincent

import pymc3 as pm
import numpy as np
import arviz as az
%config InlineBackend.figure_format = 'retina'

# Data from https://twitter.com/tomstafford/status/1456914037195907079?s=20
N = np.array([1258, 280])  # total number of caffiene and non-caffiene drinkers
k = np.array([966, 168])  # total number of those who have favourite mugs

def cohens_h(p):

drbenvincent

% Add tooolbox code to path
addpath('~/git-local/darc-experiments-matlab')

% Run the toolbox setup code
env_setup

% Get participant information with GUI
[expt_options] = getHumanExperimentOptions();
plotting_style = 'full';

drbenvincent

% Add tooolbox code to path
addpath('~/git-local/darc-experiments-matlab')

% Run the toolbox setup code
env_setup

% Get participant information with GUI
[expt_options] = getHumanExperimentOptions();
plotting_style = 'full';

drbenvincent

# We have a dataframe with many columns. 
# 15 of these columns correspond to responses to questions about hunger.
# They group into 5 subscales (each with 3 questions).
# I want to calculate these subscales (means) as well as an overall score (mean of all).
# This then needs to applied to the control condition (C) and a fasted condition (F) scores
# Once the means are calculated I no longer need the raw scores, so bonus is to automatically remove these columns. Need to retain all other columns in orginal dataframe.

# The code below works just fine, but seems pretty verbose. Is there a more R-like, concise way to do this? 
# It seems like a split-apply-combine, but applied to columns rather than rows?

drbenvincent

true_mean = 0;
true_sigma = 1;
% likelihood_func = @(x, mean, sigma) normpdf(x, mean, sigma);
% the above function to calcalate in matrix form, for speed
likelihood_func = @(params)...
	prod(normpdf(repmat(x,[1 numel(params(1))]),...
	repmat(params(1), [1 numel(x)])',...
	repmat(params(2),[1 numel(x)])' ), 1);

%% generate data

drbenvincent

true_mean = 0;
true_sigma = 1;
% likelihood_func = @(x, mean, sigma) normpdf(x, mean, sigma);
% the above function to calcalate in matrix form, for speed
likelihood_func = @(x, mean, sigma)...
	prod(normpdf(repmat(x,[1 numel(mean)]),...
	repmat(mean, [1 numel(x)])',...
	repmat(sigma,[1 numel(x)])' ), 1);

%% generate data

drbenvincent

%% true probability distribution
true_func = @(x) betapdf(x,1+1,1+10);

%% Do importance sampling
N = 10^6;
% uniform proposal distribution
x_samples = rand(N,1);
proposal = 1/N;
% evaluate for each sample
target = true_func(x_samples);

drbenvincent

true_mean = 0;
true_sigma = 1;
% likelihood_func = @(x, mean, sigma) normpdf(x, mean, sigma);
% the above function to calcalate in matrix form, for speed
likelihood_func = @(x, mean, sigma)...
	prod(normpdf(repmat(x,[1 numel(mean)]),...
	repmat(mean, [1 numel(x)])',...
	repmat(sigma,[1 numel(x)])' ), 1);

%% generate data