kagaya

function ForceData=autorec(duration, strikeThreshold, rewardThreshold)
%% DISCRIPTION 
% This program automatically records foce data by detecting rising phase of
% the voltage change and it produce digital output whether the peak of the
% voltage above a threshold. The condition of output can be customized.

%% HOW TO USE
% The termination of this program is tricky. Please press "Ctrl - c" to
% quit the program and after that, command "daqreset" in the MATLAB prompt.

kagaya

function formatted_data = format2txt()
%%
if ismac == 1
    file_names = ls();
elseif ispc == 1
    file_names = char(mat2str(ls()));
end

data_dir = regexp(file_names, '[0-9]*-[0-9]*\.[0-9]*', 'match');
data_dir

kagaya

## functions for statistical analysis and data visualizations in the paper,
## The paper's title: Motor control of ultrafast, ballistic movements (by K. Kagaya and S. N. Patek)
## This script is written by Katsushi Kagaya (k.kagaya@me.com)
## Gist URL: https://gist.github.com/kagaya/cbafe0a332e6d6766168

## to load libraries
library(ggplot2) # for plotting data and model prediction
library(mgcv) # for generalized addive modeling (GAM fitting)
library(plyr) # for data manipulation
library(nlme) # for linear mixed effects modeling

kagaya

## functions for 'mdf' files which stores digitized kinematic data from MTrackJ
## The paper's title: Motor control of ultrafast, ballistic movements (by K. Kagaya and S. N. Patek)
## This script is written by Katsushi Kagaya (k.kagaya@me.com)
## Gist URL: https://gist.github.com/kagaya/540596f6d1191c157cb7


library(ggplot2)
library(plyr)

kagaya

## functions for physiological data analyses in the paper,
## The paper's title: Motor control of ultrafast, ballistic movements (by K. Kagaya and S. N. Patek)
## written by Katsushi Kagaya (k.kagaya@me.com)
## Gist URL: https://gist.github.com/kagaya/36145a370a1da0f4ba0d

## to load libraries necessary for the current script
library(ggplot2)
library(plyr)

kagaya

library(tidyverse)
library(forcats)
library(rstan)
library(shinystan)

waic <- function(log_likelihood) {
  # from https://gist.github.com/MatsuuraKentaro/3f6ae5863e700f5039c19e36a9bdf646
  training_error <- - mean(log(colMeans(exp(log_likelihood))))
  functional_variance_div_N <- mean(colMeans(log_likelihood^2) - colMeans(log_likelihood)^2)
  waic <- training_error + functional_variance_div_N

kagaya

library(tidyverse)
library(ggrepel)
library(rstan)

rstan::expose_stan_functions("my_stan_functions.stan") # https://gist.github.com/kagaya/c726f16d82b80026bb1924b408a72b5c
source("utility.R")


###### choice #####

kagaya

---
header-includes:
- \usepackage{setspace}
- \doublespacing
- \usepackage{lineno}
- \linenumbers
output:
  pdf_document: default
  html_document:
    df_print: paged

kagaya

---
title: Supplementary information for the paper 'Individual behavioral type captured by a Bayesian model comparison in cap making by sponge crab'
---

This document is the supplementary information for the paper 'Individual behavioral type captured by a Bayesian framework in cap making by sponge crab' by Harada, Hayashi and Kagaya. 

The basic conceptural framework for data analysis in the paper is a Bayesian framework based on the mathematical theory founded by Dr. Akaike and Dr. Watanabe (Watanabe, 'Mathematical Theory of Bayesian Statistics', 2018). The author of this document wrote an example of model selection by Widely Applicable Information Criterion (WAIC) using the data of 'artificial case' (http://rpubs.com/katzkagaya/460937). It may be of help for clarifying what the analysis of this document is doing, by using numerically generated 'artificial' data and several relatively simple hierarchical models. 

In this study, we deal with the behavioral data of a 'natural case'. Therefore, we do not know

kagaya

library(rstan)
library(tidyverse)
library(shinystan)

rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())

# preprocessing                     
d <- read_csv(file='Dromiidae.csv') # https://gist.github.com/kagaya/0d309397300b7e8294fe53c44b6fc525
d <- d %>%