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passage	line	line_text	probe	correct_response	Dimension	Foreshadow	Shift
6	1	Juan’s meeting with the boss didn’t quite go as planned.	n	space	Temporal	y	y
6	2	He sat at his desk snacking on mixed nuts.	n	space	Temporal	y	y
6	3	He was going to have to finish the new designs before the next client meeting.	n	space	Temporal	y	y
6	4	It was going to take all night.	n	space	Temporal	y	y
6	5	The next morning, he reached for his coffee before remembering the cup was empty.	n	space	Temporal	y	y
6	6	At least the designs were finished.	n	space	Temporal	y	y
6	7	He could probably get a couple of hours of sleep before the meeting.	n	space	Temporal	y	y
6	0	SNACKING	y	y	Temporal	y	y
6	0	Juan’s meeting went as expected.	y	n	Temporal	y	y

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##install.packages("googlesheets")

# Load googlesheets package
library(tidyverse)
library(googlesheets)
library(randomizeR)
# Connect R and my google drive
gs_auth(new_user = TRUE)

# Export the sheet.

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## Load Raaijmakers 1999 data set from languageR
library(languageR)
data(quasif)

summary(quasif)

## Compute the variance of random effect
library(lme4)

## Parameters for the general effect size

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if(!require(simr)){install.packages("simr"); library(simr)}else{library(simr)}
#library(lme4)

## Means and Variance of Behavior data
CWL2016RT <- rbind(M=c(.793,.815,.893,.866),VAR=c(.011^2,.010^2,.016^2,.015^2))

## Got the parameters of RT distributions
## Referring to https://stats.stackexchange.com/questions/12232/calculating-the-parameters-of-a-beta-distribution-using-the-mean-and-variance

estBetaParams <-function(mu, var) {

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## Model from appendix 3
```
model{
 # There are 20 individuals.
 for (i in 1:n) {
 # The dependent variable has a mean (beta) and prior precision (tau).
 y[i] ~ dnorm(beta[i], tau)
 # Beta consists of an intercept: mu (which is, without any predictors in the model equal to the mean of our dependent variable)
 beta[i] <- mu [i]}
 # Mu (=mean of dependent variable) has a normal prior distribution with a mean of 80 and a prior precision of .01 and the prior is limited to obtain scores between 40 and 180. The prior precision of the dependent variable, tau, has a inverse gamma prior distribution.

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## 在Rstudio，Tools -> Global Options -> General -> Default Text Encoding -> UTF-8
## 系統語系要設定為台灣正體中文
Sys.setlocale(category = "LC_ALL", locale = "cht")

##讀取檔案、提取資料與製造變項

#這是一般 TXT 檔，檔頭有變項名稱

#資料來自於 NHIS 2010 調查,取 1955 年以前出生者（55歲以上）