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# TO DO: ADJUST TO OTHER PARAMETERS (E:G: WHAT IF THERE ARE STIMULUS EFFECTS? OR MORE FIX EFFS?)
createNewData <- function (J,K,model){
  # J is the number of subjects
  # K is the number of visits
  # HP is the hyperparameter matrix from get Hyperparam
  # TO DO: LOOP THROUGH ALL FE ROWS AND AUTOMATICALLY EXTRACT NAMES OF FIXED EFFECTS AND ESTIMATES
  fe <- fixef(model)
  Intercept <- fe[1] #intercept
  bVisit <- fe[2] #visit
  bDiagnosis <- fe[3] #diagnosis

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// contest model
data{
    int N_choices;  // amount of data points
    int N_items;  // unique stimuli
    int N_raters;  // unique raters
    int y[N_choices];  // outcome
    int item1[N_choices];  // which stimulus 1 in each trials
    int item2[N_choices];  // which stimulus 2 in each trials
    int rater[N_choices];  // which rater in each trials
    real Period[N_items];      // item feature: period

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library(brms)
zero_one_inflated_beta2 <- custom_family(
  "zero_one_inflated_beta2", dpars = c("mu", "phi","zero","one"),
  links = c("logit", "log","logit","logit"), lb = c(NA, 0, 0, 0),
  ub = c(NA, NA, 1, 1), type = "real"
)
  


stan_funs <- "

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# Defining formulas
PitchMean_f <- bf(Pitch_MeanS ~ Diagnosis + (1|ID))
PitchMean_Language_f <- bf(Pitch_MeanS ~ 0 + language + language:Diagnosis + (1|ID))
PitchMean_Age_f <- bf(Pitch_MeanS ~ 0 + Diagnosis + AgeS:Diagnosis + (1|ID))
PitchMean_Gender_f <- bf(Pitch_MeanS ~ 0 + Gender + Gender:Diagnosis + (1|ID))
PitchMean_AgeGender_f <- bf(Pitch_MeanS ~ 0 + Gender + Gender:AgeS + Gender:Diagnosis + Gender:AgeS:Diagnosis + (1 | ID))
PitchMean_Full_f <- bf(Pitch_MeanS ~ 1 + Diagnosis * (Gender*AgeS + language) + (1 | ID))

# Fittig and saving models
PitchMean_SkepticModel_Trial <- brmsFun(Name="PitchMean_SkepticModel_Trial",formula=PitchMean_f,data=d_trial,prior=SkepticPrior)

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## Load libraries
pacman::p_load(tidyverse,brms)

## Load data
data <- read_csv("data.csv")


## Define formulas
Model1_f <- bf(Outcome ~ Diagnosis + (1|ID))
Model2_f <- bf(Outcome ~ Diagnosis + (1|p|ID), 

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# Load libraries
library(brms)
library(tidyverse)

# Load data

data <- read_csv("temp_data.csv")

# Define model
Model2_Language_f <- bf(Outcome ~ 0 + language + language:Diagnosis +

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# Load libraries
library(brms)
library(tidyverse)

# Load data

data <- read_csv("temp_data.csv")

# Define model

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pacman::p_load(tidyverse, brms)

# Prepare the data

d <- Ass3
#summary(d)

d$Diagnosis <- plyr::revalue(as.character(d$Diagnosis), 
                             c("0"="Controls", "1"="Schizophrenia"))

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pacman::p_load(tidyverse, brms)

seed <- 1981 # Defining a seed so the results are always the same
samplesize <- 300 # Defining the amount of datapoints
n <- 1000 # Defining number of simulations
MaxScore <- 8 # Defining max rating
MinScore <- 1 # Defining min rating

## Regression to the mean?
Regression2Mean <- 0.7 # 1st to 2nd in the empirical data : 0.668444

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library(tidyverse)
library(data.table)

temp <- read_delim("~/Downloads/temp.csv", delim = ";")
setDT(temp)

r <- ":acute interstitial pneumonitis:|:alveolar lung disease:|:alveolar proteinosis:|:alveolitis:|:alveolitis necrotising:|:autoimmune lung disease:|:bronchiolitis:|:combined pulmonary fibrosis and emphysema:|:diffuse alveolar damage:|:eosinophilia myalgia syndrome:|:eosinophilic granulomatosis with polyangiitis:|:eosinophilic pneumonia:|:eosinophilic pneumonia acute:|:eosinophilic pneumonia chronic:|:hypersensitivity pneumonitis:|:idiopathic interstitial pneumonia:|:idiopathic pneumonia syndrome:|:idiopathic pulmonary fibrosis:|:immune-mediated pneumonitis:|:interstitial lung disease:|:lung infiltration:|:lung opacity:|:necrotising bronchiolitis:|:obliterative bronchiolitis:|:pneumonitis:|:progressive massive fibrosis:|:pulmonary fibrosis:|:pulmonary necrosis:|:pulmonary toxicity:|:pulmonary vasculitis:|:small airways disease:|:transfusion-related acute lung injury:|:acute lung injury:|:acute respira