cse_simul.Rmd

---
title: "simulrt"
author: "Miklos Bognar"
date: "2024-02-09"
output: html_document
---
```{r}
library(dplyr)
library(tidyverse)
library(papaja)
subject_number = 200
trials = 1:200
tau_0 = rnorm(subject_number,0,100)
subject_data = data.frame(subject_id = seq_len(subject_number), tau_0)
trial_df = expand.grid(subject_id = subject_data$subject_id, trial_id = trials)

raw_data = trial_df %>% 
  left_join(subject_data, by="subject_id") %>% 
  mutate(congruent = sample(c(0,1),size=n(), replace=T),
         prev_congruent = sample(c(0,1),size=n(), replace=T),
         beta_0 = rexgaussian(n=n(), mu=700, sigma=100, beta = 180),
         beta_1 = rnorm(n=n(), mean=50, sd = 10),
         beta_2 = rnorm(n=n(), mean=25, sd = 5)) %>% 
  mutate(RT = tau_0 +
           beta_0+
           ifelse(congruent,beta_1*-.5,beta_1*.5)+
           ifelse(prev_congruent,0,ifelse(congruent,beta_2*.5,beta_2*-.5)))

summary = raw_data %>% 
  group_by(congruent,prev_congruent) %>% 
  summarize(mean_rt = mean(RT),
            sd_rt = sd(RT))

raw_data %>% 
  ggplot()+
  aes(x=RT) +
  papaja::theme_apa() +
  geom_histogram(bins = 100)


summary %>% ggplot()+
  aes(x=factor(prev_congruent, levels = c(0,1)), y=mean_rt, color=as.factor(congruent), group=as.factor(congruent)) +
  geom_point()+
  geom_path()

mymodel = lm(RT ~ congruent * prev_congruent, data = raw_data)
summary(mymodel)
```