SM01_Analysis.Rmd

---
title: "R Notebook"
output: html_notebook
---

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```{r}
plot(cars)
```

```{r}
data <- read.csv(file = "SM01_Retest_Aesthetic_Task.csv")
print(data)
```

```{r}
#Splitting the dataframe by subject
sbj <- split (data, data$subj)
sbj[[1]]
```

```{r}
```

```{r}
library("retimes")
```
```{r}
#running loop for the aesthetic task
d = NULL
for (i in 1:100){
  dist <-  timefit(sbj[[i]]$durs, iter = 10)
  mu[i] <- dist@par[1]
  sig[i] <- dist@par[2]
  tau[i] <- dist@par[3]
  log_like[i] <- dist@logLik
  aic[i] <- dist@AIC
  bic[i] <- dist@BIC
}
d <- rbind(d, data.frame(mu, sig, tau, log_like, aic, bic))
d$subject <- 1:nrow(d)
print(d)
```
```{r}
for (x in 1:100){
  durs_mean [x] <- mean(sbj[[x]]$durs)
}
print(durs_mean)
```

```{r}
write.csv(d, file = "Aesthetic_Retest_Gauss.csv", row.names=FALSE)
```

```{r}
mem_data <- read.csv(file = "SM01_Retest_Memorize_Task.csv")
print(mem_data)
```

```{r}
mem_sbj <- split (mem_data, mem_data$subj)
mem_sbj[[1]]
```

```{r}
df = NULL
for (i in 1:100){
  dist <-  timefit(mem_sbj[[i]]$durs)@par
  mu[i] <- dist[1]
  sig[i] <- dist[2]
  tau[i] <- dist[3]
}
df <- rbind(df, data.frame(mu, sig, tau))
df$subject <- 1:nrow(df)
print(df)
```
```{r}
write.csv(df, file = "Memorize_Retest_Gauss.csv", row.names=FALSE)
```

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