20_example_bock.Rmd

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

```{r}
library(tidyverse)
library(nlme)
```



```{r}
df_long <- read.table("data/bock1983_wpss_tca.txt",
           col.names = c("id", "wpss", 
                         "int", "linear_time", "change_time",
                         "group", "x2", "x3")) %>% 
  dplyr::mutate(week = 3.5 + linear_time) %>% 
  dplyr::mutate(group = factor(group,
                               levels = c(0, 1),
                               labels = c("TCA-None",
                                          "None-TCA"))) %>% 
  dplyr::select(id, group, week, wpss)
```


```{r}
df_long
```


```{r}
df_wide <- df_long %>% 
  tidyr::pivot_wider(names_from = week,
                     names_prefix = "wpss_",
                     values_from = wpss)
```


```{r}
df_wide %>% 
  dplyr::select(starts_with("wpss")) %>% 
  cor() %>% 
  corrplot::corrplot.mixed()
```

```{r}
df_long %>% 
  ggplot(aes(x = week,
             y = wpss)) +
  geom_line(aes(group = id),
            alpha = .2) +
  theme_bw() +
  facet_grid(. ~ group) +
  stat_summary() +
  stat_summary(fun = "mean",
               geom = "line") +
  geom_vline(xintercept = 3.5, linetype = "dashed")
```


## Autoregression 1

```{r}
fit_cpm_ar1 <- nlme::gls(wpss ~ group*I(week-3.5) + group*I((week - 3.5)^2),
                         data = df_long,
                         correlation = corAR1(form = ~ 1 | id))
```


```{r}
summary(fit_cpm_ar1)
```


```{r}
texreg::knitreg(fit_cpm_ar1, single.row = TRUE)
```


```{r}
fit_cpm_ar1 %>% 
  emmeans::emmeans(~ week*group,
                   at = list(week = seq(from = 1, to = 6, by = .01))) %>% 
  data.frame() %>% 
  ggplot(aes(x = week,
             y = emmean,
             fill = group)) +
  geom_ribbon(aes(ymin = emmean - SE,
                    ymax = emmean + SE),
              alpha = .5) +
  geom_line(aes(linetype = group)) +
  theme_bw() +
  geom_vline(xintercept = 3.5, linetype = "dashed")
```

### Unstructured

```{r}
fit_cpm_uns <- nlme::gls(wpss ~ group*I(week-3.5) + group*I((week - 3.5)^2),
                         data = df_long,
                         correlation = corSymm(form = ~ 1 | id))
```


```{r}
summary(fit_cpm_uns)
```



```{r}
fit_cpm_uns %>% 
  emmeans::emmeans(~ week*group,
                   at = list(week = seq(from = 1, to = 6, by = .01))) %>% 
  data.frame() %>% 
  ggplot(aes(x = week,
             y = emmean,
             fill = group)) +
  geom_ribbon(aes(ymin = emmean - SE,
                    ymax = emmean + SE),
              alpha = .5) +
  geom_line(aes(linetype = group)) +
  theme_bw() +
  geom_vline(xintercept = 3.5, linetype = "dashed")
```




### Compound Symmetry (with constant correlation)

```{r}
fit_cpm_cps <- nlme::gls(wpss ~ group*I(week-3.5) + group*I((week - 3.5)^2),
                         data = df_long,
                         correlation = corCompSymm(form = ~ 1 | id))
```





```{r}
fit_cpm_cps %>% 
  emmeans::emmeans(~ week*group,
                   at = list(week = seq(from = 1, to = 6, by = .01))) %>% 
  data.frame() %>% 
  ggplot(aes(x = week,
             y = emmean,
             fill = group)) +
  geom_ribbon(aes(ymin = emmean - SE,
                    ymax = emmean + SE),
              alpha = .5) +
  geom_line(aes(linetype = group)) +
  theme_bw() +
  geom_vline(xintercept = 3.5, linetype = "dashed")
```


### Compare performance


```{r}
performance::compare_performance(fit_cpm_ar1, fit_cpm_cps, fit_cpm_uns)
```