---
title: "Investigating specific specifications"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Investigating specific specifications}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  #fig.path = "man/figures/README-",
  out.width = "100%",
  fig.retina = 2
)
```

Per default, `specr()` summarizes individual specifications by using `broom::tidy()` and `broom::glance()`. For most cases, this provides a sufficient and appropriate summary of the relationship of interest and model characteristics. Sometimes, however, it might be useful to investigate specific models in more detail or to investigate a specific parameter that is not provided by the two functions (e.g., r-square). This vignette shows how to access individual models and extract further information from them.

```{r, message = F, warning = F}
library(tidyverse)
library(specr)
library(performance)
```

## Setup specifications with a specific extract function

If we want to investigate individual models and particularly all aspects of that model, we need to create a custom extract function that also stores the entire model object in the result data frame. 

```{r, message=F, warning = F}
# Custom function
tidy_new <- function(x) {
  fit <- broom::tidy(x, conf.int = TRUE)
  fit$res <- list(x)  # Store model object
  return(fit)
}

# Run specification curve analysis
specs <- setup(data = example_data, 
               y = c("y1", "y2"), 
               x = c("x1", "x2"), 
               model = c("lm"),
               controls = c("c1", "c2"),
               subsets = list(group1 = unique(example_data$group1),  
                              group2 = unique(example_data$group2)),
               fun1 = tidy_new)

results <- specr(specs)
```


## Identify model(s) of interest

For this example, we are going to look at two specific models (same independent variables, all controls, all participants, but different dependent variables).

```{r}
(y_models <- results %>%
  as_tibble %>%
  filter(x == "x1", 
         controls == "c1 + c2",
         subsets == "all")) %>%
  select(x:group2, estimate:res)
```

As you can see, the resulting tibble includes an additional column called `res`. This column includes the entire "model object" and we can use it to further investigate each model. 

## Investigate models

For example, we can now easily get a full summary of the models and compare individual coefficients and statistics. 

```{r}
y_models %>%
  pull(res) %>%
  map(summary) %>%
  map(coef)
```

Or we could get r-squared values for both models (here using the function `r2()` from the [performance](https://easystats.github.io/performance/) package).

```{r}
y_models %>%
  pull(res) %>% 
  map(r2)       # r2 is include in the package "performance"
```


## Some more examples

This way, we can analyze or compare such statistics across several models.

```{r, fig.height=8, fig.width=10, message=F, warning = F}
r2_results <- results %>%
  as_tibble %>%
  filter(subsets == "all") %>%
  mutate(r2 = map(res, r2), 
         r2 = map_dbl(r2, 1)) %>%
  arrange(r2)

r2_results %>%
  select(x:controls, r2)
```

And we can plot comparisons...

```{r}
r2_results %>%
  arrange(r2) %>%
  mutate(rank = 1:n()) %>%
  ggplot(aes(x = rank, 
             y = r2)) +
  geom_point() +
  geom_line() +
  theme_minimal() +
  theme(strip.text = element_blank(),
        axis.line = element_line("black", size = .5),
        axis.text = element_text(colour = "black"))
```