---
title: "Mixed models"
author: "Oliver Jayasinghe and Rex Parsons"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{mixed-models}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup, message=FALSE}
library(GLMMcosinor)
library(dplyr)
library(ggplot2)
```

`{GLMMcosinor}` allows specification of mixed models accounting for fixed and/or random effects. Mixed model specification follows the `{lme4}` format. See their vignette, [Fitting Linear Mixed-Effects Models Using lme4](https://cran.r-project.org/package=lme4/vignettes/lmer.pdf), for details about how to specify mixed models.

### Data with subject-level differences

To illustrate an example of using a model with random effects on the cosinor components, we will first simulate some data with `id`-level differences in amplitude and acrophase.

```{r}
f_sample_id <- function(id_num,
                        n = 30,
                        mesor,
                        amp,
                        acro,
                        family = "gaussian",
                        sd = 0.2,
                        period,
                        n_components,
                        beta.group = TRUE) {
  data <- simulate_cosinor(
    n = n,
    mesor = mesor,
    amp = amp,
    acro = acro,
    family = family,
    sd = sd,
    period = period,
    n_components = n_components
  )
  data$subject <- id_num
  data
}

dat_mixed <- do.call(
  "rbind",
  lapply(1:30, function(x) {
    f_sample_id(
      id_num = x,
      mesor = rnorm(1, mean = 0, sd = 1),
      amp = rnorm(1, mean = 3, sd = 0.5),
      acro = rnorm(1, mean = 1.5, sd = 0.2),
      period = 24,
      n_components = 1
    )
  })
)
dat_mixed$subject <- as.factor(dat_mixed$subject)
```

```{r echo=FALSE}
withr::with_seed(
  50,
  {
    dat_mixed <- do.call(
      "rbind",
      lapply(1:30, function(x) {
        f_sample_id(
          id_num = x,
          mesor = rnorm(1, mean = 0, sd = 1),
          amp = rnorm(1, mean = 3, sd = 0.5),
          acro = rnorm(1, mean = 1.5, sd = 0.2),
          period = 24,
          n_components = 1
        )
      })
    )
    dat_mixed$subject <- as.factor(dat_mixed$subject)
  }
)
```


A quick graph shows how there are individual differences in terms of MESOR, amplitude and phase.

```{r}
ggplot(dat_mixed, aes(times, Y, col = subject)) +
  geom_point() +
  geom_line() +
  theme_bw()
```

### A single component model with random effects

For the model, we should include a random effect for the MESOR, amplitude and acrophase as these are clustered within individuals.

In the model formula, we can use the special `amp_acro[n]` which represents the n<sup>th</sup> cosinor component. In this case, we only have one component so we use `amp_acro1`. Following the `{lme4}`-style mixed model formula, we add our random effect for this component and the intercept term (MESOR) clustered within subjects by using `(1 + amp_acro1 | subject)`. The code below fits this model

```r
mixed_mod <- cglmm(
    Y ~ amp_acro(times, n_components = 1, period = 24) + 
      (1 + amp_acro1 | subject),
    data = dat_mixed
  )
```

```{r echo=FALSE}
withr::with_seed(42, {
  mixed_mod <- cglmm(
    Y ~ amp_acro(times, n_components = 1, period = 24) +
      (1 + amp_acro1 | subject),
    data = dat_mixed
  )
})
```

This works by replacing the amp_acro1 with the relevant cosinor components when the data is rearranged and the formula created. The formula created can be accessed using `.$formula`, and shows the `amp_acro1` is replaced by the `main_rrr1` and `main_sss1` (the cosine and sine components of time that also appear in the fixed effects).

```{r}
mixed_mod$formula
```

The mixed model can also be plotted using `autoplot`, but some of the plotting features that are available for fixed-effects models may not be available for mixed-effect models.

```{r}
autoplot(mixed_mod, superimpose.data = TRUE)
```

The summary of the model shows that the input means for MESOR, amplitude and acrophase are similar to what we specified in the simulation (0, 3, and 1.5, respectively).

```{r}
summary(mixed_mod)
```

We can see that the predicted values from the model closely resemble the patterns we see in the input data.

```{r}
ggplot(cbind(dat_mixed, pred = predict(mixed_mod))) +
  geom_point(aes(x = times, y = Y, col = subject)) +
  geom_line(aes(x = times, y = pred, col = subject))
```

This looks like a good model fit for these data. We can highlight the importance of using a mixed model in this situation rather than a fixed effects only model by creating that (bad) model and comparing the two by using the Akaike information criterion using `AIC()`.

```{r}
fixed_effects_mod <- cglmm(
  Y ~ amp_acro(times, n_components = 1, period = 24),
  data = dat_mixed
)

AIC(fixed_effects_mod$fit)
AIC(mixed_mod$fit)
```

Aside from not being able to be useful to see the differences between subjects from the model, we end up with much worse model fit and likely biased and/or imprecise estimates of our fixed effects that we are interested in!
