---
title: "Unsupervised clustering with general GMCMs"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Unsupervised clustering with general GMCMs}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r knit-int, echo=FALSE, include=FALSE}
set.seed(7869670)
knitr::opts_knit$set(self.contained = TRUE)
```

This is a quick tutorial for using the general GMCM for unsupervised clustering. 

## Initialization
The **GMCM**^[1][1]^ package is loaded.

```{r load-packages, include=TRUE}
#install.packages("GMCM")  # Uncomment to install the GMCM package
library("GMCM")
```

If **GMCM** is *not* installed, please uncomment the above line and rerun the script to install from CRAN.
The development version can be installed from [GitHub following the instructions there](https://github.com/AEBilgrau/GMCM).


## Simulation of data
First, we simulate some toy data. We wish to simulate, say, 1000 observations of 2-dimensions each of which stems from one of 3 components.
In order to do so, we construct a parameter object `theta` and simulate from the GMCM.

```{r sim-data, include=TRUE, echo=TRUE}
true_theta <- rtheta(m = 3, d = 2)
plot(true_theta)
ds <- SimulateGMCMData(n = 1000, theta = true_theta)
str(ds)
```

As can be seen, the `SimulateGMCMData` function returns a `list` containing the copula observations `u`, the unobserved process `z`, the latent groups `K`, and the `theta` used for simulation.

Plotting the true copula realizations and coloring by the true classes shows what we intend to estimate and recover:

```{r data-plot}
plot(ds$u, col = ds$K)
```

A ranking of `u` (or `z`) corresponds to what would be the data in a real application.

```{r select-data, include=TRUE, echo=TRUE}
uhat <- Uhat(ds$u)
plot(uhat)
```


## Initial parameters
To fit a general GMCM, we must choose some initial parameters. The `choose.theta` is a (sometimes) helpful default we here invoke explicitly:

```{r show-initial-params, include=TRUE, echo=TRUE}
init_theta <- choose.theta(uhat, m = 3)
print(init_theta)
```

The function needs to know how many components we want to estimate though this number may very well be unknown in practice.


## Model fitting
With the data loaded and defined initial parameters, the model is now fitted.

```{r fit_model, error=TRUE}
est_theta <- fit.full.GMCM(u = uhat,  # Ranking function is applied automatically
                           theta = init_theta,
                           method = "NM",
                           max.ite = 5000,
                           verbose = FALSE)
print(est_theta)
```

The fitting method is set to `"NM"` with a maximum number of iterations of `100`.


## Unsupervised clustering
The estimated parameters are used to calculated posterior component probabilities on which the classification is based:

```{r compute_probs}
membership <- classify(uhat, est_theta)
str(membership)
```

If it is of interest, the posterior probability can be computed directly using

```{r post_prob}
post_prob <- get.prob(uhat, est_theta)  # Compute component probabilities
```

## Results
The number of observations in each class can be e.g. counted by

```{r classes_table}
table(membership)
```

The results are also displayed by plotting

```{r plot_results}
par(mfrow = c(1,2))
plot(uhat, col = membership, asp = 1) # Plot of estimated copula values
z <- GMCM:::qgmm.marginal(uhat, theta = est_theta) # Estimate latent process
plot(z, col = membership, asp = 1) # Plot of estimated latent process
```

The fitted `theta` object can also be plotted directly:

```{r plot_theta}
plot(est_theta)
```


### Session information
This report was generated using **rmarkdown**^[2][2]^ and **knitr**^[3][3]^ under the session
given below.

```{r session-info}
sessionInfo()
```

### References
Please cite the **GMCM** paper^[1][1]^ if you use the package or shiny app.

```{r citation, echo=FALSE, results='asis'}
cites <- lapply(c("GMCM"), citation)
fmt_cites <- unlist(lapply(cites, format, style = "text"))
cat(paste0("  ", seq_along(fmt_cites), ". ", fmt_cites, "\n"))
```

[1]: http://doi.org/10.18637/jss.v070.i02
[2]: https://pkg.yihui.org/rmarkdown-book/parameterized-reports.html
[3]: https://yihui.org/knitr/demo/stitch/
