---
title: "Meta analysis and clustering with special GMCMs"
output: 
  html_document:
    self_contained: true
date: '`r Sys.time()`'
author: "Generated by the GMCM shiny app"
---

```{r knit-int, echo=FALSE, include=FALSE}
set.seed(5828993)
opts_knit$set(self.contained = TRUE)
```


## 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.

## Load data
The data is loaded and the first rows are printed

```{r load-data, include=TRUE, echo=TRUE}
ds <- read.table(file   = "{{gsub("\\", "/", data_file, fixed = TRUE)}}",
                 header = {{header}},
                 sep    = "{{sep}}",
                 quote  = "\{{quote}}")
head(ds, n = 4)
```

To reproduce the output, change the file path above to the data file of interest.

Next, the data is subset to the columns of interest.

```{r select-data, include=TRUE, echo=TRUE}
x <- ds[, {{cput(model_cols)}}]
head(x, n = 2)
```

The rescaling is modified if the 'large values indicate strong evidence' check box was checked or not.
The check box was `{{ ifelse(meta_large_vals, "checked.", "unchecked, so we reverse ranking.") }}`

```{r preprocess-data}
{{ ifelse(meta_large_vals, "u <- Uhat(x)", "u <- Uhat(-x)") }}
```


## Initial parameters
The initial parameters, as chosen in the application, are set to

```{r show-initial-params, include=TRUE, echo=TRUE}
init_par <- {{cput(init_par)}}
```


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

```{r fit_model, error=TRUE}
par <- fit.meta.GMCM(u = u,
                     init.par = init_par,
                     method = "{{meta_method}}",
                     max.ite = {{meta_max_ite}},
                     positive.rho = {{meta_positive_rho}},
                     verbose = FALSE)
print(par)
```


I.e. we set the fitting method to `"{{meta_method}}"` with a maximum number of iterations of `{{meta_max_ite}}`.

## Meta analysis with unsupervised clustering
The estimated parameters are used to calculate the local and adjusted irreproducibility discovery rates:

```{r compute_probs}
meta_IDR_thres <- {{meta_IDR_thres}}
out <- get.IDR(x, par = par,
               threshold = meta_IDR_thres) # Compute IDR
str(out)

# Use idr instead of IDR to threshold if specified
out <- get.IDR(u, par = par, threshold = meta_IDR_thres)
below <- (out[["{{meta_IDR_thres_type}}"]] < meta_IDR_thres)
out$l <- sum(below)
out$Khat <- ifelse(below, 2, 1)
```

By default, `get.IDR` computes thresholds on the adjusted IDR.
The local irreproducibility discovery rate correspond to the posterior probability of the point originating from the irreproducible component.

## Results
The classes are counted by

```{r classes_table}
table(out$Khat)
```

The results are also displayed by plotting

```{r plot_results}
plot(x, col = out$Khat, asp = 1) # Plot of raw values
plot(u, col = out$Khat, asp = 1) # Plot of copula values
z <- GMCM:::qgmm.marginal(u, theta = meta2full(par, d = ncol(u))) # Estimate latent process
plot(z, col = out$Khat, asp = 1) # Plot of estimated latent process
```

The fitted `par` object can be converted to a `theta` object which can be plotted directly:

```{r plot_theta}
plot(meta2full(par, d = ncol(u)))
```


### Session information
This report was generated using **rmarkdown**^[2][2]^ and **knitr**^[3][3]^ under the session
given below. The report utilizes [parameterized reports][2] and [`knitr::spin`][3].


```{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", "knitr", "rmarkdown"), 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://pkg.yihui.org/rmarkdown-cookbook/
