---
title: "MCMC Settings"
author: "Nicole Erler"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 4
vignette: >
  %\VignetteIndexEntry{MCMC settings}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.align = 'center'
)
library(JointAI)
options(width = 100)
```

In **JointAI**, models are estimated in the Bayesian framework, using MCMC
([Markov Chain Monte Carlo](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo))
sampling.
The sampling is done by the software [JAGS](http://mcmc-jags.sourceforge.net/) 
("Just Another Gibbs Sampler"),
which performs [Gibbs](https://en.wikipedia.org/wiki/Gibbs_sampling) sampling.
**JointAI** pre-processes the data to get it into a form that can be
passed to JAGS and writes the JAGS model. The R package 
[**rjags**](https://CRAN.R-project.org/package=rjags) is used as interface
to JAGS.

Here, we describe how to specify the arguments in the main functions that
control MCMC related settings.
To learn more about how to specify the other arguments in **JointAI** models,
check out the vignette on [*Model Specification*](https://nerler.github.io/JointAI/articles/ModelSpecification.html).

In this vignette we use the [NHANES](https://nerler.github.io/JointAI/reference/NHANES.html)
data for examples in cross-sectional data and the 
dataset [simLong](https://nerler.github.io/JointAI/reference/simLong.html) for
examples in longitudinal data.
For more info on these datasets, check out the vignette 
[*Visualizing Incomplete Data*](https://nerler.github.io/JointAI/articles/VisualizingIncompleteData.html),
in which the distributions of variables and missing values in both sets is
explored.

**Note:**<br>
In many of the examples we use `progress.bar = 'none'` which prevents printing 
of the progress of the MCMC sampling, since this would result in lengthy output
in the vignette.


## MCMC related parameters in **JointAI**
The main functions [`*_imp()`](https://nerler.github.io/JointAI/reference/model_imp.html)
have a number of arguments that specify settings for the MCMC sampling:

* `n.chains`: number of MCMC chains
* `n.adapt`: number of iterations in the adaptive phase
* `n.iter`: number of iterations in the sampling phase
* `thin`: thinning degree
* `monitor_params`: parameters/nodes to be monitored
* `seed`: optional seed value for reproducibility
* `inits`: initial values
* `parallel`: whether the MCMC chains should be sampled in parallel
* `ncores`: how many cores are used for parallel sampling


And some other parameters passed to functions from **rjags**:

* `quiet`: should printing of information be suppressed?
* `progress.bar`: type of progress bar


## Chains and Iterations
In MCMC sampling, values are drawn from a probability distribution.
The distribution the current value is drawn from depends on the previously
drawn value (but not on values before that).
Values, thus, form a chain. Once the chain has converged, its elements can be seen
as a sample from the target posterior distribution.

### Number of chains
To evaluate convergence of MCMC chains it is helpful to create multiple
chains, that have different starting values.
The argument `n.chains` selects the number of chains (by default, `n.chains = 3`).

For calculating the model summary, multiple chains are merged.


### Adaptive phase 
JAGS has an adaptive mode, in which samplers are optimized (for example the
step size is adjusted).
Samples obtained during the adaptive mode do not form a Markov chain and are 
discarded.
The argument `n.adapt` controls the length of this adaptive phase.

The default value for `n.adapt` is 100, which works well in many of the examples
considered here. Complex models may require longer adaptive phases. If the adaptive
phase is not sufficient for JAGS to optimize the samplers, a warning message will be
printed (see example below).

### Sampling iterations
`n.iter` specifies the number of iterations in the sampling phase, i.e., the length
of the MCMC chain. How many samples are required to reach convergence and to
have sufficient precision depends on the complexity of data and model, and may
range from as few as 100 to several million.

#### Side note: How to evaluate convergence?
Convergence can for instance be evaluated visually using a 
[`traceplot()`](https://nerler.github.io/JointAI/reference/traceplot.html) 
or using the Gelman-Rubin diagnostic criterion^[
Gelman, A., Meng, X. L., & Stern, H. (1996). Posterior predictive assessment 
of model fitness via realized discrepancies. Statistica Sinica, 733-760.]
(implemented in [`GR_crit()`](https://nerler.github.io/JointAI/reference/GR_crit.html),
but also returned with the model summary).
The latter compares within and between chain variability and requires the 
JointAI object to have at least two chains.

#### Side note: How to check precision?
Precision of the MCMC sample can be checked with the function 
[`MC_error()`](https://nerler.github.io/JointAI/reference/MC_error.html).
It calculates the Monte Carlo error (the error that is made since the sample
is finite) and compares is to the standard deviation of the posterior sample.
A rule of thumb is that the Monte Carlo error should not be more than 5% of the
standard deviation.^[See p. 195 of Lesaffre, E., & Lawson, A. B. (2012).
Bayesian Biostatistics. John Wiley & Sons.]


### Thinning
In settings with high autocorrelation i.e., there are no large jumps in
the chain but sampled values are always close to the previous value,
it may take many iterations before a sample is created that sufficiently
represents the whole range of the posterior distribution.

Processing of such long chains can be slow and take a lot of memory.
The parameter `thin` allows to specify if and how much the MCMC chains should
be thinned out before storing them. By default `thin = 1` is used,
which corresponds to keeping all values. A value `thin = 10` would result in
keeping every 10th value and discarding all other values.


### Examples 
#### Default settings
`n.adapt = 100` and `thin = 1` with 100 sampling iterations
```{r, message = FALSE}
mod1 <- lm_imp(SBP ~ alc, data = NHANES, n.iter = 100, progress.bar = 'none')
```

The relevant part of the model summary (obtained with [`summary()`](https://nerler.github.io/JointAI/reference/summary.JointAI.html))
shows that the first 100 iterations
(adaptive phase) were discarded, and the 100 iterations that follow form the
posterior sample. Thinning was set to 1 and there are 3 chains.
```{r, echo = FALSE}
a1 <- capture.output(print(summary(mod1)))
cat(paste0('[...]', '\n',
    paste(a1[17:21], collapse = "\n")))
```

#### Insufficient adaptation phase
```{r, message = FALSE}
mod2 <- lm_imp(SBP ~ alc, data = NHANES, n.adapt = 10, n.iter = 100, progress.bar = 'none')
```
Specifying `n.adapt = 10` results in a warning message. The relevant part of the
model summary from the resulting model is:
```{r, echo = FALSE}
a2 <- capture.output(print(summary(mod2)))
cat(paste0('[...]', '\n',
    paste(a2[18:22], collapse = "\n")))
```


#### Thinning
```{r, message = FALSE}
mod3 <- lm_imp(SBP ~ alc, data = NHANES, n.iter = 500, thin = 10, progress.bar = 'none')
```

Here, iterations 110 until 600 are used in the output, but due to thinning
interval of ten, the resulting MCMC
chains contain only 50 samples instead of 500, that is, the samples from iteration
110, 120, 130, ...

```{r, echo = FALSE}
a3 <- capture.output(print(summary(mod3)))
cat(paste0('[...]', '\n',
    paste(a3[18:22], collapse = "\n")))
```

## Parameters to follow
JAGS only saves the values of MCMC chains for those nodes for
which the user has specified that they should be monitored. This is also the
case in **JointAI**.

### What are nodes?
Nodes are variables in the Bayesian framework, i.e., everything that is observed
or unobserved.
This includes the data and parameters that are estimated, but also missing values in the
data or parts of the data that are generally unobserved, such as random effects
or latent class indicators.

### Specifying which nodes should be monitored
For this purpose, the main analysis functions `*_imp` have an argument
`monitor_params`.

For details, explanation and examples see the vignette [*Parameter Selection*](https://nerler.github.io/JointAI/articles/SelectingParameters.html).


## Initial values
Initial values are the starting point for the MCMC sampler. Setting good
initial values, i.e., initial values that are likely under the posterior 
distribution, can speed up convergence. By default,
the argument `inits = NULL`, which means that initial values for the nodes
are generated by JAGS. **JointAI** only sets the initial values for the random
number generators to allow reproducibility of the results.

The argument `seed` allows the specification of a seed value with which the
starting values of the random number generator can be reproduced.


### User specified initial values
It is possible to supply initial values directly as

* a list or
* a function.

Initial values can be specified for every unobserved node, that is, parameters
and missing values, but it is possible to only specify initial values for a 
subset of nodes.

Unless the user specified initial values contain initial values for the
random number generator (named `.RNG.name` and `.RNG.seed`), **JointAI** will
add these to the initial values. This is necessary for reproducibility of the
results but also when the MCMC chains are sampled in parallel (i.e, when 
the argument `parallel = TRUE`). 


#### Initial values in a list of lists
A list containing initial values should have the same length as the number of 
chains, where each element is a named list of initial values. Initial values
should differ between the chains.

For example, to create initial values for the parameter vector `beta` and 
the precision parameter `tau_SBP` for three chains:
```{r, message = FALSE}
init_list <- lapply(1:3, function(i) {
  list(beta = rnorm(4), 
       tau_SBP = rgamma(1, 1, 1))
})

init_list
```

The list is then passed to the argument `inits`. The amended version of the user
provided lists of initial values are stored in the JointAI object:
```{r, message = FALSE}
mod4a <- lm_imp(SBP ~ gender + age + WC, data = NHANES, progress.bar = 'none',
                inits = init_list)

mod4a$mcmc_settings$inits
```

#### Initial values as a function
Initial values can be specified by a function. The function should either take no
arguments or a single argument called `chain`, and return a named list that 
supplies values for one chain.

For example, to create initial values for the parameter vectors `beta` and `alpha`:
```{r, message = FALSE}
inits_fun <- function() {
  list(beta = rnorm(4),
       alpha = rnorm(3))
}

inits_fun()


mod4b <- lm_imp(SBP ~ gender + age + WC, data = NHANES, progress.bar = 'none',
                inits = inits_fun)

mod4b$mcmc_settings$inits
```
When a function is supplied, the function will be evaluated once per chain and
the resulting list is stored.


### For which nodes can initial values be specified?
Initial values can be specified for all unobserved stochastic nodes, i.e.,
parameters or unobserved data for which a distribution is specified in the
JAGS model.

Initial values have to be supplied in the format the parameter or unobserved
value is used in the JAGS model. 


To find out which nodes there are in a model, the function `coef()` from 
package **rjags** can be used. It returns a list with the current values of the
MCMC chains,  by default the first chain. Elements of the initial values should 
have the same structure as the elements in this list.

**Example:<br>**
We are using a longitudinal model and the `simLong` data in this example. 
The output is abbreviated to show the relevant characteristics.

```{r, eval = FALSE}
mod4c <- lme_imp(bmi ~ time + HEIGHT_M + hc + SMOKE, random = ~ time | ID,
                 data = simLong, no_model = 'time', progress.bar = 'none')

coef(mod4c$model)
```

```{r echo = FALSE, message = FALSE}
mod4c <- lme_imp(bmi ~ time + HEIGHT_M + hc + SMOKE, random = ~ time | ID,
                 data = simLong, no_model = 'time', progress.bar = 'none')

a4 <- capture.output(coef(mod4c$model))
cat(
  paste0(paste(a4[1:14], collapse = "\n"), # start
         '\n\n[...]\n\n',
         paste(a4[21:24], collapse = "\n"), # values in Xc
         '\n\n[...]\n\n',
         paste(a4[510:516], collapse = "\n"), # end Xc
         '\n\n[...]\n\n',
         paste(a4[550:555], collapse = "\n"), # values Xcat
         '\n\n[...]\n\n',
         paste(a4[1012:1022], collapse = "\n"), # end Xcat; alpha; begin b
         '\n\n[...]\n\n',
         paste(a4[1515:1524], collapse = "\n"), # end b; start b_hc
         '\n\n[...]\n\n',
         paste(a4[2018:2045], collapse = "\n"), # end b_hc; betas, delta, gamma, invD, begin mu_b
         '\n\n[...]\n\n',
         paste(a4[2540:2546], collapse = "\n"), # end mu_b; begin mu_b_hc
         '\n\n[...]\n\n',
         paste(a4[3042:3053], collapse = "\n")
  )
)

a4mod <- capture.output(mod4c$model)
```

`RinvD` is the scale matrix in the Wishart prior for the inverse of the
random effects design matrix `D`.
In the data that is passed JAGS (which is stored in the element `data_list` 
in a JointAI object), this matrix is specified as diagonal matrix,
with unknown diagonal elements:
```{r}
mod4c$data_list['RinvD']
```

These diagonal elements are estimated in the model and have a Gamma prior.
The corresponding part of the JAGS model is:
```{r, echo = FALSE}
cat(paste0('[...]\n', paste(a4mod[28:33], collapse = '\n'), '\n[...]\n'))
```

The element `RinvD` in the initial values has to be a 2 $\times$ 2 matrix,
with positive values on the diagonal and `NA` as off-diagonal elements, 
since these are fixed in the data.


The following element in the output of `coef(mod4c$model)` is `Xc`, 
the fixed effects design matrix containing baseline covariates.
The corresponding matrix in the data is
```{r}
head(mod4c$data_list$Xc, 15)
```

Again, the matrix `Xc` in the initial values has the same dimension as `Xc` in the
data, and values where there are missing values in `Xc`, e.g., `Xc[11, 2]` and
`NA` elsewhere.
There are no initial values specified for the third and fourth column, since
these columns contain the dummy variables corresponding to the categorical 
variable `SMOKE` and are calculated from a column in the matrix `Xcat`, 
which contains the original version of `SMOKE`:
```{r}
head(mod4c$data_list$Xcat)
```

The relevant part of the JAGS model is:
```{r, echo = FALSE}
cat(paste0('[...]\n',
           paste(a4mod[54:55], collapse = '\n'),
           '\n\n[...]\n\n',
           paste(a4mod[65:66], collapse = '\n'),
           '\n[...]\n'))
```

Where `Xcat` in the data has missing values (e.g., rows 37 and 39), initial
values need to be specified.

Elements that are completely unobserved, like the parameter vectors `alpha`
and `beta`, the random effects `b` or scalar parameters `delta_SMOKE` and 
`gamma_SMOKE` are entirely specified in the initial values.


## Parallel sampling
To reduce computational time, it is possible to perform sampling of multiple
MCMC chains in parallel on multiple cores. This can be specified by setting
the argument `parallel = TRUE`. The maximum number of cores to be used can be
set with the argument `ncores`. By default, this is two less than the number of
cores available on the machine, but never more than the number of MCMC chains.

Parallel computation is done using the R packages [**foreach**](https://CRAN.R-project.org/package=foreach) and [**doParallel**](https://CRAN.R-project.org/package=doParallel).
Unfortunately, currently it is not possible to obtain a progress bar when using
parallel computation.


## Other arguments
There are two more arguments in `*_imp()` that
are passed directly to the **rjags** functions `jags.model()` or `coda.samples()`:

* `quiet`: should messages generated during compilation be suppressed?
* `progress.bar`: allows to select the type of progress bar. Possible values
are `"text"`, `"gui"` and `"none"`.
