This example shows how to quickly build and run an MCMC algorithm in NIMBLE with default sampler choices.

Letâ€™s use another of the classic WinBUGS examples: the pump model.

A description can be found here. The original example can be found in our GitHub repository here.

We could load the model using `readBUGSmodel()`

, but
instead weâ€™ll show it fully here:

`library(nimble, warn.conflicts = FALSE)`

```
## nimble version 1.2.1 is loaded.
## For more information on NIMBLE and a User Manual,
## please visit https://R-nimble.org.
##
## Note for advanced users who have written their own MCMC samplers:
## As of version 0.13.0, NIMBLE's protocol for handling posterior
## predictive nodes has changed in a way that could affect user-defined
## samplers in some situations. Please see Section 15.5.1 of the User Manual.
```

```
pumpCode <- nimbleCode({
# Define relationships between nodes
for (i in 1:N){
theta[i] ~ dgamma(alpha,beta)
lambda[i] <- theta[i]*t[i]
x[i] ~ dpois(lambda[i])
}
# Set priors
alpha ~ dexp(1.0)
beta ~ dgamma(0.1,1.0)
})
# Create some contrants, data, and initial values to pass to the model builder
pumpConsts <- list(N = 10,
t = c(94.3, 15.7, 62.9, 126, 5.24,
31.4, 1.05, 1.05, 2.1, 10.5))
pumpData <- list(x = c(5, 1, 5, 14, 3, 19, 1, 1, 4, 22))
pumpInits <- list(alpha = 1, beta = 1,
theta = rep(0.1, pumpConsts$N))
```

NIMBLE provides three different workflows for running an MCMC. These
give you different levels of control over how the algorithm runs. From
least to most control, these are: the `nimbleMCMC()`

function, the `runMCMC()`

function, and the
`mcmc$run()`

MCMC object function.

In general, we recommend the `runMCMC()`

path, although
`nimbleMCMC()`

is useful for very straightforward cases.

`nimbleMCMC()`

The `nimbleMCMC()`

function handles everything from model
building to compilation to running the MCMC. It takes model code and
model inputs (constants, data, and initial values) and returns MCMC
samples. The `nimbleMCMC()`

function is easy and quick to use
but provides little control over the MCMC algorithm. Also, since the
objects (the uncompiled and compiled model and MCMC) are used only in
the function, they arenâ€™t saved and canâ€™t be manipulated for
debugging.

```
nimbleMCMC_samples <- nimbleMCMC(code = pumpCode,
constants = pumpConsts,
data = pumpData,
inits = pumpInits,
nburnin = 1000, niter = 10000)
```

`## Defining model`

`## Building model`

`## Setting data and initial values`

```
## Running calculate on model
## [Note] Any error reports that follow may simply reflect missing values in model variables.
```

`## Checking model sizes and dimensions`

`## Checking model calculations`

```
## Compiling
## [Note] This may take a minute.
## [Note] Use 'showCompilerOutput = TRUE' to see C++ compilation details.
```

`## running chain 1...`

```
## |-------------|-------------|-------------|-------------|
## |-------------------------------------------------------|
```

`head(nimbleMCMC_samples)`

```
## alpha beta
## [1,] 1.0245088 1.347820
## [2,] 1.0245088 1.047093
## [3,] 0.6799841 1.483939
## [4,] 1.1029944 1.013421
## [5,] 1.1029944 1.206070
## [6,] 0.8156879 1.353926
```

We can look at the results:

```
plot(nimbleMCMC_samples[ , 'alpha'], type = 'l', xlab = 'iteration',
ylab = expression(alpha))
```

```
plot(nimbleMCMC_samples[ , 'beta'], type = 'l', xlab = 'iteration',
ylab = expression(beta))
```

`runMCMC()`

The `runMCMC()`

function provides more control and a
closer look at what NIMBLE is doing. `runMCMC()`

takes a
NIMBLE MCMC object, so we need to back up and build these things.

First, build the NIMBLE model.

```
pumpModel <- nimbleModel(code = pumpCode, name = 'pump', constants = pumpConsts,
data = pumpData, inits = pumpInits)
```

`## Defining model`

`## Building model`

`## Setting data and initial values`

```
## Running calculate on model
## [Note] Any error reports that follow may simply reflect missing values in model variables.
```

`## Checking model sizes and dimensions`

Next, build an MCMC object for this model using
`buildMCMC()`

.

`pumpMCMC <- buildMCMC(pumpModel)`

```
## ===== Monitors =====
## thin = 1: alpha, beta
## ===== Samplers =====
## RW sampler (1)
## - alpha
## conjugate sampler (11)
## - beta
## - theta[] (10 elements)
```

We can use `runMCMC()`

on this uncompiled model if we want
to, but weâ€™ll get a warning reminding us that compiled MCMC will be much
faster. Uncompiled MCMC will be really slow, but running in R allows
easy testing and debugging. Here is how you would run it for 5
iterations:

`runMCMC(pumpMCMC, niter = 5)`

`## [Warning] Running an uncompiled MCMC algorithm. Use compileNimble() for faster execution.`

`## running chain 1...`

```
## alpha beta
## [1,] 1.000000 4.198396
## [2,] 1.000000 4.058742
## [3,] 2.051323 3.409339
## [4,] 2.051323 2.640646
## [5,] 2.051323 3.365239
```

To compile the model and MCMC object, use
`compileNimble()`

. Note that the model object must be
compiled first for the MCMC to compile.

`CpumpModel <- compileNimble(pumpModel)`

```
## Compiling
## [Note] This may take a minute.
## [Note] Use 'showCompilerOutput = TRUE' to see C++ compilation details.
```

`CpumpMCMC <- compileNimble(pumpMCMC, project = pumpModel)`

```
## Compiling
## [Note] This may take a minute.
## [Note] Use 'showCompilerOutput = TRUE' to see C++ compilation details.
```

Now we can use `runMCMC()`

on the compiled model
object.

`runMCMC_samples <- runMCMC(CpumpMCMC, nburnin = 1000, niter = 10000)`

`## running chain 1...`

```
## |-------------|-------------|-------------|-------------|
## |-------------------------------------------------------|
```

We can look at the results:

`plot(runMCMC_samples[ , 'alpha'], type = 'l', xlab = 'iteration', ylab = expression(alpha))`