Package 'fmcmc'

Append MCMC chains (objects of class coda::mcmc)

Description

Combines two or more MCMC runs into a single run. If runs have multiple chains, it will check that all have the same number of chains, and it will join chains using the rbind function.

Usage

append_chains(...)

Arguments

Value

If mcmc.list, an object of class mcmc.list, otherwise, an object of class mcmc.

Examples

# Appending two chains
data("lifeexpect")
logpost <- function(p) {
  sum(with(lifeexpect, dnorm(
    age - p[1] - smoke * p[2] - female * p[3],
    sd = p[4], log = TRUE)
  ))
}

# Using the RAM kernel
kern <- kernel_ram(lb = c(-100, -100, -100, .00001))

init <- c(
  avg_age = 70,
  smoke   = 0,
  female  = 0,
  sd      = 1
)

ans0 <- MCMC(initial = init, fun = logpost, nsteps = 1000, seed = 22, kernel = kern)
ans1 <- MCMC(initial = ans0, fun = logpost, nsteps = 2000, seed = 55, kernel = kern)
ans2 <- MCMC(initial = ans1, fun = logpost, nsteps = 2000, seed = 1155, kernel = kern)
ans_tot <- append_chains(ans0, ans1, ans2)

# Looking at the posterior distributions (see ?lifeexpect for info about
# the model). Only the trace
op <- par(mfrow = c(2,2))
for (i in 1:4)
  coda::traceplot(ans_tot[, i, drop=FALSE])
par(op)

Description

This function is for internal use only.

Usage

check_initial(initial, nchains)

Arguments

Details

When initial is a vector, the values are recycled to form a matrix of size nchains * length(initial).

Value

A named matrix.

Examples

init <- c(.4, .1)
check_initial(init, 1)
check_initial(init, 2)

init <- matrix(1:9, ncol=3)
check_initial(init, 3)

# check_initial(init, 2) # Returns an error

Description

Built-in set of functions to be used in companion with the argument conv_checker in MCMC. These functions are not intended to be used in a context other than the MCMC function.

The object LAST_CONV_CHECK is an environment that holds information regarding the convergence checker used. This information can be updated every time that the conv_checker function is called by MCMC using the functions convergence_data_set and convergence_msg_set. The function convergence_data_get is just a wrapper of get().

The msg member of LAST_CONV_CHECK is resetted before conv_checker is called.

Usage

LAST_CONV_CHECK

convergence_data_set(x)

convergence_data_get(x)

convergence_msg_set(msg = NA_character_)

convergence_msg_get()

convergence_gelman(freq = 1000L, threshold = 1.1, check_invariant = TRUE, ...)

convergence_geweke(
  freq = 1000L,
  threshold = 0.025,
  check_invariant = TRUE,
  ...
)

convergence_heildel(freq = 1000L, ..., check_invariant = TRUE)

convergence_auto(freq = 1000L)

Arguments

Format

An object of class fmcmc_output_conv_check (inherits from environment) of length 1.

Details

convergence_gelman is a wrapper of coda::gelman.diag().

In the case of convergence_geweke, threshold sets the p-value for the null $H_0: Z = 0$, i.e. equal means between the first and last chunks of the chain. See coda::geweke.diag. This implies that the higher the threshold, the lower the probability of stopping the chain.

In the case that the chain has more than one parameter, the algorithm will return true if and only if the test fails to reject the null for all the parameters.

For the convergence_heildel, see coda::heidel.diag for details.

The convergence_auto function is the default and is just a wrapper for convergence_gelman and convergence_geweke. This function returns a convergence checker that will be either of the other two depending on whether nchains in MCMC is greater than one–in which case it will use the Gelman test–or not–in which case it will use the Geweke test.

Value

A function passed to MCMC to check automatic convergence.

Building a convergence checker

Convergence checkers are simply a function that receives as argument a matrix (or list of them) with sampled values, and returns a logical scalar with the value TRUE if the chain converged. An example of a personalized convergence checker is provided below. The frequency with which the check is performed is retrieved from the attribute "freq" from the convergence checker function, i.e., attr(..., "freq"). If missing, convergence will be checked halfway the number of steps in the chain, i.e., floor(nsteps/2).

Examples

# Example 1: Presonalized conv checker --------------------------------------
# Dummy rule, if acceptance rate is near between .2 and .3.
convergence_example <- function(x) {
  arate <- 1 - coda::rejectionRate(x)
  all(
    abs(arate - .25) < .05
  )
}

# Tell fmcmc::MCMC what is the frequency
attr(convergence_example, "freq") <- 2e3

set.seed(223)
x <- rnorm(1000)
y <- x * 2 + rnorm(1000)
logpost <- function(p) {
  sum(dnorm(y, mean = x * p, log = TRUE))
}

ans <- MCMC(
  initial = 0, fun = logpost, nsteps = 5e4,
  kernel= kernel_ram(),
  conv_checker = convergence_example
)

# Example 2: Adding information ---------------------------------------------
# Here we do two things: Save a value and set a message for the user
convergence_example_with_info <- structure(function(x) {
  arate <- 1 - coda::rejectionRate(x)
  
  # Saving a value
  if (!exists("arates", envir = LAST_CONV_CHECK, inherits = FALSE)) {
    convergence_data_set(list(arates = arate))
  } else {
    convergence_data_set(list(
      arates = rbind(convergence_data_get("arates"), arate)
    ))
  }
  
  # Setting up the message
  convergence_msg_set(
    sprintf("Current Avg. Accept. Rate: %.2f", mean(arate))
  )
  
  all(
    abs(arate - .25) < .05
  )
}, freq = 2000)


ans <- MCMC(
  initial = 0, fun = logpost, nsteps = 5e4,
  kernel= kernel_ram(),
  conv_checker = convergence_example_with_info,
  seed = 112,
  progress = FALSE
)

Description

These algorithms are used in kernel_adapt() to simplify variance-covariance recalculation at every step of the algorithm.

Usage

cov_recursive(
  X_t,
  Cov_t,
  Mean_t_prev,
  t.,
  Mean_t = NULL,
  eps = 0,
  Sd = 1,
  Ik = diag(ncol(Cov_t))
)

mean_recursive(X_t, Mean_t_prev, t.)

Arguments

Details

The variance covariance algorithm was described in Haario, Saksman and Tamminen (2002).

References

Haario, H., Saksman, E., & Tamminen, J. (2001). An adaptive Metropolis algorithm. Bernoulli, 7(2), 223–242. https://projecteuclid.org/euclid.bj/1080222083

Examples

# Generating random data (only four points to see the difference)
set.seed(1231)
n <- 3
X <- matrix(rnorm(n*4), ncol = 4)

# These two should be equal
mean_recursive(
  X_t         = X[1,],
  Mean_t_prev = colMeans(X[-1,]),
  t.          = n - 1
)
colMeans(X)

# These two should be equal
cov_recursive(
  X_t         = X[1, ], 
  Cov_t       = cov(X[-1,]), 
  Mean_t      = colMeans(X),
  Mean_t_prev = colMeans(X[-1, ]),
  t           = n-1
)
cov(X)

# Speed example -------------------------------------------------------------
set.seed(13155511)
X <- matrix(rnorm(1e3*100), ncol = 100)

ans0 <- cov(X[-1,])
t0 <- system.time({
  ans1 <- cov(X)
})

t1 <- system.time(ans2 <- cov_recursive(
  X[1, ], ans0,
  Mean_t      = colMeans(X),
  Mean_t_prev = colMeans(X[-1,]),
  t. = 1e3 - 1
))

# Comparing accuracy and speed
range(ans1 - ans2)
t0/t1

Description

The fmcmc package provides a flexible framework for implementing MCMC models using a lightweight in terms of dependencies. Among its main features, fmcmc allows:

Details

• Implementing arbitrary transition kernels.

• Incorporating convergence monitors for automatic stop.

• Out-of-the-box parallel computing implementation for running multiple chains simultaneously.

For more information see the packages vignettes:

vignette("workflow-with-fmcmc", "fmcmc")

vignette("user-defined-kernels", "fmcmc")

References

Vega Yon et al., (2019). fmcmc: A friendly MCMC framework. Journal of Open Source Software, 4(39), 1427, doi:10.21105/joss.01427

Description

These functions will no longer be included starting version 0.6-0. Instead, use the functions in mcmc-output.

Usage

last_elapsed()

LAST_MCMC

last_nsteps()

last_nchains()

last_kernel()

last_conv_checker()

last_(x)

Arguments

Format

An object of class fmcmc_last_mcmc of length 0.

Value

The function last_elapsed returns the elapsed time of the last call to MCMC. In particular, the MCMC function records the running time of R at the beginning and end of the function using proc.time(). So this function returns the difference between the two (time_end - time_start).

Description

Implementation of Haario et al. (2001)'s Adaptive Metropolis.

Usage

kernel_adapt(
  mu = 0,
  bw = 0L,
  lb = -.Machine$double.xmax,
  ub = .Machine$double.xmax,
  freq = 1L,
  warmup = 500L,
  Sigma = NULL,
  Sd = NULL,
  eps = 1e-04,
  fixed = FALSE,
  until = Inf
)

kernel_am(
  mu = 0,
  bw = 0L,
  lb = -.Machine$double.xmax,
  ub = .Machine$double.xmax,
  freq = 1L,
  warmup = 500L,
  Sigma = NULL,
  Sd = NULL,
  eps = 1e-04,
  fixed = FALSE,
  until = Inf
)

Arguments

Details

While it has been shown that under regular conditions this transition kernel generates ergodic chains even when the adaptation does not stop, some practitioners may want to stop adaptation at some point.

kernel_adapt Implements the adaptive Metropolis (AM) algorithm of Haario et al. (2001). If the value of bw is greater than zero, then the algorithm folds back AP, a previous version which is known to have ergodicity problems.

The parameter eps has two functions. The first one is to set the initial scale for the multivariate normal kernel, which is replaced after warmup steps with the actual variance-covariance computed by the main algorithm. The second usage is in the equation that ensures that the variance-covariance is greater than zero, this is, the $\varepsilon$ parameter in the original paper.

The update of the covariance matrix is done using cov_recursive() function, which makes the updates faster. The freq parameter, besides of indicating the frequency with which the updates are done, it specifies what are the samples included in each update, in other words, like a thinning parameter, only every freq samples will be used to compute the covariance matrix. Since this implementation uses the recursive formula for updating the covariance, there is no practical need to set freq != 1.

kernel_am is just an alias for kernel_adapt.

Value

An object of class fmcmc_kernel. fmcmc_kernel objects are intended to be used with the MCMC() function.

References

Haario, H., Saksman, E., & Tamminen, J. (2001). An adaptive Metropolis algorithm. Bernoulli, 7(2), 223–242. https://projecteuclid.org/euclid.bj/1080222083

See Also

Other kernels: kernel_mirror, kernel_new(), kernel_normal(), kernel_ram(), kernel_unif()

Examples

# Update every-step and wait 1,000 steps before starting to adapt
kern <- kernel_adapt(freq = 1, warmup = 1000)

# Two parameters model, the second parameter with a restricted range, i.e.
# a lower bound of 1
kern <- kernel_adapt(lb = c(-.Machine$double.xmax, 0))

Description

NMirror and UMirror transition kernels described in Thawornwattana et al. (2018).

Usage

kernel_nmirror(
  mu = 0,
  scale = 1,
  warmup = 500L,
  nadapt = 4L,
  arate = 0.4,
  lb = -.Machine$double.xmax,
  ub = .Machine$double.xmax,
  fixed = FALSE,
  scheme = "joint"
)

kernel_umirror(
  mu = 0,
  scale = 1,
  warmup = 500L,
  nadapt = 4L,
  arate = 0.4,
  lb = -.Machine$double.xmax,
  ub = .Machine$double.xmax,
  fixed = FALSE,
  scheme = "joint"
)

Arguments

Details

The kernel_nmirror and kernel_umirror functions implement simple symmetric transition kernels that pivot around an approximation of the asymptotic mean.

In the multidimensional case, this implementation just draws a vector of independent draws from the proposal kernel, instead of using, for example, a multivariate distribution of some kind. This will be implemented in the next update of the package.

During the warmup period (or burnin as described in the paper), the algorithm adapts both the scale and the reference mean of the proposal distribution. While the mean is adapted continuously, the scale is updated only a handful of times, in particular, nadapt times during the warmup time. The adaptation is done as proposed by Yang and Rodriguez (2013) in which the scale is adapted four times.

Value

An object of class fmcmc_kernel. fmcmc_kernel objects are intended to be used with the MCMC() function.

References

Thawornwattana, Y., Dalquen, D., & Yang, Z. (2018). Designing Simple and Efficient Markov Chain Monte Carlo Proposal Kernels. Bayesian Analysis, 13(4), 1037–1063. doi:10.1214/17-BA1084

Yang, Z., & Rodriguez, C. E. (2013). Searching for efficient Markov chain Monte Carlo proposal kernels. Proceedings of the National Academy of Sciences, 110(48), 19307–19312. doi:10.1073/pnas.1311790110

See Also

Other kernels: kernel_adapt(), kernel_new(), kernel_normal(), kernel_ram(), kernel_unif()

Examples

# Normal mirror kernel with 5 adaptations and 1000 steps of warmup (burnin)
kern <- kernel_nmirror(nadapt = 5, warmup = 1000)

# Same as before but using a uniform mirror and choosing a target acceptance
# rate of 24 %
kern <- kernel_umirror(nadapt = 5, warmup = 1000, arate = .24)

Description

The function kernel_new is a helper function that allows creating fmcmc_kernel objects which are passed to the MCMC() function.

Usage

kernel_new(proposal, ..., logratio = NULL, kernel_env = new.env(hash = TRUE))

Arguments

Details

The objects fmcmc_kernels are environments that in general contain the following objects:

• proposal: The function used to propose changes in the chain based on the current state. The function must return a vector of length equal to the number of parameters in the model.

• logratio: This function is called after a new state has been proposed, and is used to compute the log of the Hastings ratio.

  In the case that the logratio function is not specified, then it is assumed that the transition kernel is symmetric, this is, log-ratio is then implemented as function(env) {env$f1 - env$f0}

• ...: Further objects that are used within those functions.

Both functions, proposal and logratio, receive a single argument, an environment, which is passed by the MCMC() function during each step using the function environment().

The passed environment is actually the environment in which the MCMC function is running, in particular, this environment contains the following objects:

These are the core component of the MCMC function. The following block of code is how this is actually implemented in the package:

for (i in 1L:nsteps) {
  # Step 1. Propose
  theta1[] <- kernel$proposal(environment())
  f1       <- f(theta1)
  
  # Checking f(theta1) (it must be a number, can be Inf)
  if (is.nan(f1) | is.na(f1) | is.null(f1)) 
    stop(
      "fun(par) is undefined (", f1, ")",
      "Check either -fun- or the -lb- and -ub- parameters.",
      call. = FALSE
    )
  
  # Step 2. Hastings ratio
  if (R[i] < kernel$logratio(environment())) {
    theta0 <- theta1
    f0     <- f1
  }
  
  # Step 3. Saving the state
  ans[i,] <- theta0
  
}

For an extensive example on how to create new kernel objects see the vignette vignette("user-defined-kernels", "fmcmc").

Value

An environment of class fmcmc_kernel which contains the following:

• proposal A function that receives a single argument, an environment. This is the proposal function used within MCMC().

• logratio A function to compute log ratios of the current vs the proposed step of the chain. Also used within MCMC().

• ... Further arguments passed to kernel_new.

Behavior

In some cases, calls to the proposal() and logratio() functions in fmcmc_kernels can trigger changes or updates of variables stored within them. A concrete example is with adaptive kernels.

Adaptive Metropolis and Robust Adaptive Metropolis implemented in the functions kernel_adapt() and kernel_ram() both update a covariance matrix used during the proposal stage, and furthermore, have a warmup stage that sets the point at which both will start adapting. Because of this, both kernels have internal counters of the absolute step count which allows them activating, scaling, etc. the proposals correctly.

1. When running multiple chains, MCMC will create independent copies of a baseline passed fmcmc_kernel object. These are managed together in a fmcmc_kernel_list object.

2. Even if the chains are run in parallel, if a predefined kernel object is passed it will be updated to reflect the last state of the kernels before the MCMC call returns.

References

Brooks, S., Gelman, A., Jones, G. L., & Meng, X. L. (2011). Handbook of Markov Chain Monte Carlo. Handbook of Markov Chain Monte Carlo.

See Also

Other kernels: kernel_adapt(), kernel_mirror, kernel_normal(), kernel_ram(), kernel_unif()

Examples

# Example creating a multivariate normal kernel using the mvtnorm R package
# for a bivariate normal distribution
library(mvtnorm)

# Define your Sigma
sigma <- matrix(c(1, .2, .2, 1), ncol = 2)

# How does it looks like?
sigma
#      [,1] [,2]
# [1,]  1.0  0.2
# [2,]  0.2  1.0

# Create the kernel
kernel_mvn <- kernel_new(
  proposal = function(env) {
  env$theta0 + as.vector(mvtnorm::rmvnorm(1, mean = 0, sigma = sigma.))
  },
  sigma. = sigma
)

# As you can see, in the previous call we passed sigma as it will be used by
# the proposal function
# The logaratio function was not necesary to be passed since this kernel is
# symmetric.

Description

Gaussian Transition Kernel

Usage

kernel_normal(mu = 0, scale = 1, fixed = FALSE, scheme = "joint")

kernel_normal_reflective(
  mu = 0,
  scale = 1,
  lb = -.Machine$double.xmax,
  ub = .Machine$double.xmax,
  fixed = FALSE,
  scheme = "joint"
)

Arguments

Details

The kernel_normal function provides the canonical normal kernel with symmetric transition probabilities.

The kernel_normal_reflective implements the normal kernel with reflective boundaries. Lower and upper bounds are treated using reflecting boundaries, this is, if the proposed $\theta'$ is greater than the ub, then $\theta' - ub$ is subtracted from $ub$. At the same time, if it is less than lb, then $lb - \theta'$ is added to lb iterating until $\theta$ is within [lb, ub].

In this case, the transition probability is symmetric (just like the normal kernel).

Value

An object of class fmcmc_kernel. fmcmc_kernel objects are intended to be used with the MCMC() function.

See Also

Other kernels: kernel_adapt(), kernel_mirror, kernel_new(), kernel_ram(), kernel_unif()

Examples

# Normal kernel with a small scale (sd) of 0.05
kern <- kernel_normal(scale = 0.05)

# Using boundaries for the second parameter of a two parameter chain
# to have values in [0, 100].
kern <- kernel_normal_reflective(
  ub = c(.Machine$double.xmax, 100),
  lb = c(-.Machine$double.xmax, 0)
  )

Description

Implementation of Vihola (2012)'s Robust Adaptive Metropolis.

Usage

kernel_ram(
  mu = 0,
  eta = function(i, k) min(c(1, i^(-2/3) * k)),
  qfun = function(k) stats::rt(k, k),
  arate = 0.234,
  freq = 1L,
  warmup = 0L,
  Sigma = NULL,
  eps = 1e-04,
  lb = -.Machine$double.xmax,
  ub = .Machine$double.xmax,
  fixed = FALSE,
  until = Inf,
  constr = NULL
)

Arguments

Details

While it has been shown that under regular conditions this transition kernel generates ergodic chains even when the adaptation does not stop, some practitioners may want to stop adaptation at some point.

The idea is similar to that of the Adaptive Metropolis algorithm (AM implemented as kernel_adapt() here) with the difference that it takes into account a target acceptance rate.

The eta function regulates the rate of adaptation. The default implementation will decrease the rate of adaptation exponentially as a function of the iteration number.

$Y_n\equiv X_{n-1} + S_{n-1}U_n,\quad\mbox{where }U_n\sim q\mbox{ (the \texttt{qfun})}$

And the $S_n$ matrix is updated according to the following equation:

$S_nS_n^T = S_{n-1}\left(I + \eta_n(\alpha_n - \alpha_*)\frac{U_nU_n^T}{\|U_n\|^2}\right)S_{n-1}^T$

Value

An object of class fmcmc_kernel.

References

Vihola, M. (2012). Robust adaptive Metropolis algorithm with coerced acceptance rate. Statistics and Computing, 22(5), 997–1008. doi:10.1007/s11222-011-9269-5

See Also

Other kernels: kernel_adapt(), kernel_mirror, kernel_new(), kernel_normal(), kernel_unif()

Examples

# Setting the acceptance rate to 30 % and deferring the updates until
# after 1000 steps
kern <- kernel_ram(arate = .3, warmup = 1000)

Description

Uniform Transition Kernel

Usage

kernel_unif(min. = -1, max. = 1, fixed = FALSE, scheme = "joint")

kernel_unif_reflective(
  min. = -1,
  max. = 1,
  lb = min.,
  ub = max.,
  fixed = FALSE,
  scheme = "joint"
)

Arguments

Details

The kernel_unif function provides a uniform transition kernel. This (symmetric) kernel function by default adds the current status values between [-1,1].

The kernel_unif_reflective is similar to kernel_unif with the main difference that proposals are bounded to be within ⁠[lb, ub]⁠.

Value

An object of class fmcmc_kernel. fmcmc_kernel objects are intended to be used with the MCMC() function.

See Also

Other kernels: kernel_adapt(), kernel_mirror, kernel_new(), kernel_normal(), kernel_ram()

Examples

# Multivariate setting with 4 parameters in which we set the kernel to make
# proposals one parameter at-a-time in a random ordering.
kern <- kernel_unif(scheme = "random")

Description

A simulated data set based on statistics of life expectancy in the US at birth.

Usage

lifeexpect

Format

A 1,000 rows data frame with three columns:

• age: Years lived.

• smoke: 0/1 variable equal to 1 if the individual smokes.

• female: 0/1 variable equal to 1 if the individual is a female at birth.

Details

The data was generated using official statistics from the CDC and a study of life expectancy of smokers in the US published in the New England Journal of Medicine (see references).

According to the CDC, data from 2020 indicates that the average life expectancy of females in the US is 80.5 years vs 75.1 years for males (which declined with respect to 2019 after COVID hit the US). In Jha et al. (2013), evidence is presented indicating that individuals who smoke have at least ten years left of life expectancy compared to non-smokers.

The parameter estimates for the data generating process where:

• An average of life expectancy of 80.1.

• Smokers live 10 years less than non-smokers.

• Females live 5.4 years longer than males.

References

Jha, P., Ramasundarahettige, C., Landsman, V., Rostron, B., Thun, M., Anderson, R. N., ... Peto, R. (2013). 21st-Century Hazards of Smoking and Benefits of Cessation in the United States. New England Journal of Medicine, 368(4), 341–350. doi:10.1056/NEJMsa1211128

Arias, E., Tejada-Vera, B., & Ahmad, F. (2021). Provisional life expectancy estimates for January through June, 2020. https://www.cdc.gov/nchs/data/vsrr/VSRR10-508.pdf

Description

A flexible implementation of the Metropolis-Hastings MCMC algorithm, users can utilize arbitrary transition kernels as well as set-up an automatic stop criterion using a convergence check test.

Usage

MCMC(
  initial,
  fun,
  nsteps,
  ...,
  seed = NULL,
  nchains = 1L,
  burnin = 0L,
  thin = 1L,
  kernel = kernel_normal(),
  multicore = FALSE,
  conv_checker = NULL,
  cl = NULL,
  progress = interactive() && !multicore,
  chain_id = 1L
)

MCMC_without_conv_checker(
  initial,
  fun,
  nsteps,
  ...,
  nchains = 1L,
  burnin = 0L,
  thin = 1L,
  kernel = kernel_normal(),
  multicore = FALSE,
  conv_checker = NULL,
  cl = NULL,
  progress = interactive() && !multicore,
  chain_id = 1L
)

MCMC_OUTPUT

Arguments

Details

This function implements Markov Chain Monte Carlo (MCMC) using the Metropolis-Hastings ratio with flexible transition kernels. Users can specify either one of the available transition kernels or define one of their own (see kernels). Furthermore, it allows easy parallel implementation running multiple chains in parallel. The function also allows using convergence diagnostics tests to set-up a criterion for automatically stopping the algorithm (see convergence-checker).

The canonical form of the Metropolis Hastings algorithm consists on accepting a move from state $x$ to state $y$ based on the Hastings ratio $r(x,y)$:

$r(x,y) = \frac{h(y)q(y,x)}{h(x)q(x,y)},$

where $h$ is the unnormalized density of the specified distribution ( the posterior probability), and $q$ has the conditional probability of moving from state $x$ to $y$ (the proposal density). The move $x \to y$ is then accepted with probability

$\alpha(x,y) = \min\left(1, r(x,y)\right)$

Observe that, in the case that $q()$ is symmetric, meaning $q(x, y) = q(y, x)$, the Hastings ration reduces to $h(y)/h(x)$. Starting version 0.5-0, the value of the log unnormalized density and the proposed states y can be accessed using the functions get_logpost() and get_draws().

We now give details of the various options included in the function.

The function MCMC_without_conv_checker is for internal use only.

Value

MCMC returns an object of class coda::mcmc from the coda package. The mcmc object is a matrix with one column per parameter, and nsteps rows. If nchains > 1, then it returns a coda::mcmc.list.

While the main output of MCMC is the mcmc(.list) object, other information and intermediate outputs of the process are stored in MCMC_OUTPUT. For information about how to retrieve/set data, see mcmc-output.

Starting point

By default, if initial is of class mcmc, MCMC will take the last nchains points from the chain as starting point for the new sequence. If initial is of class mcmc.list, the number of chains in initial must match the nchains parameter.

If initial is a vector, then it must be of length equal to the number of parameters used in the model. When using multiple chains, if initial is not an object of class mcmc or mcmc.list, then it must be a numeric matrix with as many rows as chains, and as many columns as parameters in the model.

Multiple chains

When nchains > 1, the function will run multiple chains. Furthermore, if cl is not passed, MCMC will create a PSOCK cluster using makePSOCKcluster with parallel::detectCores clusters and attempt to execute using multiple cores. Internally, the function does the following:

  # Creating the cluster
  ncores <- parallel::detectCores()
  ncores <- ifelse(nchains < ncores, nchains, ncores)
  cl     <- parallel::makePSOCKcluster(ncores)
  
  # Loading the package and setting the seed using clusterRNGStream
  invisible(parallel::clusterEvalQ(cl, library(fmcmc)))
  parallel::clusterSetRNGStream(cl, .Random.seed)

When running in parallel, objects that are used within fun must be passed through ..., otherwise the cluster will return with an error.

The user controls the initial value of the parameters of the MCMC algorithm using the argument initial. When using multiple chains, i.e., nchains > 1, the user can specify multiple starting points, which is recommended. In such a case, each row of initial is use as a starting point for each of the chains. If initial is a vector and nchains > 1, the value is recycled, so all chains start from the same point (not recommended, the function throws a warning message).

Automatic stop

By default, no automatic stop is implemented. If one of the functions in convergence-checker is used, then the MCMC is done by bulks as specified by the convergence checker function, and thus the algorithm will stop if, the conv_checker returns TRUE. For more information see convergence-checker.

References

Brooks, S., Gelman, A., Jones, G. L., & Meng, X. L. (2011). Handbook of Markov Chain Monte Carlo. Handbook of Markov Chain Monte Carlo.

Vega Yon, G., & Marjoram, P. (2019). fmcmc: A friendly MCMC framework. Journal of Open Source Software, 4(39), 1427. doi:10.21105/joss.01427

See Also

get_logpost(), get_logpost() (mcmc-output) for post execution of MCMC, and ith_step() for accessing objects within an MCMC call.

Examples

# Univariate distributed data with multiple parameters ----------------------
# Parameters
set.seed(1231)
n <- 1e3
pars <- c(mean = 2.6, sd = 3)

# Generating data and writing the log likelihood function
D <- rnorm(n, pars[1], pars[2])
fun <- function(x) {
  x <- log(dnorm(D, x[1], x[2]))
  sum(x)
}

# Calling MCMC, but first, loading the coda R package for
# diagnostics
library(coda)
ans <- MCMC(
  fun, initial = c(mu=1, sigma=1), nsteps = 2e3,
  kernel = kernel_normal_reflective(scale = .1, ub = 10, lb = 0)
  )

# Ploting the output
oldpar <- par(no.readonly = TRUE)
par(mfrow = c(1,2))
boxplot(as.matrix(ans), 
        main = expression("Posterior distribution of"~mu~and~sigma),
        names =  expression(mu, sigma), horizontal = TRUE,
        col  = blues9[c(4,9)],
        sub = bquote(mu == .(pars[1])~", and"~sigma == .(pars[2]))
)
abline(v = pars, col  = blues9[c(4,9)], lwd = 2, lty = 2)

plot(apply(as.matrix(ans), 1, fun), type = "l",
     main = "LogLikelihood",
     ylab = expression(L("{"~mu,sigma~"}"~"|"~D)) 
)
par(oldpar)


# In this example we estimate the parameter for a dataset with ----------------
# With 5,000 draws from a MVN() with parameters M and S.

# Loading the required packages
library(mvtnorm)
library(coda)

# Parameters and data simulation
S <- cbind(c(.8, .2), c(.2, 1))
M <- c(0, 1)

set.seed(123)
D <- rmvnorm(5e3, mean = M, sigma = S)

# Function to pass to MCMC
fun <- function(pars) {
  # Putting the parameters in a sensible way
  m <- pars[1:2]
  s <- cbind( c(pars[3], pars[4]), c(pars[4], pars[5]) )
  
  # Computing the unnormalized log likelihood
  sum(log(dmvnorm(D, m, s)))
}

# Calling MCMC
ans <- MCMC(
  initial = c(mu0=5, mu1=5, s0=5, s01=0, s2=5), 
  fun,
  kernel  = kernel_normal_reflective(
    lb    = c(-10, -10, .01, -5, .01),
    ub    = 5,
    scale = 0.01
  ),
  nsteps  = 1e3,
  thin    = 10,
  burnin  = 5e2
)

# Checking out the outcomes
plot(ans)
summary(ans)

# Multiple chains -----------------------------------------------------------

# As we want to run -fun- in multiple cores, we have to
# pass -D- explicitly (unless using Fork Clusters)
# just like specifying that we are calling a function from the
# -mvtnorm- package.
  
fun <- function(pars, D) {
  # Putting the parameters in a sensible way
  m <- pars[1:2]
  s <- cbind( c(pars[3], pars[4]), c(pars[4], pars[5]) )
  
  # Computing the unnormalized log likelihood
  sum(log(mvtnorm::dmvnorm(D, m, s)))
}

# Two chains
ans <- MCMC(
  initial = c(mu0=5, mu1=5, s0=5, s01=0, s2=5), 
  fun,
  nchains = 2,
  kernel  = kernel_normal_reflective(
    lb    = c(-10, -10, .01, -5, .01),
    ub    = 5,
    scale = 0.01
  ),
  nsteps  = 1e3,
  thin    = 10,
  burnin  = 5e2,
  D       = D
)

summary(ans)


# Example using a user-defined cl object -------------------------------------

# A silly function that gets the first two parameters of the
# vector. Using the default multicore example will cause and error
get_m <- function(pars) {
  pars[1:2]
}

fun <- function(pars, D) {
  # Putting the parameters in a sensible way
  m <- get_m(pars)
  s <- cbind( c(pars[3], pars[4]), c(pars[4], pars[5]) )
  
  # Computing the unnormalized log likelihood
  sum(log(mvtnorm::dmvnorm(D, m, s)))
}

if (FALSE) {

  # Thi will fail with the error
  # Error in checkForRemoteErrors(val) :
  #   4 nodes produced errors; first error: could not find function "get_m"
  ans <- MCMC(
    initial = c(mu0=5, mu1=5, s0=5, s01=0, s2=5), 
    fun,
    kernel  = kernel_normal_reflective(
      lb    = c(-10, -10, .01, -5, .01),
      ub    = 5,
      scale = 0.01
    ),
    nsteps  = 1e3,
    thin    = 10,
    burnin  = 5e2,
    D       = D,
    # Multiple chains using parallel computing
    multicore = TRUE,
    nchains = 4
  )
  
  summary(ans)

}

# To solve the error, we need to build the cluster object
library(parallel)
cl <- makePSOCKcluster(4)

# Export the function `get_m` to the cluster. The function `fun` and data `D`
# are automatically exported by the `MCMC` function.
clusterExport(cl, "get_m")

# Run the MCMC
ans <- MCMC(
  initial = c(mu0=5, mu1=5, s0=5, s01=0, s2=5), 
  fun,
  kernel  = kernel_normal_reflective(
    lb    = c(-10, -10, .01, -5, .01),
    ub    = 5,
    scale = 0.01
  ),
  nsteps  = 1e3,
  thin    = 10,
  burnin  = 5e2,
  D       = D,
  # Multiple chains using parallel computing
  multicore = TRUE,
  nchains = 4,
  # Use the cluster object
  cl = cl
)

summary(ans)

Description

You can use these functions to read variables, store, and retrieve data during the MCMC process.

Usage

ith_step(x)

set_userdata(...)

get_userdata()

Arguments

Value

The function ith_step() provides access to the following elements:

• i : (int) Step (iteration) number.

• nsteps : (int) Number of steps.

• chain_id : (int) Id of the chain (goes from 1 to -nchains-)

• theta0 : (double vector) Current state of the chain.

• theta1 : (double vector) Proposed state of the chain.

• ans : (double matrix) Set of accepted states (it will be NA for rows >= i).

• draws : (double matrix) Set of proposed states (it will be NA for rows >= i).

• logpost : (double vector) Value of -fun- (it will be NA for elements >= i).

• R : (double vector) Random values from U(0,1). This is used with the Hastings ratio.

• thin : (int) Thinning (applied after the last step).

• burnin : (int) Burn-in (applied after the last step).

• conv_checker : (function) Convergence checker function.

• kernel : (fmcmc_kernel) Kernel object.

• fun : (function) Passed function to MCMC.

• f : (function) Wrapper of -fun-.

• initial : (double vector) Starting point of the chain.

The following objects always have fixed values (see ?ith_step): nchains, cl, multicore

Other available objects: cnames, funargs, MCMC_OUTPUT, passedargs, progress

The function set_userdata() returns invisible(). The only side effect is appending the information by row.

Advanced usage

The function ith_step() is a convenience function that provides access to the environment within which the main loop of the MCMC call is being evaluated. This is a wrapper of MCMC_OUTPUT$loop_envir that will either return the value x or, if missing, the entire environment. If ith_step() is called outside of the MCMC call, then it will return with an error.

For example, if you wanted to print information if the current value of logpost is greater than the previous value of logpost, you could define the objective function as follows:

f <- function(p) {

  i            <- ith_step("i")
  logpost_prev <- ith_step("logpost")[i - 1L]
  logpost_curr <- sum(dnorm(y - x*p, log = TRUE))
  
  if (logpost_prev < logpost_curr)
    cat("At a higher point!\n")
    
  return(logpost_curr)

}

In the case of the objects nchains, cl, and multicore, the function will always return the default values 1, NULL, and FALSE, respectively. Thus, the user shouldn't rely on these objects to provide information regarding runs using multiple chains. More examples below.

Examples

#' # Getting the logpost -------------------------------------------------------
set.seed(23133)
x <- rnorm(200)
y <- x*2 + rnorm(200)
f <- function(p) {
  sum(dnorm(y - x*p, log = TRUE))
}

ans <- MCMC(fun = f, initial = c(0), nsteps=2000)
plot(get_logpost(), type = "l") # Plotting the logpost from the last run


# Printing information every 500 step ---------------------------------------
# for this we use ith_step()

f <- function(p) {

  # Capturing info from within the loop
  i      <- ith_step("i")
  nsteps <- ith_step("nsteps")
  
  if (!(i %% 500)) {
  
    cat(
      "////////////////////////////////////////////////////\n",
      "Step ", i, " of ", nsteps,". Values in the loop:\n",
      "theta0: ", ith_step("theta0"), "\n",
      "theta1: ", ith_step()$theta1, "\n",
      sep = ""
    )
  }
    

  sum(dnorm(y - x*p, log = TRUE))
}

ans0 <- MCMC(fun = f, initial = c(0), nsteps=2000, progress = FALSE, seed = 22)
# ////////////////////////////////////////////////////
# Step 500 of 2000. Values in the loop:
# theta0: 2.025379
# theta1: 1.04524
# ////////////////////////////////////////////////////
# Step 1000 of 2000. Values in the loop:
# theta0: 2.145967
# theta1: 0.2054037
# ////////////////////////////////////////////////////
# Step 1500 of 2000. Values in the loop:
# theta0: 2.211691
# theta1: 2.515361
# ////////////////////////////////////////////////////
# Step 2000 of 2000. Values in the loop:
# theta0: 1.998789
# theta1: 1.33034


# Printing information if the current logpost is greater than max -----------
f <- function(p) {

  i            <- ith_step("i")
  logpost_prev <- max(ith_step("logpost")[1:(i-1)])
  logpost_curr <- sum(dnorm(y - x*p, log = TRUE))
  
  # Only worthwhile after the first step
  if ((i > 1L) && logpost_prev < logpost_curr)
    cat("At a higher point!:", logpost_curr, ", step:", i,"\n")
    
  return(logpost_curr)

}
ans1 <- MCMC(fun = f, initial = c(0), nsteps=1000, progress = FALSE, seed = 22)
# At a higher point!: -357.3584 , step: 2 
# At a higher point!: -272.6816 , step: 6 
# At a higher point!: -270.9969 , step: 7 
# At a higher point!: -269.8128 , step: 24 
# At a higher point!: -269.7435 , step: 46 
# At a higher point!: -269.7422 , step: 543 
# At a higher point!: -269.7421 , step: 788 
# Saving extra information --------------------------------------------------
data("lifeexpect")

# Defining the logposterior
logpost <- function(p) {

  # Reconding the sum of the parameters (just because) 
  # and the number of step.
  set_userdata(i = ith_step("i"), sum_of_p = sum(p))

  with(lifeexpect, {
    sum(dnorm(age - (p[1] + p[2]*smoke + p[3]*female), sd = p[4], log = TRUE))
  })
  
}

# A kernel where sd is positive, the first is average age, so we 
# make it positive too
kern <- kernel_ram(lb = c(10, -20, -20, .0001), eps = .01)
ans <- MCMC(
  initial = c(70, -2, 2, 1), fun = logpost, kernel = kern, nsteps = 1000, seed = 1
  )

# Retrieving the data
head(get_userdata())

# It also works using multiple chains
ans_two <- MCMC(
  initial = c(70, -2, 2, 1), fun = logpost, kernel = kern, nsteps = 1000, seed = 1, nchains = 2
  )
  
user_dat <- get_userdata()
lapply(user_dat, head)

Description

This environment holds a copy of the last call to MCMC, including the start and end time (to compute total elapsed time) of the call. Since the resulting object of MCMC is an object of class coda::mcmc, this is a way to capture more information in case the user needs it.

Usage

get_(x)

get_logpost()

get_draws()

get_elapsed()

get_initial()

get_fun()

get_nsteps()

get_seed()

get_nchains()

get_burnin()

get_thin()

get_kernel()

get_multicore()

get_conv_checker()

get_cl()

get_progress()

get_chain_id()

Arguments

Details

The function get_logpost returns the logposterior value at each iteration. The values correspond to a named numeric vector. If nchains > 1 then it will return a list of length nchains with the corresponding logpost values for each chain.

The function get_draws() retrieves the proposed states from the kernel function.

Examples

# Getting the logpost -------------------------------------------------------
set.seed(23133)
x <- rnorm(200)
y <- -4 + x*2 + rnorm(200)
f <- function(p) {
  sum(dnorm(y - p[1] - x*p[2], log = TRUE))
}

# Setting a RAM kernel
kern <- kernel_am(eps = 1e-2)

ans <- MCMC(fun = f, initial = c(0, 1), nsteps = 2000, kernel = kern)
plot(
  # Plotting the logpost from the last run
  -get_logpost(), 
  # Getting the number of chains
  main = paste0("nchains: ", get_nchains()),
  
  # And the elapsed time
  sub  = sprintf("Run time: %.4f(s)", get_elapsed()[3]),
  type = "l",
  log = "y"
) 

# This also works using multiple chains
ans <- MCMC(fun = f, initial = c(0, 0), nsteps=2000, nchains = 2, kernel = kern)

# In this case, just like -ans-, 
draws <- get_draws()

# Plotting proposed points vs accepted
plot(
  draws[[1]], pch = 20,
  col = adjustcolor("gray", alpha = .5),
  main = "Accepted vs proposed states\n(chain 1)"
  )
lines(ans[[1]], pch = 20, col = "tomato", lwd = 2)
legend(
  "topleft", legend = c("Accepted", "Proposed"), pch = c(NA, 20),
  col = c("tomato", "black"), lty = c(1, NA), lwd = c(2, NA)
)

Description

A simple progress bar. This function is used in MCMC when the function has been called with a single processor.

Usage

new_progress_bar(
  n,
  probs = c(0, 0.25, 0.5, 0.75, 1),
  width = getOption("width", 80),
  symbol = "/",
  ...
)

Arguments

Value

A function that can be included at the interior of a loop to mark the progress of the loop. It receives a single argument, i, which is the number of the current step.

Examples

x <- new_progress_bar(20)
for (i in 1:20) {
  Sys.sleep(2/20)
  x(i)
}

Description

Parameters' update sequence

Usage

plan_update_sequence(k, nsteps, fixed, scheme)

Arguments

Details

The parameter scheme present on the currently available kernels sets the way in which proposals are made. By default, scheme = "joint", proposals are done jointly, this is, at each step of the chain we are proposing new states for each parameter of the model. When scheme = "ordered", a sequential update schema is followed, in which, at each step of the chain, proposals are made one variable at a time, If scheme = "random", proposals are also made one variable at a time but in a random scheme.

Finally, users can specify their own sequence of proposals for the variables by passing a numeric vector to scheme, for example, if the user wants to make sequential proposals following the scheme 2, 1, 3, then scheme must be set to be scheme = c(2, 1, 3).

Value

A logical vector of size nsteps x k.

Description

Adjust a proposal according to its support by reflecting it. This is the workhorse of kernel_normal_reflective and kernel_unif_reflective. It is intended for internal use only.

Usage

reflect_on_boundaries(x, lb, ub, which)

Arguments

Value

An adjusted proposal vector.