configureRJ: Configure Reversible Jump for Variable Selection

In nimble: MCMC, Particle Filtering, and Programmable Hierarchical Modeling

Configure Reversible Jump for Variable Selection

Description

Modifies an MCMC configuration object to perform a reversible jump MCMC sampling for variable selection, using a univariate normal proposal distribution. Users can control the mean and scale of the proposal. This function supports two different types of model specification: with and without indicator variables.

Usage

configureRJ(
  conf,
  targetNodes,
  indicatorNodes = NULL,
  priorProb = NULL,
  control = list(mean = NULL, scale = NULL, fixedValue = NULL)
)

Arguments

conf	An MCMCconf object.
targetNodes	A character vector, specifying the nodes and/or variables for which variable selection is to be performed. Nodes may be specified in their indexed form, 'y[1, 3]'. Alternatively, nodes specified without indexing will be expanded, e.g., 'x' will be expanded to 'x[1]', 'x[2]', etc.
indicatorNodes	An optional character vector, specifying the indicator nodes and/or variables paired with targetNodes. Nodes may be specified in their indexed form, 'y[1, 3]'. Alternatively, nodes specified without indexing will be expanded, e.g., 'x' will be expanded to 'x[1]', 'x[2]', etc. Nodes must be provided consistently with targetNodes. See details.
priorProb	An optional value or vector of prior probabilities for each node to be in the model. See details.
control	An optional list of control arguments: mean. The mean of the normal proposal distribution (default = 0). scale. The standard deviation of the normal proposal distribution (default = 1). fixedValue. Value for the variable when it is out of the model, which can be used only when priorProb is provided (default = 0). If specified when indicatorNodes is passed, a warning is given and fixedValue is ignored.

Details

This function modifies the samplers in MCMC configuration object for each of the nodes provided in the targetNodes argument. To these elements two samplers are assigned: a reversible jump sampler to transition the variable in/out of the model, and a modified version of the original sampler, which performs updates only when the target node is already in the model.

configureRJ can handle two different ways of writing a NIMBLE model, either with or without indicator variables. When using indicator variables, the indicatorNodes argument must be provided. Without indicator variables, the priorProb argument must be provided. In the latter case, the user can provide a non-zero value for fixedValue if desired.

Note that this functionality is intended for variable selection in regression-style models but may be useful for other situations as well. At the moment, setting a variance component to zero and thereby removing a set of random effects that are explicitly part of a model will not work because MCMC sampling in that case would need to propose values for multiple parameters (the random effects), whereas the current functionality only proposes adding/removing a single model node.

Value

NULL configureRJ modifies the input MCMC configuration object in place.

Author(s)

Sally Paganin, Perry de Valpine, Daniel Turek

References

Peter J. Green. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711-732.

See Also

samplers configureMCMC

Examples

## Not run: 

## Linear regression with intercept and two covariates, using indicator variables

code <- nimbleCode({
  beta0 ~ dnorm(0, sd = 100)
  beta1 ~ dnorm(0, sd = 100)
  beta2 ~ dnorm(0, sd = 100)
  sigma ~ dunif(0, 100) 
  z1 ~ dbern(psi)   ## indicator variable associated with beta1
  z2 ~ dbern(psi)   ## indicator variable associated with beta2
  psi ~ dunif(0, 1) ## hyperprior on inclusion probability
  for(i in 1:N) {
    Ypred[i] <- beta0 + beta1 * z1 * x1[i] + beta2 * z2 * x2[i]
    Y[i] ~ dnorm(Ypred[i], sd = sigma)
  }
})

## simulate some data
set.seed(1)
N <- 100
x1 <- runif(N, -1, 1)
x2 <- runif(N, -1, 1) ## this covariate is not included
Y <- rnorm(N, 1 + 2.5 * x1, sd = 1)

## build the model
rIndicatorModel <- nimbleModel(code, constants = list(N = N),
                               data = list(Y = Y, x1 = x1, x2 = x2), 
                               inits = list(beta0 = 0, beta1 = 0, beta2 = 0, sigma = sd(Y),
                               z1 = 1, z2 = 1, psi = 0.5))

indicatorModelConf <- configureMCMC(rIndicatorModel)

## Add reversible jump  
configureRJ(conf = indicatorModelConf,        ## model configuration
            targetNodes = c("beta1", "beta2"), ## coefficients for selection
            indicatorNodes = c("z1", "z2"),    ## indicators paired with coefficients
            control = list(mean = 0, scale = 2))

indicatorModelConf$addMonitors("beta1", "beta2", "z1", "z2")

rIndicatorMCMC <- buildMCMC(indicatorModelConf)
cIndicatorModel <- compileNimble(rIndicatorModel)
cIndicatorMCMC <- compileNimble(rIndicatorMCMC, project = rIndicatorModel)

set.seed(1)
samples <- runMCMC(cIndicatorMCMC, 10000, nburnin = 6000)

## posterior probability to be included in the mode
mean(samples[ , "z1"])
mean(samples[ , "z2"])

## posterior means when in the model
mean(samples[ , "beta1"][samples[ , "z1"] != 0])
mean(samples[ , "beta2"][samples[ , "z2"] != 0])


## Linear regression with intercept and two covariates, without indicator variables

code <- nimbleCode({
  beta0 ~ dnorm(0, sd = 100)
  beta1 ~ dnorm(0, sd = 100)
  beta2 ~ dnorm(0, sd = 100)
  sigma ~ dunif(0, 100)
  for(i in 1:N) {
    Ypred[i] <- beta0 + beta1 * x1[i] + beta2 * x2[i]
    Y[i] ~ dnorm(Ypred[i], sd = sigma)
  }
})

rNoIndicatorModel <- nimbleModel(code, constants = list(N = N),
                                 data = list(Y = Y, x1 = x1, x2 = x2), 
                                 inits=  list(beta0 = 0, beta1 = 0, beta2 = 0, sigma = sd(Y)))

noIndicatorModelConf <- configureMCMC(rNoIndicatorModel)

## Add reversible jump  
configureRJ(conf = noIndicatorModelConf,      ## model configuration
            targetNodes = c("beta1", "beta2"), ## coefficients for selection   
            priorProb = 0.5,                   ## prior probability of inclusion
            control = list(mean = 0, scale = 2))

## add monitors
noIndicatorModelConf$addMonitors("beta1", "beta2")
rNoIndicatorMCMC <- buildMCMC(noIndicatorModelConf) 

cNoIndicatorModel <- compileNimble(rNoIndicatorModel)
cNoIndicatorMCMC <- compileNimble(rNoIndicatorMCMC, project = rNoIndicatorModel)

set.seed(1)
samples <- runMCMC(cNoIndicatorMCMC, 10000, nburnin = 6000)

## posterior probability to be included in the mode
mean(samples[ , "beta1"] != 0)
mean(samples[ , "beta2"] != 0)

## posterior means when in the model
mean(samples[ , "beta1"][samples[ , "beta1"] != 0])
mean(samples[ , "beta2"][samples[ , "beta2"] != 0])

## End(Not run)

nimble documentation built on July 9, 2023, 5:24 p.m.