MultiBatchModel: Constructor for MultiBatchModel

In CNPBayes: Bayesian mixture models for copy number polymorphisms

Description

Initializes a MultiBatchModel, a container for storing data, parameters, and MCMC output for mixture models with batch- and component-specific means and variances.

Initializes a MultiBatchModel, a container for storing data, parameters, and MCMC output for mixture models with batch- and component-specific means and variances.

Usage

MultiBatchModel2(dat = numeric(), hp = HyperparametersMultiBatch(),
  mp = McmcParams(iter = 1000, thin = 10, burnin = 1000, nStarts = 4),
  batches = integer())

MB(dat = numeric(), hp = HyperparametersMultiBatch(),
  mp = McmcParams(iter = 1000, thin = 10, burnin = 1000, nStarts = 4),
  batches = integer())

Arguments

dat	the data for the simulation.
hp	An object of class 'Hyperparameters' used to specify the hyperparameters of the model.
mp	An object of class 'McmcParams'
batches	an integer-vector of the different batches
dat	the data for the simulation.
batches	an integer-vector of the different batches
hp	An object of class 'Hyperparameters' used to specify the hyperparameters of the model.
mp	An object of class 'McmcParams'

Value

An object of class 'MultiBatchModel'

An object of class 'MultiBatchModel'

Examples

  model <- MultiBatchModel2(rnorm(10), batch=rep(1:2, each=5))
  set.seed(100)
  nbatch <- 3
  k <- 3
  means <- matrix(c(-2.1, -2, -1.95, -0.41, -0.4, -0.395, -0.1,
      0, 0.05), nbatch, k, byrow = FALSE)
  sds <- matrix(0.15, nbatch, k)
  sds[, 1] <- 0.3
  N <- 1000
  truth <- simulateBatchData(N = N, batch = rep(letters[1:3],
                                                length.out = N),
                             p = c(1/10, 1/5, 1 - 0.1 - 0.2),
                             theta = means,
                             sds = sds)

    truth <- simulateBatchData(N = 2500,
                               batch = rep(letters[1:3], length.out = 2500),
                               theta = means, sds = sds,
                               p = c(1/5, 1/3, 1 - 1/3 - 1/5))
    MultiBatchModel2(dat=y(truth), batches=batch(truth),
                     hp=hpList(k=3)[["MB"]])
  model <- MultiBatchModel2(rnorm(10), batch=rep(1:2, each=5))
  set.seed(100)
  nbatch <- 3
  k <- 3
  means <- matrix(c(-2.1, -2, -1.95, -0.41, -0.4, -0.395, -0.1,
      0, 0.05), nbatch, k, byrow = FALSE)
  sds <- matrix(0.15, nbatch, k)
  sds[, 1] <- 0.3
  N <- 1000
  truth <- simulateBatchData(N = N, batch = rep(letters[1:3],
                                                length.out = N),
                             p = c(1/10, 1/5, 1 - 0.1 - 0.2),
                             theta = means,
                             sds = sds)
    MB(dat=y(truth), batches=batch(truth),
       hp=hpList(k=3)[["MB"]])

CNPBayes documentation built on May 6, 2019, 4:06 a.m.