R: Spatial factor analysis using a Bayesian hierarchical model.

bfa_sp {spBFA}

R Documentation

Spatial factor analysis using a Bayesian hierarchical model.

Description

bfa_sp is a Markov chain Monte Carlo (MCMC) sampler for a Bayesian spatial factor analysis model. The spatial component is introduced using a Probit stick-breaking process prior on the factor loadings. The model is implemented using a Bayesian hierarchical framework.

Usage

bfa_sp(
  formula,
  data,
  dist,
  time,
  K,
  L = Inf,
  trials = NULL,
  family = "normal",
  temporal.structure = "exponential",
  spatial.structure = "discrete",
  starting = NULL,
  hypers = NULL,
  tuning = NULL,
  mcmc = NULL,
  seed = 54,
  gamma.shrinkage = TRUE,
  include.space = TRUE,
  clustering = TRUE
)

Arguments

`formula`	A `formula` object, corresponding to the spatial factor analysis model. The response must be on the left of a `~` operator, and the terms on the right must indicate the covariates to be included in the fixed effects. If no covariates are desired a zero should be used, `~ 0`.
`data`	A required `data.frame` containing the variables in the model. The data frame must contain `M x O x Nu` rows. Here, `M` represents the number of spatial locations, `O` the number of different observation types and `Nu` the number of temporal visits. The observations must be first be ordered spatially, second by observation type and then temporally. This means that the first `M x O` observations come from the first time point and the first `M` observations come the first spatial observation type.
`dist`	A `M x M` dimensional distance matrix. For a `discrete` spatial process the matrix contains binary adjacencies that dictate the spatial neighborhood structure and for `continuous` spatial processes the matrix should be a continuous distance matrix (e.g., Euclidean).
`time`	A `Nu` dimensional vector containing the observed time points in increasing order.
`K`	A scalar that indicates the dimension (i.e., quantity) of latent factors.
`L`	The number of latent clusters. If finite, a scalar indicating the number of clusters for each column of the factor loadings matrix. By default `L` is set at `Inf` so that the Probit stick-breaking process becomes an infinite mixture model.
`trials`	A variable in `data` that contains the number of trials for each of the binomial observations. If there is no count data, `trials` should be left missing.
`family`	Character string indicating the distribution of the observed data. Options include: `"normal"`, `"probit"`, `"tobit"`, and `"binomial"`. `family` must have either `O` or `1` dimension(s) (the one populates the rest). Any combination of likelihoods can be used.
`temporal.structure`	Character string indicating the temporal kernel. Options include: `"exponential"` and `"ar1"`.
`spatial.structure`	Character string indicating the type of spatial process. Options include: `"continuous"` (i.e., Gaussian process with exponential kernel) and `"discrete"` (i.e., proper CAR).
`starting`	Either `NULL` or a `list` containing starting values to be specified for the MCMC sampler. If `NULL` is not chosen then none, some or all of the starting values may be specified. When `NULL` is chosen then default starting values are automatically generated. Otherwise a `list` must be provided with names `Beta`, `Delta`, `Sigma2`, `Kappa`, `Rho`, `Upsilon` or `Psi` containing appropriate objects. `Beta` (or `Delta`) must either be a `P` (or `K`) dimensional vector or a scalar (the scalar populates the entire vector). `Sigma2` must be either a `M x (O - C)` matrix or a scalar. `Kappa` must be a `O x O` dimensional matrix, `Rho` a scalar, `Upsilon` a `K x K` matrix, and `Psi` a scalar.
`hypers`	Either `NULL` or a `list` containing hyperparameter values to be specified for the MCMC sampler. If `NULL` is not chosen then none, some or all of the hyperparameter values may be specified. When `NULL` is chosen then default hyperparameter values are automatically generated. These default hyperparameters are described in detail in (Berchuck et al.). Otherwise a `list` must be provided with names `Beta`, `Delta`, `Sigma2`, `Kappa`, `Rho`, `Upsilon` or `Psi` containing further hyperparameter information. These objects are themselves `lists` and may be constructed as follows. `Beta` is a `list` with two objects, `MuBeta` and `SigmaBeta`. These values represent the prior mean and variance parameters for the multivariate normal prior. `Delta` is a `list` with two objects, `A1` and `A2`. These values represent the prior shape parameters for the multiplicative Gamma shrinkage prior. `Sigma2` is a `list` with two objects, `A` and `B`. These values represent the shape and scale for the variance parameters. `Kappa` is a `list` with two objects, `SmallUpsilon` and `BigTheta`. `SmallUpsilon` represents the degrees of freedom parameter for the inverse-Wishart hyperprior and must be a real number scalar, while `BigTheta` represents the scale matrix and must be a `O x O` dimensional positive definite matrix. `Rho` is a `list` with two objects, `ARho` and `BRho`. `ARho` represents the lower bound for the uniform hyperprior, while `BRho` represents the upper bound. The bounds must be specified carefully. This is only specified for continuous spatial processes. `Upsilon` is a `list` with two objects, `Zeta` and `Omega`. `Zeta` represents the degrees of freedom parameter for the inverse-Wishart hyperprior and must be a real number scalar, while `Omega` represents the scale matrix and must be a `K x K` dimensional positive definite matrix. `Psi` is a `list` with two objects, dependent on if the temporal kernel is `exponential` or `ar1`. For `exponential`, the two objects are `APsi` and `BPsi`. `APsi` represents the lower bound for the uniform hyperprior, while `BPsi` represents the upper bound. The bounds must be specified carefully. For `ar1`, the two objects are `Beta` and `Gamma`, which are the two shape parameters of a Beta distribution shifted to have domain in (-1, 1).
`tuning`	Either `NULL` or a `list` containing tuning values to be specified for the MCMC Metropolis steps. If `NULL` is not chosen then all of the tuning values must be specified. When `NULL` is chosen then default tuning values are automatically generated to `1`. Otherwise a `list` must be provided with names `Psi`, or `Rho`. Each of these entries must be scalars containing tuning variances for their corresponding Metropolis updates. `Rho` is only specified for continuous spatial processes.
`mcmc`	Either `NULL` or a `list` containing input values to be used for implementing the MCMC sampler. If `NULL` is not chosen then all of the MCMC input values must be specified. `NBurn`: The number of sampler scans included in the burn-in phase. (default = `10,000`) `NSims`: The number of post-burn-in scans for which to perform the sampler. (default = `10,000`) `NThin`: Value such that during the post-burn-in phase, only every `NThin`-th scan is recorded for use in posterior inference (For return values we define, NKeep = NSims / NThin (default = `1`). `NPilot`: The number of times during the burn-in phase that pilot adaptation is performed (default = `20`)
`seed`	An integer value used to set the seed for the random number generator (default = 54).
`gamma.shrinkage`	A logical indicating whether a gamma shrinkage process prior is used for the variances of the factor loadings columns. If FALSE, the hyperparameters (A1 and A2) indicate the shape and rate for a gamma prior on the precisions. Default is TRUE.
`include.space`	A logical indicating whether a spatial process should be included. Default is TRUE, however if FALSE the spatial correlation matrix is fixed as an identity matrix. This specification overrides the `spatial.structure` input.
`clustering`	A logical indicating whether the Bayesian non-parametric process should be used, default is TRUE. If FALSE is specified each column is instead modeled with an independent spatial process.

Details

Details of the underlying statistical model proposed by Berchuck et al. 2019. are forthcoming.

Value

bfa_sp returns a list containing the following objects

lambda: NKeep x (M x O x K) matrix of posterior samples for factor loadings matrix lambda. The labels for each column are Lambda_O_M_K.
eta: NKeep x (Nu x K) matrix of posterior samples for the latent factors eta. The labels for each column are Eta_Nu_K.
beta: NKeep x P matrix of posterior samples for beta.
sigma2: NKeep x (M * (O - C)) matrix of posterior samples for the variances sigma2. The labels for each column are Sigma2_O_M.
kappa: NKeep x ((O * (O + 1)) / 2) matrix of posterior samples for kappa. The columns have names that describe the samples within them. The row is listed first, e.g., Kappa3_2 refers to the entry in row 3, column 2.
delta: NKeep x K matrix of posterior samples for delta.
tau: NKeep x K matrix of posterior samples for tau.
upsilon: NKeep x ((K * (K + 1)) / 2) matrix of posterior samples for Upsilon. The columns have names that describe the samples within them. The row is listed first, e.g., Upsilon3_2 refers to the entry in row 3, column 2.
psi: NKeep x 1 matrix of posterior samples for psi.
xi: NKeep x (M x O x K) matrix of posterior samples for factor loadings cluster labels xi. The labels for each column are Xi_O_M_K.
rho: NKeep x 1 matrix of posterior samples for rho.
metropolis: 2 (or 1) x 3 matrix of metropolis acceptance rates, updated tuners, and original tuners that result from the pilot adaptation.
runtime: A character string giving the runtime of the MCMC sampler.
datobj: A list of data objects that are used in future bfa_sp functions and should be ignored by the user.
dataug: A list of data augmentation objects that are used in future bfa_sp functions and should be ignored by the user.

References

Reference for Berchuck et al. 2019 is forthcoming.

Examples


###Load womblR for example visual field data
library(womblR)

###Format data for MCMC sampler
blind_spot <- c(26, 35) # define blind spot
VFSeries <- VFSeries[order(VFSeries$Location), ] # sort by location
VFSeries <- VFSeries[order(VFSeries$Visit), ] # sort by visit
VFSeries <- VFSeries[!VFSeries$Location %in% blind_spot, ] # remove blind spot locations
dat <- data.frame(Y = VFSeries$DLS / 10) # create data frame with scaled data
Time <- unique(VFSeries$Time) / 365 # years since baseline visit
W <- HFAII_Queen[-blind_spot, -blind_spot] # visual field adjacency matrix (data object from womblR)
M <- dim(W)[1] # number of locations

###Prior bounds for psi
TimeDist <- as.matrix(dist(Time))
BPsi <- log(0.025) / -min(TimeDist[TimeDist > 0])
APsi <- log(0.975) / -max(TimeDist)

###MCMC options
K <- 10 # number of latent factors
O <- 1 # number of spatial observation types
Hypers <- list(Sigma2 = list(A = 0.001, B = 0.001),
               Kappa = list(SmallUpsilon = O + 1, BigTheta = diag(O)),
               Delta = list(A1 = 1, A2 = 20),
               Psi = list(APsi = APsi, BPsi = BPsi),
               Upsilon = list(Zeta = K + 1, Omega = diag(K)))
Starting <- list(Sigma2 = 1,
                 Kappa = diag(O),
                 Delta = 2 * (1:K),
                 Psi = (APsi + BPsi) / 2,
                 Upsilon = diag(K))
Tuning <- list(Psi = 1)
MCMC <- list(NBurn = 1000, NSims = 1000, NThin = 2, NPilot = 5)

###Fit MCMC Sampler
reg.bfa_sp <- bfa_sp(Y ~ 0, data = dat, dist = W, time = Time,  K = 10, 
                     starting = Starting, hypers = Hypers, tuning = Tuning, mcmc = MCMC,
                     L = Inf,
                     family = "tobit",
                     trials = NULL,
                     temporal.structure = "exponential",
                     spatial.structure = "discrete",
                     seed = 54, 
                     gamma.shrinkage = TRUE,
                     include.space = TRUE,
                     clustering = TRUE)

###Note that this code produces the pre-computed data object "reg.bfa_sp"
###More details can be found in the vignette.

[Package spBFA version 1.3 Index]