nested.regression {BoomSpikeSlab}  R Documentation 
Fits a Bayesian hierarchical regression model to data nested within groups. The model is
%
y_{ig} \sim N(x_i \beta_g, \sigma^2) \\ %
1 / \sigma^2 \sim Gamma(df/2, ss/2) \\ %
\beta_g \sim N(b, V) \\ %
Optional hyperprior distributions can be supplied to the prior parameters.
%
b ~ N(prior.mean, prior.variance) \\ %
V ~ InverseWishart(df, variance.guess). \\ %
Either hyperprior can be omitted, in which case the corresponding prior parameter is assumed fixed at the usersupplied value.
NestedRegression(response,
predictors,
group.id,
residual.precision.prior = NULL,
coefficient.prior = NULL,
coefficient.mean.hyperprior = NULL,
coefficient.variance.hyperprior = NULL,
suf = NULL,
niter,
ping = niter / 10,
sampling.method = c("ASIS", "DA"),
seed = NULL)
response 
A numeric vector. The response variable to be modeled. 
predictors 
A numeric matrix of predictor variables, including an intercept term if one is desired. The number of rows must match length(response). 
group.id 
A factor (or object that can be converted using

residual.precision.prior 
An object of type

coefficient.prior 
An object of class MvnPrior, or 
coefficient.mean.hyperprior 
An object of class

coefficient.variance.hyperprior 
An object of class

suf 
A list, where each entry is of type

niter 
The desired number of MCMC iterations. 
ping 
The frequency with which to print status updates. 
sampling.method 
The MCMC sampling scheme that should be used.
If either hyperprior is set to 
seed 
The integervalued seed (or 
Note: ASIS (Yu and Meng, 2011) has slightly better MCMC convergence, but is slightly slower than the classic DA (data augmentation) method, which alternates between sampling grouplevel regression coefficients and prior parameters. Both methods are pretty fast.
A list containing MCMC draws from the posterior distribution of model parameters. Each of the following is a vector, matrix, or array, with first index corresponding to MCMC draws, and later indices to distinct parameters.
coefficients: regression coefficients.
residual.sd: the residual standard deviation from the regression model.
prior.mean: The posterior distribution of the coefficient means across groups.
prior.variance: The posterior distribution of the variance matrix describing the distribution of regression coefficients across groups.
priors: A list of the prior distributions used to fit the model.
Steven L. Scott
SimulateNestedRegressionData < function() {
beta.hyperprior.mean < c(8, 6, 7, 5)
xdim < length(beta.hyperprior.mean)
beta.hyperprior.variance <
rWishart(2 * xdim, diag(rep(1, xdim)), inverse = TRUE)
number.of.groups < 27
nobs.per.group = 23
beta < rmvn(number.of.groups,
beta.hyperprior.mean,
beta.hyperprior.variance)
residual.sd < 2.4
X < cbind(1, matrix(rnorm(number.of.groups * (xdim  1) * nobs.per.group),
ncol = xdim  1))
group.id < rep(1:number.of.groups, len = nrow(X))
y.hat < numeric(nrow(X))
for (i in 1:nrow(X)) {
y.hat[i] = sum(X[i, ] * beta[group.id[i], ])
}
y < rnorm(length(y.hat), y.hat, residual.sd)
suf < BoomSpikeSlab:::.RegressionSufList(X, y, group.id)
return(list(beta.hyperprior.mean = beta.hyperprior.mean,
beta.hyperprior.variance = beta.hyperprior.variance,
beta = beta,
residual.sd = residual.sd,
X = X,
y = y,
group.id = group.id,
suf = suf))
}
d < SimulateNestedRegressionData()
model < NestedRegression(suf = d$suf, niter = 500)