Nothing

```
ShrinkageRegression <- function(response,
predictors,
coefficient.groups,
residual.precision.prior = NULL,
suf = NULL,
niter,
ping = niter / 10,
seed = NULL) {
## Fit a Bayesian regression model with a shrinkage prior on the coefficient.
## The model is
##
## y[i] ~ N(x[i,] %*% beta, sigma^2)
## 1 / sigma^2 ~ Gamma(df/2, ss/2)
## group(beta, 1) ~ N(b1, v1)
## group(beta, 2) ~ N(b2, v2)
## ....
##
## In this notation, group(beta, k) ~ N(bk, vk) indicates that the subset of
## coefficients in group k are a priori independent draws from the specified
## normal distribution. In addition, each subset-level prior may include a
## hyperprior, in which case the subset-level prior parameters will be
## updated as part of the MCMC. The hyperprior has the form of independent
## priors on the mean and precision parameters.
##
## bi ~ N(prior.mean, prior.variance)
## 1.0 / vi ~ Chisq(df, guess.at.sd)
##
## Args:
## response: The numeric vector of responses.
## predictors: The matrix of predictors, including an intercept term, if
## desired.
## coefficient.groups: A list of objects of type CoefficientGroup, defining
## the pattern in which the coefficients should be shrunk together. Each
## coefficient must belong to exactly one CoefficientGroup.
## residual.precision.prior: An object of type SdPrior describing the prior
## distribution of the residual standard deviation.
## suf: An object of class RegressionSuf containing the sufficient
## statistics for the regression model. If this is NULL then it will be
## computed from 'response' and 'predictors'. If it is supplied then
## 'response' and 'predictors' are not used and can be left missing.
## niter: The desired number of MCMC iterations.
## ping: The frequency with which to print status updates.
## seed: The integer-valued seed (or NULL) to use for the C++ random number
## generator.
##
## Returns:
## A list containing MCMC draws from the posterior distribution of model
## parameters. Each of the following is a matrix, with rows corresponding
## to MCMC draws, and columns to distinct parameters.
## * coefficients: regression coefficients.
## * residual.sd: the residual standard deviation from the regression
## model.
## * group.means: The posterior distribution of the mean of each
## coefficient group. If no mean hyperprior was assigned to a
## particular group, then the value here will be a constant (the values
## supplied by the 'prior' argument to 'CoefficientGroup' for that
## group).
## * group.sds: The posterior distribution of the standard deviation of
## each coefficient group. If no sd.hyperprior was assigned to a
## particular group, then the value here will be a constant (the values
## supplied by the 'prior' argument to 'CoefficientGroup' for that
## group).
if (is.null(suf)) {
## No need to touch response or predictors if sufficient statistics are
## supplied directly.
stopifnot(is.numeric(response))
stopifnot(is.matrix(predictors),
nrow(predictors) == length(response))
stopifnot(is.list(coefficient.groups),
all(sapply(coefficient.groups, inherits, "CoefficientGroup")))
suf <- RegressionSuf(X = predictors, y = response)
}
stopifnot(inherits(suf, "RegressionSuf"))
stopifnot(is.list(coefficient.groups))
all.indices <- sort(unlist(lapply(coefficient.groups, function(x) x$indices)))
xdim <- ncol(suf$xtx)
if (any(all.indices != 1:xdim)) {
if (any(all.indices > xdim)) {
stop("One or more indices were larger than the available ",
"number of predictors.")
} else if (any(all.indices <= 0)) {
stop("All indices must be 1 or larger.")
} else if (all.indices != unique(all.indices)) {
stop("Each index can only appear in one group.")
} else {
omitted <- !(all.indices %in% 1:xdim)
omitted.indices <- all.indices[omitted]
if (length(omitted.indices) > 10) {
msg <- paste("There were ", length(omitted.indices),
" indices omitted from index groups.")
stop(msg)
} else if (length(omitted.indices > 1)) {
msg <- paste("The following indices were omitted from a ",
"coefficient groups: \n")
msg <- paste(msg, paste(omitted.indices, collapse = " "), "\n")
stop(msg)
} else {
msg <- paste("Index ", omitted.indices,
" was not present in any coefficient groups.\n")
stop(msg)
}
}
} ## done checking index groups
if (is.null(residual.precision.prior)) {
residual.precision.prior <- SdPrior(1, 1)
}
stopifnot(inherits(residual.precision.prior, "SdPrior"))
stopifnot(is.numeric(niter),
length(niter) == 1,
niter > 0)
stopifnot(is.numeric(ping),
length(ping) == 1)
if (!is.null(seed)) {
seed <- as.integer(seed)
}
ans <- .Call("boom_shrinkage_regression_wrapper",
suf,
coefficient.groups,
residual.precision.prior,
as.integer(niter),
as.integer(ping),
seed)
class(ans) <- "ShrinkageRegression"
return(ans)
}
CoefficientGroup <- function(indices,
mean.hyperprior = NULL,
sd.hyperprior = NULL,
prior = NULL) {
## Args:
## indices: A vector of integers giving the positions of the regression
## coefficients that should be viewed as exchangeable.
## mean.hyperprior: A NormalPrior object describing the hyperprior
## distribution for the average coefficient.
## sd.hyperprior: An SdPrior object describing the hyperprior distribution
## for the standard deviation of the coefficients.
## prior: An object of type NormalPrior giving the initial value of the
## distribution describing the collection of coefficients in this group.
## If either hyperprior is NULL then the corresponding prior parameter
## will not be updated. If both hyperpriors are non-NULL then this
## parameter can be left unspecified.
##
## Returns:
## An object (list) containing the arguments, with values checked for
## legality, and with names as expected by the underlying C++ code.
##
## Details:
## The model for the coefficients in this group is that they are independent
## draws from N(b0, sigma^2 / kappa), where sigma^2 is the residual variance
## from the regression model. The hyperprior distribution for this model is
## b0 ~ mean.hyperprior and kappa ~ shrinkage.hyperprior, independently.
stopifnot(is.numeric(indices),
length(unique(indices)) == length(indices),
all(indices >= 1))
if (!is.null(mean.hyperprior)) {
stopifnot(inherits(mean.hyperprior, "NormalPrior"))
}
if (!is.null(sd.hyperprior)) {
stopifnot(inherits(sd.hyperprior, "SdPrior"))
}
if (is.null(prior) && (is.null(mean.hyperprior) || is.null(sd.hyperprior))) {
stop("If either hyperprior is NULL, then an initial prior distribution ",
"must be supplied.")
}
if (!is.null(prior)) {
stopifnot(inherits(prior, "NormalPrior"))
}
ans <- list(indices = as.integer(indices),
mean.hyperprior = mean.hyperprior,
sd.hyperprior = sd.hyperprior,
prior = prior)
class(ans) <- "CoefficientGroup"
return(ans)
}
```

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