Nothing
spMisalignGLM <- function(formula, family="binomial", weights, data = parent.frame(), coords,
starting, tuning, priors, cov.model,
amcmc, n.samples,
verbose=TRUE, n.report=100, ...){
####################################################
##Check for unused args
####################################################
formal.args <- names(formals(sys.function(sys.parent())))
elip.args <- names(list(...))
for(i in elip.args){
if(! i %in% formal.args)
warning("'",i, "' is not an argument")
}
####################################################
##formula
####################################################
if(missing(formula)){stop("error: formula must be specified")}
if(is.list(formula) && is.list(coords)){
if(length(formula) != length(coords)){
stop("error: formula and coords are misspecified")
}
mod.dat <- mkMisalignYX(formula, data)
Y <- mod.dat[[1]]
X <- mod.dat[[2]]
misalign.n <- mod.dat[[3]]
misalign.p <- mod.dat[[4]]
x.names <- mod.dat[[5]]
m <- length(formula)
storage.mode(misalign.n) <- "integer"
storage.mode(misalign.p) <- "integer"
}else{
stop("error: formula is misspecified")
}
p <- ncol(X)
n <- nrow(X)
n.ltr <- m*(m+1)/2
##make sure storage mode is correct
storage.mode(Y) <- "double"
storage.mode(X) <- "double"
storage.mode(m) <- "integer"
storage.mode(p) <- "integer"
storage.mode(n) <- "integer"
####################################################
##family and weights
####################################################
if(!family %in% c("binomial","poisson"))
stop("error: family must be binomial or poisson")
if(missing(weights)){
weights <- rep(1, n)
}else if(is.list(weights) && length(weights) == m && all(sapply(weights, length) == misalign.n)){
weights<- do.call(c, weights)
}else{
stop(paste("error: weights must be a list length ", m," consisting of weight vectors of lengths ", paste(misalign.n, collapse=" "), sep=""))
}
storage.mode(weights) <- "integer"
####################################################
##sampling method
####################################################
n.batch <- 0
batch.length <- 0
accept.rate <- 0
is.amcmc <- TRUE
##require amcmc for now
if(missing(amcmc)){
stop("error: amcmc must be be specified")
## if(missing(n.samples)){stop("error: n.samples needs to be specified")}
## n.batch <- n.samples
## batch.length <- 1
## is.amcmc <- FALSE
}else{
names(amcmc) <- tolower(names(amcmc))
if(!"n.batch" %in% names(amcmc)){stop("error: n.batch must be specified in amcmc list")}
n.batch <- amcmc[["n.batch"]]
if(!"batch.length" %in% names(amcmc)){stop("error: batch.length must be specified in amcmc list")}
batch.length <- amcmc[["batch.length"]]
if(!"accept.rate" %in% names(amcmc)){
warning("accept.rate was not specified in the amcmc list and was therefore set to the default 0.43")
accept.rate <- 0.43
}else{
accept.rate <- amcmc[["accept.rate"]]
}
}
storage.mode(is.amcmc) <- "integer"
storage.mode(n.batch) <- "integer"
storage.mode(batch.length) <- "integer"
storage.mode(accept.rate) <- "double"
####################################################
##Distance matrices
####################################################
####################
##Coords
####################
if(missing(coords)){stop("error: coords must be specified")}
coords <- as.matrix(do.call(rbind, coords))
if(ncol(coords) != 2 || nrow(coords) != n){
stop("error: either the coords have more than two columns or then number of rows is different than data used in the model formula")
}
coords.D <- iDist(coords)
storage.mode(coords.D) <- "double"
####################################################
##Covariance model
####################################################
if(missing(cov.model)){stop("error: cov.model must be specified")}
if(!cov.model%in%c("gaussian","exponential","matern","spherical"))
{stop("error: specified cov.model '",cov.model,"' is not a valid option; choose, from gaussian, exponential, matern, spherical.")}
####################################################
##Starting values
####################################################
beta.starting <- 0
A.starting <- 0
phi.starting <- 0
nu.starting <- 0
w.starting <- 0
if(missing(starting)){stop("error: starting value list for the parameters must be specified")}
names(starting) <- tolower(names(starting))
if("beta" %in% names(starting)){
beta.starting <- starting[["beta"]]
if(length(beta.starting) != p){stop(paste("error: starting values for beta must be of length ",p,sep=""))}
}else{
stop("error: beta must be specified in starting")
}
if(!"a" %in% names(starting)){stop("error: A must be specified in starting")}
A.starting <- starting[["a"]]
if(length(A.starting) != n.ltr){stop(paste("error: A must be of length ",n.ltr," in starting value list",sep=""))}
if(!"phi" %in% names(starting)){stop("error: phi must be specified in starting")}
phi.starting <- starting[["phi"]]
if(length(phi.starting) != m){stop(paste("error: phi must be of length ",m," in starting value list",sep=""))}
if(cov.model == "matern"){
if(!"nu" %in% names(starting)){stop("error: nu must be specified in starting")}
nu.starting <- starting[["nu"]]
if(length(nu.starting) != m){stop(paste("error: nu must be of length ",m," in starting value list",sep=""))}
}
if(!"w" %in% names(starting)){stop("error: w must be specified in starting value list")}
w.starting <- starting[["w"]]
if(is.numeric(w.starting) && length(w.starting) == 1){
w.starting <- rep(w.starting, n)
}else if(is.list(w.starting) && length(w.starting) == m && all(sapply(w.starting, length) == misalign.n)){
w.starting <- do.call(c, w.starting)
}else{
stop(paste("error: w in the starting value list must be a scalar of length 1 or a list of length ",m, " consisting of vectors of lengths ", paste(misalign.n, collapse=" "), sep=""))
}
storage.mode(beta.starting) <- "double"
storage.mode(phi.starting) <- "double"
storage.mode(A.starting) <- "double"
storage.mode(nu.starting) <- "double"
storage.mode(w.starting) <- "double"
####################################################
##Priors
####################################################
beta.Norm <- 0
beta.prior <- "flat"
K.IW <- 0
nu.Unif <- 0
phi.Unif <- 0
if(missing(priors)) {stop("error: prior list for the parameters must be specified")}
names(priors) <- tolower(names(priors))
if("beta.norm" %in% names(priors)){
beta.Norm <- priors[["beta.normal"]]
if(!is.list(beta.Norm) || length(beta.Norm) != 2){stop("error: beta.Norm must be a list of length 2")}
if(length(beta.Norm[[1]]) != p ){stop(paste("error: beta.Norm[[1]] must be a vector of length, ",p, " with elements corresponding to betas' mean",sep=""))}
if(length(beta.Norm[[2]]) != p ){stop(paste("error: beta.Norm[[2]] must be a vector of length, ",p, " with elements corresponding to betas' sd",sep=""))}
beta.prior <- "normal"
}
if(!"k.iw" %in% names(priors)){stop("error: K.IW must be specified")}
K.IW <- priors[["k.iw"]]
if(!is.list(K.IW) || length(K.IW) != 2){stop("error: K.IW must be a list of length 2")}
if(length(K.IW[[1]]) != 1 ){stop("error: K.IW[[1]] must be of length 1 (i.e., the IW df hyperparameter)")}
if(length(K.IW[[2]]) != m^2 ){stop(paste("error: K.IW[[2]] must be a vector or matrix of length, ",m^2, ", (i.e., the IW scale matrix hyperparameter)",sep=""))}
if(!"phi.unif" %in% names(priors)){stop("error: phi.Unif must be specified")}
phi.Unif <- priors[["phi.unif"]]
if(!is.list(phi.Unif) || length(phi.Unif) != 2){stop("error: phi.Unif must be a list of length 2")}
if(length(phi.Unif[[1]]) != m){stop(paste("error: phi.Unif[[1]] must be a vector of length, ",m, "",sep=""))}
if(length(phi.Unif[[2]]) != m){stop(paste("error: phi.Unif[[2]] must be a vector of length, ",m, "",sep=""))}
if(any(phi.Unif[[2]]-phi.Unif[[1]] <= 0)){stop("error: phi.Unif has zero support")}
phi.Unif <- as.vector(t(cbind(phi.Unif[[1]],phi.Unif[[2]])))
if(cov.model == "matern"){
if(!"nu.unif" %in% names(priors)){stop("error: nu.Unif must be specified")}
nu.Unif <- priors[["nu.unif"]]
if(!is.list(nu.Unif) || length(nu.Unif) != 2){stop("error: nu.Unif must be a list of length 2")}
if(length(nu.Unif[[1]]) != m){stop(paste("error: nu.Unif[[1]] must be a vector of length, ",m, "",sep=""))}
if(length(nu.Unif[[2]]) != m){stop(paste("error: nu.Unif[[2]] must be a vector of length, ",m, "",sep=""))}
if(any(nu.Unif[[2]]-nu.Unif[[1]] <= 0)){stop("error: nu.Unif has zero support")}
nu.Unif <- as.vector(t(cbind(nu.Unif[[1]],nu.Unif[[2]])))
}
storage.mode(K.IW[[1]]) <- "double"; storage.mode(K.IW[[2]]) <- "double"
storage.mode(nu.Unif) <- "double"
storage.mode(phi.Unif) <- "double"
####################################################
##Tuning values
####################################################
beta.tuning <- 0
phi.tuning <- 0
A.tuning <- 0
nu.tuning <- 0
w.tuning <- 0
if(!missing(tuning)){
names(tuning) <- tolower(names(tuning))
if(!"beta" %in% names(tuning)){stop("error: beta must be specified in tuning value list")}
beta.tuning <- tuning[["beta"]]
if(is.matrix(beta.tuning)){
if(nrow(beta.tuning) != p || ncol(beta.tuning) != p){
stop(paste("error: if beta tuning is a matrix, it must be of dimension ",p,sep=""))
}
if(is.amcmc){
beta.tuning <- diag(beta.tuning)
}
}else if(is.vector(beta.tuning)){
if(length(beta.tuning) != p){
stop(paste("error: if beta tuning is a vector, it must be of length ",p,sep=""))
}
if(!is.amcmc){
beta.tuning <- diag(beta.tuning)
}
}else{
stop("error: beta tuning is misspecified")
}
if(!"a" %in% names(tuning)){stop("error: A must be specified in tuning value list")}
A.tuning <- as.vector(tuning[["a"]])
if(length(A.tuning) != n.ltr){stop(paste("error: A must be of length ",n.ltr," in tuning value list",sep=""))}
if(!"phi" %in% names(tuning)){stop("error: phi must be specified in tuning value list")}
phi.tuning <- tuning[["phi"]]
if(length(phi.tuning) != m){stop(paste("error: phi must be of length ",m," in tuning value list",sep=""))}
if(cov.model == "matern"){
if(!"nu" %in% names(tuning)){stop("error: nu must be specified in tuning value list")}
nu.tuning <- tuning[["nu"]]
if(length(nu.tuning) != m){stop(paste("error: nu must be of length ",m," in tuning value list",sep=""))}
}
if(!"w" %in% names(tuning)){stop("error: w must be specified in tuning value list")}
w.tuning <- tuning[["w"]]
if(is.numeric(w.tuning) && length(w.tuning) == 1){
w.tuning <- rep(w.tuning, n)
}else if(is.list(w.tuning) && length(w.tuning) == m && all(sapply(w.tuning, length) == misalign.n)){
w.tuning <- do.call(c, w.tuning)
}else{
stop(paste("error: w in the tuning value list must be a scalar of length 1 or a list of length ",m, " consisting of vectors of lengths ", paste(misalign.n, collapse=" "), sep=""))
}
}else{##no tuning provided
if(!is.amcmc){
stop("error: tuning value list must be specified")
}
beta.tuning <- rep(0.01,p)
phi.tuning <- rep(0.01,m)
A.tuning <- rep(0.01,m*(m-1)/2+m)
nu.tuning <- rep(0.01,m)
w.tuning <- rep(0.01,n)
}
storage.mode(beta.tuning) <- "double"
storage.mode(phi.tuning) <- "double"
storage.mode(A.tuning) <- "double"
storage.mode(nu.tuning) <- "double"
storage.mode(w.tuning) <- "double"
####################################################
##Other stuff
####################################################
storage.mode(n.report) <- "integer"
storage.mode(verbose) <- "integer"
####################################################
##Pack it up and off it goes
####################################################
ptm <- proc.time()
out <- .Call("spGLMMisalign_AMCMC", Y, X, misalign.p, misalign.n, m, coords.D, family, weights,
beta.prior, beta.Norm,
K.IW, nu.Unif, phi.Unif,
phi.starting, A.starting, nu.starting, beta.starting, w.starting,
phi.tuning, A.tuning, nu.tuning, beta.tuning, w.tuning,
cov.model, n.batch, batch.length, accept.rate, verbose, n.report)
run.time <- proc.time() - ptm
##parameter names
out$p.beta.theta.samples <- mcmc(t(out$p.beta.theta.samples))
col.names <- rep("null",ncol(out$p.beta.theta.samples))
if(cov.model != "matern"){
col.names <- c(x.names, rep("K",n.ltr), paste("phi[",1:m,"]",sep=""))
}else{
col.names <- c(x.names, rep("K",n.ltr), paste("phi[",1:m,"]",sep=""), paste("nu[",1:m,"]",sep=""))
}
colnames(out$p.beta.theta.samples) <- col.names
AtA <- function(x, m){
A <- matrix(0, m, m)
A[lower.tri(A, diag=TRUE)] <- x
(A%*%t(A))[lower.tri(A, diag=TRUE)]
}
K.names <- paste("K[",matrix(apply(cbind(expand.grid(1:m,1:m)), 1, function(x) paste(x, collapse=",")),m,m)[lower.tri(matrix(0,m,m), diag=TRUE)],"]",sep="")
colnames(out$p.beta.theta.samples)[colnames(out$p.beta.theta.samples)%in%"K"] <- K.names
out$p.beta.theta.samples[,K.names] <- t(apply(out$p.beta.theta.samples[,K.names,drop=FALSE], 1, AtA, m))
out$weights <- weights
out$family <- family
out$Y <- Y
out$X <- X
out$m <- m
out$misalign.p <- misalign.p
out$misalign.n <- misalign.n
out$coords <- coords
out$x.names <- x.names
out$cov.model <- cov.model
out$run.time <- run.time
class(out) <- "spMisalignGLM"
out
}
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