Nothing
spMisalignLM <- function(formula, 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"
####################################################
##sampling method
####################################################
n.batch <- 0
batch.length <- 0
accept.rate <- 0
is.amcmc <- TRUE
if(missing(amcmc)){
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.")}
####################################################
##Priors
####################################################
beta.Norm <- 0
#beta.prior <- "flat"
K.prior <- 0
K.prior.name <- 0
Psi.prior <- 0
nu.Unif <- 0
phi.Unif <- 0
##nugget <- FALSE
if(missing(priors)) {stop("error: prior list for the parameters must be specified")}
names(priors) <- tolower(names(priors))
if("beta.norm" %in% names(priors)){
warning("beta.norm prior is not yet implemented. Switching to beta flat")
}
beta.prior <- "flat"
## if("beta.norm" %in% names(priors)){
## beta.Norm <- priors[["beta.norm"]]
## 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, "",sep=""))}
## if(length(beta.Norm[[2]]) != p^2){stop(paste("error: beta.Norm[[2]] must be a ",p,"x",p," covariance matrix",sep=""))}
## beta.prior <- "normal"
## }
if("k.iw" %in% names(priors)){
K.prior <- priors[["k.iw"]]
if(!is.list(K.prior) || length(K.prior) != 2){stop("error: K.IW must be a list of length 2")}
if(length(K.prior[[1]]) != 1){stop("error: K.IW[[1]] must be of length 1 (i.e., the IW df hyperparameter)")}
if(length(K.prior[[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=""))}
K.prior.name <- "IW"
}else if("a.norm" %in% names(priors)){
K.prior <- priors[["a.norm"]]
if(!is.list(K.prior) || length(K.prior) != 2){stop("error: A.Norm must be a list of length 2")}
if(length(K.prior[[1]]) != n.ltr){stop(paste("error: A.Norm[[1]] must be a vector of length, ",n.ltr, "",sep=""))}
if(length(K.prior[[2]]) != n.ltr){stop(paste("error: A.Norm[[2]] must be a vector of length, ",n.ltr, "",sep=""))}
K.prior.name <- "normal"
}else{
stop("error: a valid prior for K or A must be specified")
}
if(!"psi.ig" %in% names(priors)){
stop("error: psi.ig prior must be specified. The no nugget model is not yet implemented.")
}
if("psi.ig" %in% names(priors)){
Psi.prior <- priors[["psi.ig"]]
if(!is.list(Psi.prior) || length(Psi.prior) != 2){stop("error: Psi.IG must be a list of length 2")}
if(length(Psi.prior[[1]]) != m){stop(paste("error: Psi.IG[[1]] must be a vector of length, ",m, "",sep=""))}
if(length(Psi.prior[[2]]) != m){stop(paste("error: Psi.IG[[2]] must be a vector of length, ",m, "",sep=""))}
nugget <- TRUE
}
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.prior[[1]]) <- "double"; storage.mode(K.prior[[2]]) <- "double"
if(nugget){
storage.mode(Psi.prior[[1]]) <- "double"; storage.mode(Psi.prior[[2]]) <- "double"
}
storage.mode(nu.Unif) <- "double"
storage.mode(phi.Unif) <- "double"
storage.mode(nugget) <- "integer"
####################################################
##Starting values
####################################################
beta.starting <- 0
A.starting <- 0
Psi.starting <- 0
phi.starting <- 0
nu.starting <- 0
if(missing(starting)){stop("error: starting value list for the parameters must be specified")}
names(starting) <- tolower(names(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(nugget){
if(!"psi" %in% names(starting)){stop("error: Psi is specified as diagonal so Psi must be specified in starting value list")}
Psi.starting <- as.vector(starting[["psi"]])
if(length(Psi.starting) != m){stop(paste("error: Psi is specified as diagonal so Psi must be of length ",m," 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=""))}
}
storage.mode(beta.starting) <- "double"
storage.mode(phi.starting) <- "double"
storage.mode(A.starting) <- "double"
storage.mode(Psi.starting) <- "double"
storage.mode(nu.starting) <- "double"
####################################################
##Tuning values
####################################################
phi.tuning <- 0
A.tuning <- 0
Psi.tuning <- 0
nu.tuning <- 0
if(missing(tuning)){stop("error: tuning value vector for the spatial parameters must be specified")}
names(tuning) <- tolower(names(tuning))
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(nugget){
if(!"psi" %in% names(tuning)){stop("error: Psi is specified as diagonal so Psi must be specified in tuning value list")}
Psi.tuning <- as.vector(tuning[["psi"]])
if(length(Psi.tuning) != m){stop(paste("error: Psi is specified as diagonal so Psi must be of length ",m," 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=""))}
}
storage.mode(phi.tuning) <- "double"
storage.mode(A.tuning) <- "double"
storage.mode(Psi.tuning) <- "double"
storage.mode(nu.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("spMisalign", Y, X, misalign.p, misalign.n, m, coords.D,
beta.prior, beta.Norm,
K.prior, K.prior.name,
Psi.prior,
nu.Unif, phi.Unif,
phi.starting, A.starting, Psi.starting, nu.starting,
phi.tuning, A.tuning, Psi.tuning, nu.tuning,
nugget, cov.model, is.amcmc, n.batch, batch.length, accept.rate, verbose, n.report)
run.time <- proc.time() - ptm
out$p.theta.samples <- mcmc(t(out$p.theta.samples))
col.names <- rep("null",ncol(out$p.theta.samples))
if(!nugget && cov.model != "matern"){
col.names <- c(rep("K",n.ltr), paste("phi[",1:m,"]",sep=""))
}else if(nugget && cov.model != "matern"){
col.names <- c(rep("K",n.ltr), rep("Psi",m), paste("phi[",1:m,"]",sep=""))
}else if(!nugget && cov.model == "matern"){
col.names <- c(rep("K",n.ltr), paste("phi[",1:m,"]",sep=""), paste("nu[",1:m,"]",sep=""))
}else{
col.names<- c(rep("K",n.ltr), rep("Psi",m), paste("phi[",1:m,"]",sep=""), paste("nu[",1:m,"]",sep=""))
}
colnames(out$p.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$p.theta.samples)[colnames(out$p.theta.samples)%in%"K"] <- K.names
out$p.theta.samples[,K.names] <- t(apply(out$p.theta.samples[,K.names,drop=FALSE], 1, AtA, m))
if(nugget){
Psi.names <- paste("Psi[",1:m,",",1:m,"]",sep="")
colnames(out$p.theta.samples)[colnames(out$p.theta.samples)%in%"Psi"] <- Psi.names
}
out$Y <- Y
out$X <- X
out$m <- m
out$misalign.p <- misalign.p
out$misalign.n <- misalign.n
out$coords <- coords
out$cov.model <- cov.model
out$nugget <- nugget
out$beta.prior <- beta.prior
out$beta.Norm <- beta.Norm
out$x.names <- x.names
out$run.time <- run.time
class(out) <- "spMisalignLM"
out
}
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