Nothing
spGLM <- function(formula, family="binomial", weights, data = parent.frame(), coords, knots,
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(class(formula) == "formula"){
if(inherits(formula, "formula")){
holder <- parseFormula(formula, data)
Y <- holder[[1]]
X <- as.matrix(holder[[2]])
x.names <- holder[[3]]
}else{
stop("error: formula is misspecified")
}
p <- ncol(X)
n <- nrow(X)
##make sure storage mode is correct
storage.mode(Y) <- "double"
storage.mode(X) <- "double"
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)}
if(length(weights) != n){stop("error: weights vector is misspecified")}
storage.mode(weights) <- "integer"
####################################################
##sampling method
####################################################
n.batch <- 0
batch.length <- 0
accept.rate <- 0
if(missing(amcmc)){
if(missing(n.samples)){stop("error: n.samples need to be specified")}
n.batch <- n.samples
batch.length <- 1
is.amcmc <- FALSE
}else{
is.amcmc <- TRUE
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"]]
}
n.samples <- n.batch*batch.length
}
storage.mode(is.amcmc) <- "integer"
storage.mode(n.samples) <- "integer"
storage.mode(n.batch) <- "integer"
storage.mode(batch.length) <- "integer"
storage.mode(accept.rate) <- "double"
####################################################
##Fit non-spatial model if specified
####################################################
if(missing(coords)){
##Starting
beta.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{
if(family=="poisson"){
beta.starting <- coefficients(glm(Y~X-1, family="poisson"))
}else{
beta.starting <- coefficients(glm((Y/weights)~X-1, weights=weights, family="binomial"))
}
}
storage.mode(beta.starting) <- "double"
##Priors
beta.Norm <- 0
beta.prior <- "flat"
if(missing(priors)) {stop("error: prior list for the parameters must be specified")}
names(priors) <- tolower(names(priors))
if("beta.normal" %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"
}
##Tuning values
beta.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 && p > 1){
beta.tuning <- diag(beta.tuning)
}
}else{
stop("error: beta tuning is misspecified")
}
}else{##no tuning provided
if(!is.amcmc){
stop("error: tuning value list must be specified")
}
beta.tuning <- rep(0.01,p)
}
storage.mode(beta.tuning) <- "double"
##Other stuff
storage.mode(n.report) <- "integer"
storage.mode(verbose) <- "integer"
##Send if off
out <- .Call("nonSpGLM_AMCMC", Y, X, p, n, family, weights,
beta.prior, beta.Norm, beta.starting, beta.tuning,
n.batch, batch.length, accept.rate, verbose, n.report, is.amcmc)
out$p.beta.samples <- mcmc(t(out$p.beta.samples))
colnames(out$p.beta.samples) <- x.names
out$weights <- weights
out$family <- family
out$Y <- Y
out$X <- X
class(out) <- "nonSpGLM"
return(out)
}
####################################################
##Distance matrices
####################################################
####################
##Coords
####################
if(missing(coords)){stop("error: coords must be specified")}
if(!is.matrix(coords)){stop("error: coords must n-by-2 matrix of xy-coordinate locations")}
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")
}
####################
##Knots
####################
is.pp <- FALSE
if(!missing(knots)){
if(is.vector(knots) && length(knots) %in% c(2,3)){
##allow single knot dim
if(knots[1] > 1){
x.knots <- seq(min(coords[,1]), max(coords[,1]), length.out=knots[1])
}else{
x.knots <- (max(coords[,1])-min(coords[,1]))/2
}
if(knots[2] > 1){
y.knots <- seq(min(coords[,2]), max(coords[,2]), length.out=knots[2])
}else{
y.knots <- (max(coords[,2])-min(coords[,2]))/2
}
##if not single knot then adjust out half distance on all sides
if(length(knots) == 2){
if(knots[1] > 1){
x.int <- (x.knots[2]-x.knots[1])/2
x.knots <- seq(min(x.knots)-x.int, max(x.knots)+x.int, length.out=knots[1])
}
if(knots[2] > 1){
y.int <- (y.knots[2]-y.knots[1])/2
y.knots <- seq(min(y.knots)-y.int, max(y.knots)+y.int, length.out=knots[2])
}
knot.coords <- as.matrix(expand.grid(x.knots, y.knots))
is.pp <- TRUE
}else{
if(knots[1] > 1){
x.int <- knots[3]
x.knots <- seq(min(x.knots)-x.int, max(x.knots)+x.int, length.out=knots[1])
}
if(knots[2] > 1){
y.int <- knots[3]
y.knots <- seq(min(y.knots)-y.int, max(y.knots)+y.int, length.out=knots[2])
}
knot.coords <- as.matrix(expand.grid(x.knots, y.knots))
is.pp <- TRUE
}
}else if(is.matrix(knots) && ncol(knots) == 2){
knot.coords <- knots
is.pp <- TRUE
}else{
stop("error: knots is misspecified")
}
}
m <- 0
coords.D <- 0
knots.D <- 0
knots.coords.D <- 0
if(is.pp){
knots.D <- iDist(knot.coords)
m <- nrow(knots.D)
knots.coords.D <- iDist(knot.coords, coords)
}else{
coords.D <- iDist(coords)
}
storage.mode(m) <- "integer"
storage.mode(coords.D) <- "double"
storage.mode(knots.D) <- "double"
storage.mode(knots.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
sigma.sq.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{
if(family=="poisson"){
beta.starting <- coefficients(glm(Y~X-1, family="poisson"))
}else{
beta.starting <- coefficients(glm((Y/weights)~X-1, weights=weights, family="binomial"))
}
}
if(!"sigma.sq" %in% names(starting)){stop("error: sigma.sq must be specified in starting value list")}
sigma.sq.starting <- starting[["sigma.sq"]][1]
if(!"phi" %in% names(starting)){stop("error: phi must be specified in starting value list")}
phi.starting <- starting[["phi"]][1]
if(cov.model == "matern"){
if(!"nu" %in% names(starting)){stop("error: nu must be specified in starting value list")}
nu.starting <- starting[["nu"]][1]
}
if(!"w" %in% names(starting)){
warning("w is not specified in starting value list. Setting starting value to 0.")
}else{
w.starting <- starting[["w"]]
}
if(is.pp){
if(length(w.starting) == 1){w.starting <- rep(w.starting, m)}
if(length(w.starting) != m){stop(paste("error: w in the starting value list must be a scalar of length 1 or vector of length ",m," (i.e., the number of predictive process knots)",sep=""))}
}else{
if(length(w.starting) == 1){w.starting <- rep(w.starting, n)}
if(length(w.starting) != n){stop(paste("error: w in the starting value list must be a scalar of length 1 or vector of length ",n," (i.e., the number of predictive process knots)",sep=""))}
}
storage.mode(beta.starting) <- "double"
storage.mode(phi.starting) <- "double"
storage.mode(sigma.sq.starting) <- "double"
storage.mode(nu.starting) <- "double"
storage.mode(w.starting) <- "double"
####################################################
##Priors
####################################################
beta.Norm <- 0
beta.prior <- "flat"
sigma.sq.IG <- 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.normal" %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(!"sigma.sq.ig" %in% names(priors)){stop("error: sigma.sq.IG must be specified in priors value list")}
sigma.sq.IG <- priors[["sigma.sq.ig"]]
if(!is.vector(sigma.sq.IG) || length(sigma.sq.IG) != 2){stop("error: sigma.sq.IG must be a vector of length 2 in priors value list")}
if(any(sigma.sq.IG <= 0)){stop("error: sigma.sq.IG must be a positive vector of length 2 in priors value list")}
if(!"phi.unif" %in% names(priors)){stop("error: phi.Unif must be specified in priors value list")}
phi.Unif <- priors[["phi.unif"]]
if(!is.vector(phi.Unif) || length(phi.Unif) != 2){stop("error: phi.Unif must be a vector of length 2 in priors value list")}
if(any(phi.Unif <= 0, phi.Unif[1] >= phi.Unif[2])){stop("error: phi.Unif must be a positive vector of length 2 with element 1 < element 2 in priors value list")}
if(cov.model == "matern"){
if(!"nu.unif" %in% names(priors)){stop("error: nu.Unif must be specified in priors")}
nu.Unif <- priors[["nu.unif"]]
if(!is.vector(nu.Unif) || length(nu.Unif) != 2){stop("error: nu.Unif must be a vector of length 2 in priors")}
if(any(nu.Unif <= 0, nu.Unif[1] >= nu.Unif[2])){stop("error: nu.Unif must be a positive vector of length 2 with element 1 < element 2 in priors value list")}
}
storage.mode(sigma.sq.IG) <- "double"
storage.mode(nu.Unif) <- "double"
storage.mode(phi.Unif) <- "double"
####################################################
##Tuning values
####################################################
beta.tuning <- 0
phi.tuning <- 0
sigma.sq.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 && p > 1){
beta.tuning <- diag(beta.tuning)
}
}else{
stop("error: beta tuning is misspecified")
}
if(!"sigma.sq" %in% names(tuning)){stop("error: sigma.sq must be specified in tuning value list")}
sigma.sq.tuning <- tuning[["sigma.sq"]][1]
if(!"phi" %in% names(tuning)){stop("error: phi must be specified in tuning value list")}
phi.tuning <- tuning[["phi"]][1]
if(cov.model == "matern"){
if(!"nu" %in% names(tuning)){stop("error: nu must be specified in tuning value list")}
nu.tuning <- tuning[["nu"]][1]
}
if(!"w" %in% names(tuning)){stop("error: w must be specified in tuning value list")}
w.tuning <- tuning[["w"]]
if(!"w" %in% names(tuning)){stop("error: w must be specified in tuning value list")}
w.tuning <- tuning[["w"]]
if(is.pp){
if(length(w.tuning) == 1){w.tuning <- rep(w.tuning, m)}
if(length(w.tuning) != m){stop(paste("error: w in the tuning value list must be a scalar of length 1 or vector of length ",m," (i.e., the number of predictive process knots)",sep=""))}
}else{
if(length(w.tuning) == 1){w.tuning <- rep(w.tuning, n)}
if(length(w.tuning) != n){stop(paste("error: w in the tuning value list must be a scalar of length 1 or vector of length ",n," (i.e., the number of predictive process knots)",sep=""))}
}
}else{##no tuning provided
if(!is.amcmc){
stop("error: tuning value list must be specified")
}
beta.tuning <- rep(0.01,p)
phi.tuning <- 0.01
sigma.sq.tuning <- 0.01
nu.tuning <- 0.01
if(is.pp){
w.tuning <- rep(0.01,m)
}else{
w.tuning <- rep(0.01,n)
}
}
storage.mode(beta.tuning) <- "double"
storage.mode(phi.tuning) <- "double"
storage.mode(sigma.sq.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()
if(is.pp){
if(!is.amcmc){
out <- .Call("spPPGLM", Y, X, p, n, family, weights,
m, knots.D, knots.coords.D,
beta.prior, beta.Norm, sigma.sq.IG, nu.Unif, phi.Unif,
phi.starting, sigma.sq.starting, nu.starting, beta.starting, w.starting,
phi.tuning, sigma.sq.tuning, nu.tuning, beta.tuning, w.tuning,
cov.model, n.samples, verbose, n.report)
}else{
out <- .Call("spPPGLM_AMCMC", Y, X, p, n, family, weights,
m, knots.D, knots.coords.D,
beta.prior, beta.Norm, sigma.sq.IG, nu.Unif, phi.Unif,
phi.starting, sigma.sq.starting, nu.starting, beta.starting, w.starting,
phi.tuning, sigma.sq.tuning, nu.tuning, beta.tuning, w.tuning,
cov.model, n.batch, batch.length, accept.rate, verbose, n.report)
}
}else{
if(!is.amcmc){
out <- .Call("spGLM", Y, X, p, n, coords.D, family, weights,
beta.prior, beta.Norm, sigma.sq.IG, nu.Unif, phi.Unif,
phi.starting, sigma.sq.starting, nu.starting, beta.starting, w.starting,
phi.tuning, sigma.sq.tuning, nu.tuning, beta.tuning, w.tuning,
cov.model, n.samples, verbose, n.report)
}else{
out <- .Call("spGLM_AMCMC", Y, X, p, n, coords.D, family, weights,
beta.prior, beta.Norm, sigma.sq.IG, nu.Unif, phi.Unif,
phi.starting, sigma.sq.starting, nu.starting, beta.starting, w.starting,
phi.tuning, sigma.sq.tuning, nu.tuning, beta.tuning, w.tuning,
cov.model, n.batch, batch.length, accept.rate, verbose, n.report)
}
}
run.time <- proc.time() - ptm
out$p.beta.theta.samples <- mcmc(t(out$p.beta.theta.samples))
col.names <- rep("null",ncol(out$p.beta.theta.samples))
col.names[1:p] <- x.names
if(cov.model != "matern"){
col.names[(p+1):(p+2)] <- c("sigma.sq", "phi")
}else{
col.names[(p+1):(p+3)] <- c("sigma.sq", "phi", "nu")
}
colnames(out$p.beta.theta.samples) <- col.names
out$weights <- weights
out$family <- family
out$Y <- Y
out$X <- X
out$coords <- coords
out$is.pp <- is.pp
if(is.pp){
out$knot.coords <- knot.coords
}
out$cov.model <- cov.model
out$run.time <- run.time
class(out) <- "spGLM"
out
}
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