version 0.3-0

ChangeLog

@@ -1,3 +1,9 @@
+2016-09-30  Jun Yan <jun.yan@uconn.edu>
+
+	* added full likelihood based hidden Markov model
+	* full likelihood and composite likelihood are now done with Rcpp
+
+
 2013-11-04  Jun Yan  <jun.yan@uconn.edu>
 
 	* added Depends: R (>= 3.0.0).

DESCRIPTION

@@ -1,16 +1,19 @@
 Package: smam
 Type: Package
 Title: Statistical Modeling of Animal Movements
-Version: 0.2-2
-Date: 2013-11-4
-Author: Jun Yan <jun.yan@uconn.edu> and 
- Vladimir Pozdnyakov <vladimir.pozdnyakov@uconn.edu>
+Version: 0.3-0
+Date: 2016-09-02
+Author: Jun Yan <jun.yan@uconn.edu> and
+    Vladimir Pozdnyakov <vladimir.pozdnyakov@uconn.edu>
 Maintainer: Jun Yan <jun.yan@uconn.edu>
-Depends: R (>= 3.0.0), Matrix
+Depends: R (>= 3.2.0)
+Imports: stats, methods, Matrix, numDeriv, Rcpp (>= 0.12.2)
+LinkingTo: Rcpp
 Description: Animal movement models including moving-resting process
- with embedded Brownian motion, Brownian motion with measurement error.
+    with embedded Brownian motion, Brownian motion with measurement error.
 License: GPL (>= 3.0)
-Packaged: 2013-11-04 10:58:18 UTC; jyan
+RoxygenNote: 5.0.1
 NeedsCompilation: yes
+Packaged: 2016-09-30 23:24:03 UTC; junyan
 Repository: CRAN
-Date/Publication: 2013-11-04 14:11:39
+Date/Publication: 2016-10-01 14:33:22

MD5

@@ -1,11 +1,15 @@
-991ea0e90141bf8d7ebad24fe21d838f *ChangeLog
-4b69281a07fce53e8e711024a7a9e406 *DESCRIPTION
-e71ec683464d8db6d3a6f8acc790c7f1 *NAMESPACE
-332261d90bf856cc4528300c7ba4605e *R/bmme.R
-e0a92a8a2e9b6aebaa0002a1fedf6b2c *R/movres.R
-9ad7a258de2dd391fcfde01984b91379 *man/dtm.Rd
-318af21c2f7491de1943b4a889d9eb9e *man/fitBmme.Rd
-2d90fb6d5ad7a963ce33801f67a5e166 *man/fitMovRes.Rd
-cf8a771958de0ff7de8fecce8129f6bd *man/rMovRes.Rd
-8e6a097f8cc5a6877e8314a5ab1a9532 *man/rbmme.Rd
+2646fcacec898b2a7a9bb413a409941e *ChangeLog
+a40a384a2cb164533fc094ea96173991 *DESCRIPTION
+040cb528d6afd61d17fe93146e093c94 *NAMESPACE
+873fe7bd0943f1171119a6c66f19bd99 *R/RcppExports.R
+1266d5adc42c04a88dd503a6bb66d863 *R/bmme.R
+20bdc0be21ed0c9cfdb28e5c0fa53390 *R/movres.R
+3abc5257ac9f082752ec82d220c5b0a9 *man/dtm.Rd
+d1b689ceb76908d3f23d69044ca95b41 *man/fitBmme.Rd
+c4e7b9a5e92d4858e31367c0e14897e9 *man/fitMovRes.Rd
+043d6f9bc4b2ac8fe8a0e88b7b145445 *man/integr.control.Rd
+ea9d2018e85318cf41746a325c74e3f3 *man/rMovRes.Rd
+247b7abd6e1172bb545a52f6ba3268ec *man/rbmme.Rd
+f886589f1bb47e97e5e42118c9bb052b *src/RcppExports.cpp
 49ec4ce0b9a60c3a44525f67bad626a0 *src/altb.c
+d31a7edd7834fe1d9820bd8ff801a0d5 *src/movres-llk.cpp

NAMESPACE

@@ -1,14 +1,18 @@
-useDynLib(smam)
-
-
-## exportPattern(".")
+# Generated by roxygen2: do not edit by hand
 
-## BM with ME
-export(fitBmme,
-       rbmme)
-
-## Moving-Resting with embedded BM
-export(fitMovRes, rMovRes, cllk.m1,
-       dtm, 
-       dtr)
-
+export(dtm)
+export(dtr)
+export(fitBmme)
+export(fitMovRes)
+export(integr.control)
+export(rMovRes)
+export(rbmme)
+importFrom(Rcpp,evalCpp)
+importFrom(methods,is)
+importFrom(numDeriv,hessian)
+importFrom(stats,dnorm)
+importFrom(stats,integrate)
+importFrom(stats,optim)
+importFrom(stats,rexp)
+importFrom(stats,rnorm)
+useDynLib(smam)

R/RcppExports.R

@@ -0,0 +1,43 @@
+# This file was generated by Rcpp::compileAttributes
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+vp11 <- function(vw, t, lambda1, lambda0) {
+    .Call('smam_vp11', PACKAGE = 'smam', vw, t, lambda1, lambda0)
+}
+
+vp10 <- function(vw, t, lambda1, lambda0) {
+    .Call('smam_vp10', PACKAGE = 'smam', vw, t, lambda1, lambda0)
+}
+
+vp00 <- function(vw, t, lambda1, lambda0) {
+    .Call('smam_vp00', PACKAGE = 'smam', vw, t, lambda1, lambda0)
+}
+
+vp01 <- function(vw, t, lambda1, lambda0) {
+    .Call('smam_vp01', PACKAGE = 'smam', vw, t, lambda1, lambda0)
+}
+
+h11 <- function(x, t, theta, integrControl) {
+    .Call('smam_h11', PACKAGE = 'smam', x, t, theta, integrControl)
+}
+
+h10 <- function(x, t, theta, integrControl) {
+    .Call('smam_h10', PACKAGE = 'smam', x, t, theta, integrControl)
+}
+
+h00 <- function(x, t, theta, integrControl) {
+    .Call('smam_h00', PACKAGE = 'smam', x, t, theta, integrControl)
+}
+
+h01 <- function(x, t, theta, integrControl) {
+    .Call('smam_h01', PACKAGE = 'smam', x, t, theta, integrControl)
+}
+
+ncllk_m1_inc <- function(theta, data, integrControl, logtr) {
+    .Call('smam_ncllk_m1_inc', PACKAGE = 'smam', theta, data, integrControl, logtr)
+}
+
+nllk_inc <- function(theta, data, integrControl, logtr) {
+    .Call('smam_nllk_inc', PACKAGE = 'smam', theta, data, integrControl, logtr)
+}
+

R/bmme.R

@@ -9,6 +9,37 @@
 ####      delta: vector of dim, measurement error sd, recyclable
 #### Output:
 ####      a matrix of dim + 1 columns: time, location coordinates
+
+
+#' Sampling from Brown Motion with Measurement Error
+#'
+#' Given the volatility parameters of a Brownian motion and normally
+#' distributed measurement errors, generate the process at discretely
+#' observed time points of a given dimension.
+#'
+#' @param time vector of time points at which observations are to be sampled
+#' @param dim (integer) dimension of the Brownian motion
+#' @param sigma volatility parameter (sd) of the Brownian motion
+#' @param delta sd parameter of measurement error
+#'
+#' @return
+#'   A \code{data.frame} whose first column is the time points and whose
+#'   other columns are coordinates of the locations.
+#'
+#' @references
+#' Pozdnyakov V., Meyer, TH., Wang, Y., and Yan, J. (2013)
+#' On modeling animal movements using Brownian motion with measurement
+#' error. Ecology 95(2): p247--253. doi:doi:10.1890/13-0532.1.
+#'
+#' @examples
+#' tgrid <- seq(0, 10, length = 1001)
+#' ## make it irregularly spaced
+#' tgrid <- sort(sample(tgrid, 800))
+#' dat <- rbmme(tgrid, 1, 1)
+#' plot(dat[,1], dat[,2], xlab="t", ylab="X(t)", type="l")
+#'
+#' @export
+
 rbmme <- function(time, dim = 2,  sigma = 1, delta = 1) {  
     n <- length(time)
     dat <- matrix(NA_real_, n, dim)
@@ -29,11 +60,12 @@ rbmme <- function(time, dim = 2,  sigma = 1, delta = 1) {
 ####      param: vector of (sigma, delta)
 #### Output:
 ####      a banded sparse covariance matrix of the increments
+
 getSparseSigma <- function(tinc, param) {
     n <- length(tinc)
     d0 <- c(tinc * param[1]^2 + 2 * param[2]^2)   
     d1 <- rep(- param[2]^2, n - 1)
-    bandSparse(n, k = c(0, 1), diagonals = list(d0, d1), symmetric = TRUE)
+    Matrix::bandSparse(n, k = c(0, 1), diagonals = list(d0, d1), symmetric = TRUE)
 }
 
 #### multivariate density using sparse covariance matrix
@@ -92,12 +124,53 @@ bmme.start <- function(dat) {
 #### Output
 ####      vector containing parameter estimate, standard error, and convergence code
 
-fitBmme <- function(dat, start = NULL, method = "Nelder-Mead",
+
+#' Fit a Brownian Motion with Measurement Error
+#'
+#' Given discretely observed animal movement locations, fit a Brownian
+#' motion model with measurement errors.
+#'
+#' @param data a data.frame whose first column is the observation time, and other
+#'     columns are location coordinates.
+#' @param start starting value of the model, a vector of two component, one for
+#'     sigma (sd of BM) and the other for delta (sd for measurement error).
+#'     If unspecified (NULL), a moment estimator will be used assuming equal
+#'     sigma and delta.
+#' @param method the method argument to feed \code{optim}.
+#' @param optim.control a list of control that is passed down to \code{optim}.
+#'
+#' @details
+#'   The joint density of the increment data is multivariate normal with a
+#'   sparse (tri-diagonal) covariance matrix. Sparse matrix operation from
+#'   package Matrix is used for computing efficiency in handling large data.
+#'
+#' @return
+#'   A list of the following components:
+#'   \item{estimate }{the esimated parameter vector}
+#'   \item{var.est }{variance matrix of the estimator}
+#'   \item{loglik }{loglikelihood evaluated at the estimate}
+#'   \item{convergence}{convergence code from optim}
+#'
+#' @references
+#' Pozdnyakov V., Meyer, TH., Wang, Y., and Yan, J. (2013)
+#' On modeling animal movements using Brownian motion with measurement
+#' error. Ecology 95(2): p247--253. doi:doi:10.1890/13-0532.1.
+#' @seealso
+#'   \code{\link{fitMovRes}}
+#' @examples
+#' set.seed(123)
+#' tgrid <- seq(0, 500, by = 1)
+#' dat <- rbmme(tgrid, sigma = 1, delta = 0.5)
+#' fit <- fitBmme(dat)
+#' fit
+#' @export
+
+fitBmme <- function(data, start = NULL, method = "Nelder-Mead",
                     optim.control = list()) {
-    if (is.null(start)) start <- bmme.start(dat)
-    dinc <- apply(dat, 2, diff)
+    if (is.null(start)) start <- bmme.start(data)
+    dinc <- apply(data, 2, diff)
     fit <- optim(start, nllk.bmme, dinc = dinc, hessian = TRUE, method=method, control = optim.control)
-    ## Sigma <- getSparseSigma(dat[,1], fit$par)
+    ## Sigma <- getSparseSigma(data[,1], fit$par)
     ans <- list(estimate = fit$par,
                 var.est = solve(fit$hessian),
                 loglik = - fit$value,