[Pomp-commits] r1114 - in pkg/pomp: . man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri Feb 27 15:13:25 CET 2015
Author: kingaa
Date: 2015-02-27 15:13:25 +0100 (Fri, 27 Feb 2015)
New Revision: 1114
Modified:
pkg/pomp/DESCRIPTION
pkg/pomp/man/basic-probes.Rd
pkg/pomp/man/bsmc.Rd
pkg/pomp/man/bsplines.Rd
pkg/pomp/man/csnippet.Rd
pkg/pomp/man/dacca.Rd
pkg/pomp/man/design.Rd
pkg/pomp/man/eulermultinom.Rd
pkg/pomp/man/example.Rd
pkg/pomp/man/logmeanexp.Rd
pkg/pomp/man/mif.Rd
pkg/pomp/man/pfilter.Rd
pkg/pomp/man/plugins.Rd
pkg/pomp/man/pomp-methods.Rd
pkg/pomp/man/pomp.Rd
pkg/pomp/man/probe.Rd
pkg/pomp/man/sir.Rd
pkg/pomp/man/spect.Rd
pkg/pomp/man/traj-match.Rd
pkg/pomp/man/verhulst.Rd
Log:
- edits to help pages
Modified: pkg/pomp/DESCRIPTION
===================================================================
--- pkg/pomp/DESCRIPTION 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/DESCRIPTION 2015-02-27 14:13:25 UTC (rev 1114)
@@ -1,8 +1,8 @@
Package: pomp
Type: Package
Title: Statistical Inference for Partially Observed Markov Processes
-Version: 0.61-3
-Date: 2015-02-26
+Version: 0.61-4
+Date: 2015-02-27
Authors at R: c(person(given=c("Aaron","A."),family="King",
role=c("aut","cre"),email="kingaa at umich.edu"),
person(given=c("Edward","L."),family="Ionides",role=c("aut")),
Modified: pkg/pomp/man/basic-probes.Rd
===================================================================
--- pkg/pomp/man/basic-probes.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/basic-probes.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -1,6 +1,7 @@
\name{Probe functions}
\title{Some useful probes for partially-observed Markov processes}
\alias{Probe functions}
+\alias{probe functions}
\alias{basic.probes}
\alias{probe.mean}
\alias{probe.median}
Modified: pkg/pomp/man/bsmc.Rd
===================================================================
--- pkg/pomp/man/bsmc.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/bsmc.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -121,7 +121,7 @@
\author{
Michael Lavine (lavine at math dot umass dot edu),
Matthew Ferrari (mferrari at psu dot edu),
- Aaron A. King (kingaa at umich dot edu)
+ Aaron A. King (kingaa at umich dot edu),
Edward L. Ionides (ionides at umich dot edu)
}
\references{
Modified: pkg/pomp/man/bsplines.Rd
===================================================================
--- pkg/pomp/man/bsplines.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/bsplines.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -37,8 +37,8 @@
}
\section{C API}{
Access to the underlying C routines is available:
- See the header file \file{pomp.h} for the details.
- Do \preformatted{file.show(system.file("include/pomp.h",package="pomp"))} to view this file.
+ see the header file \file{pomp.h} for definition and documentation of the C API.
+ At an \R prompt, execute \preformatted{file.show(system.file("include/pomp.h",package="pomp"))} to view this file.
}
\author{Aaron A. King \email{kingaa at umich dot edu}}
\examples{
Modified: pkg/pomp/man/csnippet.Rd
===================================================================
--- pkg/pomp/man/csnippet.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/csnippet.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -21,6 +21,8 @@
The \code{pomp} constructor combines these \code{Csnippet}s into a compilable C file stored in the \R session's temporary directory.
They are then compiled (via \code{\link[=SHLIB]{R CMD SHLIB}}) into dynamically loadable shared objects.
This is then dynamically loaded as needed.
+
+ Instructions for writing models using \code{Csnippet}s, along with examples, are given in the tutorials on the \href{http://pomp.r-forge.r-project.org}{package website}.
}
\author{Aaron A. King \email{kingaa at umich dot edu}}
\seealso{
Modified: pkg/pomp/man/dacca.Rd
===================================================================
--- pkg/pomp/man/dacca.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/dacca.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -28,9 +28,9 @@
plot(simulate(dacca))
}
\references{
- King, A. A., Ionides, E. L., Pascual, M., and Bouma, M. J.
- Inapparent infections and cholera dynamics.
- Nature 454:877-880 (2008)
+ A. A. King, E. L. Ionides, M. Pascual, and M. J. Bouma,
+ Inapparent infections and cholera dynamics,
+ Nature, 454:877-880, 2008
}
\seealso{
\code{\link{euler.sir}},
Modified: pkg/pomp/man/design.Rd
===================================================================
--- pkg/pomp/man/design.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/design.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -69,7 +69,7 @@
plot(x)
}
\references{
- W. H. Press, S. A. Teukolsky, W. T. Vetterling, \& B. P. Flannery,
+ W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery,
Numerical Recipes in C,
Cambridge University Press, 1992
}
Modified: pkg/pomp/man/eulermultinom.Rd
===================================================================
--- pkg/pomp/man/eulermultinom.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/eulermultinom.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -26,41 +26,24 @@
\deqn{(N-\sum_{i=1}^k \Delta n_i, \Delta n_1, \dots, \Delta n_k) \sim \mathrm{multinomial}(N;p_0,p_1,\dots,p_k),}{(N-\sum(dni), dn1, \dots, dnk) ~ multinomial(N;p0,p1,\dots,pk),}
where \eqn{\Delta n_i}{dni} is the number of individuals dying in way \eqn{i} over the interval, the probability of remaining alive is \eqn{p_0=\exp(-\sum_i r_i \Delta t)}{p0=exp(-\sum(ri dt))}, and the probability of dying in way \eqn{j} is
\deqn{p_j=\frac{r_j}{\sum_i r_i} (1-\exp(-\sum_i r_i \Delta t)).}{pj=(1-exp(-sum(ri dt))) rj/(\sum(ri)).}
- In this case, we can say that
+ In this case, we say that
\deqn{(\Delta n_1, \dots, \Delta n_k) \sim \mathrm{eulermultinom}(N,r,\Delta t),}{(dn1,\dots,dnk)~eulermultinom(N,r,dt),}
where \eqn{r=(r_1,\dots,r_k)}{r=(r1,\dots,rk)}.
- Draw \eqn{m} random samples from this distribution by doing
-
- \code{dn <- reulermultinom(n=m,size=N,rate=r,dt=dt)},
-
+ Draw \eqn{m} random samples from this distribution by doing \preformatted{dn <- reulermultinom(n=m,size=N,rate=r,dt=dt),}
where \code{r} is the vector of rates.
- Evaluate the probability that \eqn{x=(x_1,\dots,x_k)}{x=(x1,\dots,xk)} are the numbers of individuals who have died in each of the \eqn{k} ways over the interval \eqn{\Delta t=}{}\code{dt}, by doing
-
- \code{deulermultinom(x=x,size=N,rate=r,dt=dt)}.
+ Evaluate the probability that \eqn{x=(x_1,\dots,x_k)}{x=(x1,\dots,xk)} are the numbers of individuals who have died in each of the \eqn{k} ways over the interval \eqn{\Delta t=}{}\code{dt}, by doing \preformatted{deulermultinom(x=x,size=N,rate=r,dt=dt).}
- Bret\'o & Ionides discuss how an infinitesimally overdispersed death process can be constructed by compounding a binomial process with a Gamma white noise process.
+ Breto & Ionides (2011) discuss how an infinitesimally overdispersed death process can be constructed by compounding a binomial process with a Gamma white noise process.
The Euler approximation of the resulting process can be obtained as follows.
- Let the increments of the equidispersed process be given by
-
- \code{reulermultinom(size=N,rate=r,dt=dt)}.
-
+ Let the increments of the equidispersed process be given by \preformatted{reulermultinom(size=N,rate=r,dt=dt).}
In this expression, replace the rate \eqn{r} with \eqn{r {\Delta W}/{\Delta t}},
- where \eqn{\Delta W ~ Gamma(dt/\sigma^2,\sigma^2)} is the increment of an integrated Gamma white noise process with intensity \eqn{\sigma}.
- That is, \eqn{\Delta W} has mean \eqn{\Delta t} and variance \eqn{\sigma^2 \Delta t}.
+ where \eqn{\Delta\!W \sim \mathrm{Gamma}(\Delta\!t/\sigma^2,\sigma^2)} is the increment of an integrated Gamma white noise process with intensity \eqn{\sigma}.
+ That is, \eqn{\Delta\!W} has mean \eqn{\Delta\!t} and variance \eqn{\sigma^2 \Delta\!t}.
The resulting process is overdispersed and converges (as \eqn{\Delta t} goes to zero) to a well-defined process.
- The following lines of \R code accomplish this:
- \preformatted{
- dW <- rgammawn(sigma=sigma,dt=dt)
- dn <- reulermultinom(size=N,rate=r,dt=dW)
- }
- or
- \preformatted{
- dn <- reulermultinom(size=N,rate=r*dW/dt,dt=dt).
- }
+ The following lines of \R code accomplish this: \preformatted{dW <- rgammawn(sigma=sigma,dt=dt)} \preformatted{dn <- reulermultinom(size=N,rate=r,dt=dW)} or \preformatted{dn <- reulermultinom(size=N,rate=r*dW/dt,dt=dt).}
He et al. use such overdispersed death processes in modeling measles.
- For all of the functions described here, access to the underlying C routines is available:
- see below.
+ For all of the functions described here, access to the underlying C routines is available: see below.
}
\value{
\item{reulermultinom}{
@@ -75,8 +58,8 @@
Returns a vector of length \code{n} containing random increments of the integrated Gamma white noise process with intensity \code{sigma}.
}
}
-\section{C interface}{
- A C API for these functions is also provided by the package.
+\section{C API}{
+ An interface for C codes using these functions is provided by the package.
At an \R prompt, execute \preformatted{file.show(system.file("include/pomp.h",package="pomp"))} to view the header file that defines and explains the API.
}
\author{Aaron A. King \email{kingaa at umich dot edu}}
@@ -89,7 +72,7 @@
print(dn <- reulermultinom(5,size=100,rate=c(a=1,b=2,c=3),dt=dW))
}
\references{
- C. Bret\'o & E. L. Ionides,
+ C. Breto & E. L. Ionides,
Compound Markov counting processes and their applications to modeling infinitesimally over-dispersed systems.
Stoch. Proc. Appl., 121:2571--2591, 2011.
Modified: pkg/pomp/man/example.Rd
===================================================================
--- pkg/pomp/man/example.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/example.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -1,6 +1,6 @@
\name{Example pomp models}
+\title{Examples of the construction of POMP models}
\alias{pompExample}
-\title{Examples of the construction of POMP models.}
\alias{Example pomp models}
\alias{pompExample}
\description{
@@ -23,13 +23,16 @@
}
}
\details{
- Directories in the the global option \code{pomp.examples} (set using \code{options()}) are searched for files named \file{example.R}.
+ Directories listed in the global option \code{pomp.examples} (which can be changed using \code{options()}) are searched for file named \file{<example>.R}.
If found, this file will be \code{source}d in a temporary environment.
- Additional arguments to \code{pompExample} define variables within this environment and will therefore be available when the code in \file{example.R} is \code{source}d.
+ Additional arguments to \code{pompExample} define variables within this environment and will therefore be available when the code in \file{<example>.R} is \code{source}d.
+
+ The codes that construct these \code{pomp} objects can be found in the \file{examples} directory in the installed package.
+ Do \code{system.file("examples",package="pomp"))} to find this directory.
}
\value{
- By default, \code{pompExample} has the side effect of creating one or more \code{pomp} objects in the global workspace.
- If \code{envir=NULL}, there are no side effects; rather, the \code{pomp} objects are returned as a list.
+ By default, \code{pompExample} has the side effect of creating one or more objects in the global workspace.
+ If \code{envir=NULL}, there are no side effects; rather, the objects are returned as a list.
}
\author{Aaron A. King \email{kingaa at umich dot edu}}
\examples{
Modified: pkg/pomp/man/logmeanexp.Rd
===================================================================
--- pkg/pomp/man/logmeanexp.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/logmeanexp.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -2,7 +2,7 @@
\alias{logmeanexp}
\title{The log-mean-exp trick}
\description{
- \code{logmeanexp} computes the log-mean-exp of a set of numbers.
+ \code{logmeanexp} computes \deqn{\log\frac{1}{N}\sum_{n=1}^N\!e^x_i,}{log mean exp(x_i),} avoiding over- and under-flow in doing so.
}
\usage{
logmeanexp(x, se = FALSE)
@@ -22,5 +22,6 @@
ll <- replicate(n=5,logLik(pfilter(ricker,Np=1000)))
## an estimate of the log likelihood:
logmeanexp(ll)
+ ## with standard error:
logmeanexp(ll,se=TRUE)
}
Modified: pkg/pomp/man/mif.Rd
===================================================================
--- pkg/pomp/man/mif.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/mif.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -197,7 +197,7 @@
In particular, when \code{sd=0}, the \code{particles} should return matrices with \code{Np} identical columns, each given by the parameters specified in \code{center}.
}
\references{
- E. L. Ionides, C. Bret\\'o, & A. A. King,
+ E. L. Ionides, C. Breto, & A. A. King,
Inference for nonlinear dynamical systems,
Proc. Natl. Acad. Sci. U.S.A., 103:18438--18443, 2006.
Modified: pkg/pomp/man/pfilter.Rd
===================================================================
--- pkg/pomp/man/pfilter.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/pfilter.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -152,14 +152,21 @@
}
\item{cond.logLik}{
Extracts the estimated conditional log likelihood
- \deqn{\ell_t(\theta) = \mathrm{Prob}[y_t \vert y_1, \dots, y_{t-1}]}
+ \deqn{\ell_t(\theta) = \mathrm{Prob}[y_t \vert y_1, \dots, y_{t-1}],}{ell_t(theta)=Prob[y_t | y_1, \dots, y_(t-1)],}
+ where \eqn{y_t} are the data, at time \eqn{t}.
}
\item{eff.sample.size}{
- Extracts the (time-dependent) estimated effective sample size.
+ Extracts the (time-dependent) estimated effective sample size, computed as
+ \deqn{\left(\sum_i\!w_{it}^2\right)^{-1},}{1/(sum(w_it^2)),}
+ where \eqn{w_{it}}{w_it} is the normalized weight of particle \eqn{i} at time \eqn{t}.
}
- \item{pred.mean, pred.var, filter.mean}{
- Extract the mean and variance of the approximate prediction distribution and the mean of the filtering distribution, respectively.
+ \item{pred.mean, pred.var}{
+ Extract the mean and variance of the approximate prediction distribution.
+ This prediction distribution is that of \deqn{X_t \vert y_1,\dots,y_{t-1},}{X_t | y_1,\dots,y_(t-1),} where \eqn{X_t}, \eqn{y_t} are the state vector and data, respectively, at time \eqn{t}.
}
+ \item{filter.mean}{
+ Extract the mean of the filtering distribution, which is that of \deqn{X_t \vert y_1,\dots,y_t,}{X_t | y_1,\dots,y_t,} where \eqn{X_t}, \eqn{y_t} are the state vector and data, respectively, at time \eqn{t}.
+ }
}
}
\examples{
Modified: pkg/pomp/man/plugins.Rd
===================================================================
--- pkg/pomp/man/plugins.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/plugins.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -58,7 +58,7 @@
\item{dens.fun}{
This can be either an R function, a \code{\link{Csnippet}}, or a compiled, dynamically loaded native function containing the model transition log probability density function.
- If it is an R function, it should be of the form \code{dens.fun(x1,x2,t1,t2,params,...)}.
+ If it is an R function, it should be of the form \preformatted{dens.fun(x1,x2,t1,t2,params,...).}
Here, \code{x1} and \code{x2} are named numeric vectors containing the values of the state process at times \code{t1} and \code{t2},
\code{params} is a named numeric vector containing parameters.
Modified: pkg/pomp/man/pomp-methods.Rd
===================================================================
--- pkg/pomp/man/pomp-methods.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/pomp-methods.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -152,7 +152,6 @@
In this case, if some of the names in \code{pars} do not already name parameters in \code{coef(object,transform=TRUE)}, then they are concatenated.
}
\item{obs}{
- These functions are synonymous.
\code{obs(object)} returns the array of observations.
\code{obs(object,vars)} gives just the observations of variables named in \code{vars}.
\code{vars} may specify the variables by position or by name.
@@ -186,9 +185,7 @@
Additional arguments are passed to the low-level plotting routine.
}
\item{print}{Prints the \code{pomp} object in a nice way.}
- \item{as, coerce}{
- The \code{coerce} method should typically not be used directly.
- It is defined by \code{setAs} as a method to be used by \code{as}.
+ \item{as}{
A \code{pomp} object can be coerced to a data frame via \preformatted{as(object,"data.frame").}
The data frame contains the times, the data, and the state trajectories, if they exist.
}
@@ -198,7 +195,8 @@
\seealso{
\code{\link{pomp}},
\link{pomp low-level interface},
- \link[=simulate-pomp]{simulate}
+ \link[=simulate-pomp]{simulate},
+ \code{\link{pfilter}}, \code{\link{probe}}.
}
\keyword{programming}
\keyword{ts}
Modified: pkg/pomp/man/pomp.Rd
===================================================================
--- pkg/pomp/man/pomp.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/pomp.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -77,21 +77,25 @@
\item{rmeasure}{
optional; the measurement model simulator.
This can be specified in one of four ways:
- (1) as a function of prototype \preformatted{rmeasure(x,t,params,\dots)} that makes a draw from the observation process given states \code{x}, time \code{t}, and parameters \code{params}.
- (2) as the name of a native (compiled) routine with prototype \code{pomp_measure_model_simulator} as defined in the header file \file{pomp.h}.
- (To view the header file, execute \preformatted{file.show(system.file("include/pomp.h",package="pomp"))} in an \R session.)
- (3) using the formula-based \code{measurement.model} facility (see below).
- (4) as a snippet of C code (via \code{\link{Csnippet}}) that draws from the observation process as above.
- The last is typically the preferred option, as it results in much faster code execution.
+ \enumerate{
+ \item as a function of prototype \preformatted{rmeasure(x,t,params,\dots)} that makes a draw from the observation process given states \code{x}, time \code{t}, and parameters \code{params}.
+ \item as the name of a native (compiled) routine with prototype \code{pomp_measure_model_simulator} as defined in the header file \file{pomp.h}.
+ (To view the header file, execute \preformatted{file.show(system.file("include/pomp.h",package="pomp"))} in an \R session.)
+ \item using the formula-based \code{measurement.model} facility (see below).
+ \item as a snippet of C code (via \code{\link{Csnippet}}) that draws from the observation process as above.
+ The last is typically the preferred option, as it results in much faster code execution.
+ }
}
\item{dmeasure}{
optional; the measurement model probability density function.
This can be specified in one of four ways:
- (1) as a function of prototype \preformatted{dmeasure(y,x,t,params,log,\dots)} that computes the p.d.f. of \code{y} given \code{x}, \code{t}, and \code{params}.
- (2) as the name of a native (compiled) routine with prototype \code{pomp_measure_model_density} as defined in the header file \file{pomp.h}.
- (To view the header file, execute \preformatted{file.show(system.file("include/pomp.h",package="pomp"))} in an \R session.)
- (3) using the formula-based \code{measurement.model} facility (see below).
- (4) as a snippet of C code (via \code{\link{Csnippet}}) that computes the p.d.f. as above.
+ \enumerate{
+ \item as a function of prototype \preformatted{dmeasure(y,x,t,params,log,\dots)} that computes the p.d.f. of \code{y} given \code{x}, \code{t}, and \code{params}.
+ \item as the name of a native (compiled) routine with prototype \code{pomp_measure_model_density} as defined in the header file \file{pomp.h}.
+ (To view the header file, execute \preformatted{file.show(system.file("include/pomp.h",package="pomp"))} in an \R session.)
+ \item using the formula-based \code{measurement.model} facility (see below).
+ \item as a snippet of C code (via \code{\link{Csnippet}}) that computes the p.d.f. as above.
+ }
The last is typically the preferred option, as it results in much faster code execution.
As might be expected, if \code{log=TRUE}, this function should return the log likelihood.
}
@@ -112,11 +116,13 @@
indicate this by specifying \code{skeleton.type="vectorfield"}.
The skeleton function can be specified in one of three ways:
- (1) as an \R function of prototype \preformatted{skeleton(x,t,params,\dots)} that evaluates the deterministic skeleton at state \code{x} and time \code{t} given the parameters \code{params},
- (2) as the name of a native (compiled) routine with prototype \code{pomp_skeleton} as defined in the header file \file{pomp.h}.
- (To view the header file, execute \preformatted{file.show(system.file("include/pomp.h",package="pomp"))} in an \R session.)
- (3) as a snippet of C code (via \code{\link{Csnippet}}) that performs this evaluation.
- The latter is typically the preferred option, for reasons of computational efficiency.
+ \enumerate{
+ \item as an \R function of prototype \preformatted{skeleton(x,t,params,\dots)} that evaluates the deterministic skeleton at state \code{x} and time \code{t} given the parameters \code{params},
+ \item as the name of a native (compiled) routine with prototype \code{pomp_skeleton} as defined in the header file \file{pomp.h}.
+ (To view the header file, execute \preformatted{file.show(system.file("include/pomp.h",package="pomp"))} in an \R session.)
+ \item as a snippet of C code (via \code{\link{Csnippet}}) that performs this evaluation.
+ The latter is typically the preferred option, for reasons of computational efficiency.
+ }
}
\item{initializer}{
optional function of prototype \preformatted{initializer(params,t0,\dots)} that yields initial conditions for the state process when given a vector, \code{params}, of parameters.
@@ -128,19 +134,23 @@
\item{rprior}{
optional; function drawing a sample from a prior distribution on parameters.
This can be specified in one of three ways:
- (1) as an \R function of prototype \preformatted{rprior(params,\dots)} that makes a draw from the prior distribution given \code{params},
- (2) as the name of a native (compiled) routine with prototype \code{pomp_rprior} as defined in the header file \file{pomp.h}, or
- (To view the header file, execute \preformatted{file.show(system.file("include/pomp.h",package="pomp"))} in an \R session.)
- (3) as a snippet of C code (via \code{\link{Csnippet}}).
+ \enumerate{
+ \item as an \R function of prototype \preformatted{rprior(params,\dots)} that makes a draw from the prior distribution given \code{params},
+ \item as the name of a native (compiled) routine with prototype \code{pomp_rprior} as defined in the header file \file{pomp.h}, or
+ (To view the header file, execute \preformatted{file.show(system.file("include/pomp.h",package="pomp"))} in an \R session.)
+ \item as a snippet of C code (via \code{\link{Csnippet}}).
+ }
As above, the latter is typically preferable.
}
\item{dprior}{
optional; function evaluating the prior distribution.
This can be specified in one of three ways:
- (1) as an \R function of prototype \preformatted{dprior(params,log=FALSE,\dots)} that evaluates the prior probability density,
- (2) as the name of a native (compiled) routine with prototype \code{pomp_dprior} as defined in the header file \file{pomp.h}, or
- (To view the header file, execute \preformatted{file.show(system.file("include/pomp.h",package="pomp"))} in an \R session.)
- (3) as a snippet of C code (via \code{\link{Csnippet}}).
+ \enumerate{
+ \item as an \R function of prototype \preformatted{dprior(params,log=FALSE,\dots)} that evaluates the prior probability density,
+ \item as the name of a native (compiled) routine with prototype \code{pomp_dprior} as defined in the header file \file{pomp.h}, or
+ (To view the header file, execute \preformatted{file.show(system.file("include/pomp.h",package="pomp"))} in an \R session.)
+ \item as a snippet of C code (via \code{\link{Csnippet}}).
+ }
As above, the latter is typically preferable.
}
\item{params}{
@@ -184,9 +194,8 @@
coef(obj1,transform=TRUE) <- x
identical(coef(obj),coef(obj1))
identical(coef(obj1,transform=TRUE),x).
- }
- By default, both functions are the identity transformation.
- See the demos (\code{demo(package="pomp")}), \code{\link{pompExample}}, and the tutorials on the \href{http://pomp.r-forge.r-project.org}{package website} for examples.
+ } By default, both functions are the identity transformation.
+ See the demos, \preformatted{demo(package="pomp"),} \code{\link{pompExample}}, and the tutorials on the \href{http://pomp.r-forge.r-project.org}{package website} for examples.
}
\item{globals}{
optional character;
Modified: pkg/pomp/man/probe.Rd
===================================================================
--- pkg/pomp/man/probe.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/probe.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -88,7 +88,7 @@
A single probe or a list of one or more probes.
A probe is simply a scalar- or vector-valued function of one argument that can be applied to the data array of a \code{pomp}.
A vector-valued probe must always return a vector of the same size.
- A number of useful examples are provided with the package: see \link{Probe functions}).
+ A number of useful examples are provided with the package: see \link{probe functions}).
}
\item{params}{
optional named numeric vector of model parameters.
@@ -247,7 +247,7 @@
plot(bad)
}
\seealso{
- \link{pomp}, \link{Probe functions}, \link{spect},
+ \link{pomp}, \link{probe functions}, \link{spect},
and the tutorials on the \href{http://pomp.r-forge.r-project.org}{package website}.
}
\keyword{optimize}
Modified: pkg/pomp/man/sir.Rd
===================================================================
--- pkg/pomp/man/sir.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/sir.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -1,9 +1,9 @@
\name{sir}
+\title{Compartmental epidemiological models}
\alias{euler.sir}
\alias{gillespie.sir}
\alias{bbs}
\docType{data}
-\title{SIR models.}
\description{
\code{euler.sir} is a \code{pomp} object encoding a simple seasonal SIR model.
Simulation is performed using an Euler multinomial approximation.
@@ -11,13 +11,8 @@
\code{bbs} is a nonseasonal SIR model together with data from a 1978 outbreak of influenza in a British boarding school.
}
\details{
- This example is discussed tutorials available on the \href{http://pomp.r-forge.r-project.org}{package website}.
+ This and similar examples are discussed and constructed in tutorials available on the \href{http://pomp.r-forge.r-project.org}{package website}.
- The codes that construct these \code{pomp} objects can be found in the \dQuote{examples} directory in the installed package.
- Do \code{system.file("examples",package="pomp"))} to find this directory.
- For the basic \code{rprocess}, \code{dmeasure}, \code{rmeasure}, and \code{skeleton} functions, these codes use compiled native routines built into the package's library.
- View \dQuote{src/sir.c} in the package source or \code{file.show("examples/sir.c")} from an \R session to view these codes.
-
The boarding school influenza outbreak is described in Anonymous (1978).
}
\examples{
Modified: pkg/pomp/man/spect.Rd
===================================================================
--- pkg/pomp/man/spect.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/spect.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -1,6 +1,7 @@
\name{Power spectrum computation and matching}
\title{Power spectrum computation and spectrum-matching for partially-observed Markov processes}
\alias{Power spectrum computation and matching}
+\alias{power spectrum computation and matching}
\alias{spect}
\alias{spect,pomp-method}
\alias{spect-pomp}
@@ -56,7 +57,7 @@
}
\item{seed}{
optional; if non-\code{NULL}, the random number generator will be initialized with this seed for simulations.
- See \link{simulate-pomp}.
+ See \code{\link[=simulate-pomp]{simulate}}.
}
\item{transform}{
function; this transformation will be applied to the observables prior to estimation of the spectrum, and prior to any detrending.
Modified: pkg/pomp/man/traj-match.Rd
===================================================================
--- pkg/pomp/man/traj-match.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/traj-match.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -1,5 +1,5 @@
\name{Trajectory matching}
-\title{Trajectory matching}
+\title{Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data}
\alias{Trajectory matching}
\alias{traj.match}
\alias{traj.match-pomp}
@@ -17,9 +17,10 @@
\alias{traj.match.objfun-pomp}
\alias{traj.match.objfun,pomp-method}
\description{
- Facilities for matching trajectories of a model's deterministic skeleton to data.
+ This function attempts to match trajectories of a model's deterministic skeleton to data.
Trajectory matching is equivalent to maximum likelihood estimation under the assumption that process noise is entirely absent,
i.e., that all stochasticity is measurement error.
+ Accordingly, this method uses only the \code{skeleton} and \code{dmeasure} components of a \acronym{POMP} model.
}
\usage{
\S4method{traj.match}{pomp}(object, start, est = character(0),
Modified: pkg/pomp/man/verhulst.Rd
===================================================================
--- pkg/pomp/man/verhulst.Rd 2015-02-27 13:59:44 UTC (rev 1113)
+++ pkg/pomp/man/verhulst.Rd 2015-02-27 14:13:25 UTC (rev 1114)
@@ -6,7 +6,7 @@
\code{verhulst} is a \code{pomp} object encoding a univariate stochastic logistic model with measurement error.
}
\details{
- The model is written as an Ito diffusion, \eqn{dn = r n (1-n/K) dt + \sigma n dW}, where \eqn{W} is a Wiener process.
+ The model is written as an Ito diffusion, \deqn{dn = r n \left(1-\frac{n}{K}\right) dt + \sigma n dW}{dn = r n (1-n/K) dt + sigma n dW}, where \eqn{W} is a Wiener process.
It is implemented using the \code{\link{euler.sim}} plug-in.
}
\examples{
@@ -23,7 +23,7 @@
matlines(time(verhulst),t(y['n',,]),type='l',lwd=2)
}
\seealso{
- \code{\link{pomp}} and the tutorial documents on the \href{http://pomp.r-forge.r-project.org}{package website}.
+ \code{\link{pomp}}
}
\keyword{models}
\keyword{datasets}
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