[Pomp-commits] r511 - pkg/man

noreply at r-forge.r-project.org noreply at r-forge.r-project.org
Mon Jun 6 16:19:09 CEST 2011


Author: kingaa
Date: 2011-06-06 16:19:09 +0200 (Mon, 06 Jun 2011)
New Revision: 511

Modified:
   pkg/man/bsplines.Rd
   pkg/man/dacca.Rd
   pkg/man/eulermultinom.Rd
   pkg/man/gompertz.Rd
   pkg/man/nlf.Rd
   pkg/man/plugins.Rd
   pkg/man/pomp-methods.Rd
   pkg/man/pomp.Rd
   pkg/man/ricker.Rd
Log:
- documentation improvements


Modified: pkg/man/bsplines.Rd
===================================================================
--- pkg/man/bsplines.Rd	2011-06-05 15:40:21 UTC (rev 510)
+++ pkg/man/bsplines.Rd	2011-06-06 14:19:09 UTC (rev 511)
@@ -19,7 +19,7 @@
   \item{names}{
     optional; the names to be given to the basis functions.
     These will be the column-names of the matrix returned.
-    If the names are specified as a format string (e.g., "basis%d"), \code{\link{sprintf}} will be used to generate the names from the column number.
+    If the names are specified as a format string (e.g., "basis\%d"), \code{\link{sprintf}} will be used to generate the names from the column number.
     If a single non-format string is specified, the names will be generated by \code{\link{paste}}-ing \code{name} to the column number.
     One can also specify each column name explicitly by giving a length-\code{nbasis} string vector.
     By default, no column-names are given.

Modified: pkg/man/dacca.Rd
===================================================================
--- pkg/man/dacca.Rd	2011-06-05 15:40:21 UTC (rev 510)
+++ pkg/man/dacca.Rd	2011-06-06 14:19:09 UTC (rev 511)
@@ -12,16 +12,24 @@
 }
 \usage{
 data(dacca)
+dacca.transform(params,dir=c("forward","inverse"))
 }
 \details{
   \code{dacca} is a \code{pomp} object containing the model, data, and MLE parameters.
   \code{dacca.transform} is a function that transforms the parameters.
+  If \code{params} is a matrix or vector of parameters on the natural scale for this model, \code{dacca.transform(params,dir="forward")} is the corresponding matrix or vector of parameters on the scale that they are used internally.
+  If \code{params} is a matrix or vector of parameters on the internal scale for this model, \code{dacca.transform(params,dir="inverse")} is the corresponding matrix or vector of parameters on the natural scale.
+  Parameters that naturally range over the positive reals are log-transformed by \code{dacca.transform};
+  parameters that range over the unit interval are logit-transformed;
+  parameters that are naturally unbounded or take integer values are not transformed.
 }
 \examples{
 data(dacca)
 plot(dacca)
-theta <- dacca.transform(coef(dacca),dir="inverse") #MLEs
+#MLEs on the natural scale
+theta <- dacca.transform(coef(dacca),dir="inverse") 
 plot(simulate(dacca))
+# now change 'eps' and simulate again
 theta["eps"] <- 1
 plot(simulate(dacca,params=dacca.transform(theta,dir="forward")))
 }

Modified: pkg/man/eulermultinom.Rd
===================================================================
--- pkg/man/eulermultinom.Rd	2011-06-05 15:40:21 UTC (rev 510)
+++ pkg/man/eulermultinom.Rd	2011-06-06 14:19:09 UTC (rev 511)
@@ -1,8 +1,8 @@
-\name{Euler-multinomial models}
+\name{eulermultinom}
 \alias{eulermultinom}
 \alias{reulermultinom}
 \alias{deulermultinom}
-\title{Euler-multinomial models}
+\title{Euler-multinomial death process}
 \description{
   Density and random-deviate generation for the Euler-multinomial death process with parameters \code{size}, \code{rate}, and \code{dt}.
 }
@@ -15,10 +15,28 @@
   \item{size}{scalar integer; number of individuals at risk.}
   \item{rate}{numeric vector of hazard rates.}
   \item{dt}{numeric scalar; duration of Euler step.}
-  \item{x}{Matrix or vector containing number of individuals that have succumbed to each death process.}
+  \item{x}{matrix or vector containing number of individuals that have succumbed to each death process.}
   \item{log}{logical; if TRUE, return logarithm(s) of probabilities.}
 }
 \details{
+  If \eqn{N} individuals face constant hazards of death in \eqn{k} ways at rates \eqn{r_1, r_2, \dots, r_k}, then in an interval of duration \eqn{\Delta t}, the number of individuals remaining alive and dying in each way is multinomially distributed:
+  \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),}
+  where \eqn{\Delta n_i} 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)}, 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)).}
+  In this case, we can say that
+  \deqn{(\Delta n_1, \dots, \Delta n_k) \sim \mathrm{eulermultinom}(N,r,\Delta t),}
+  where \eqn{r=(r_1,\dots,r_k)}.
+  Draw \eqn{m} random samples from this distribution by doing
+  
+  \code{reulermultinom(n=m,size=N,rate=r,dt=dt)},
+
+  where \code{r} is the vector of rates.
+  Evaluate the probability that \eqn{x_1,\dots,x_k} 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)},
+
+  where \code{x} is a length \eqn{k} vector and \code{x[i]=}\eqn{x_i}.
+  
   Direct access to the underlying C routines is available: see the header file \dQuote{pomp.h}, included with the package.
 }
 \value{

Modified: pkg/man/gompertz.Rd
===================================================================
--- pkg/man/gompertz.Rd	2011-06-05 15:40:21 UTC (rev 510)
+++ pkg/man/gompertz.Rd	2011-06-06 14:19:09 UTC (rev 511)
@@ -7,8 +7,8 @@
 }
 \usage{data(gompertz)}
 \details{
-  The state process is \eqn{X_{t+1} = K^(1-R) X_{t}^R eps_{t}}, where the \eqn{eps_t} are i.i.d. lognormal random deviates with variance \eqn{sigma^2}.
-  The observed variables \eqn{Y_t} are distributed as \eqn{normal(X_t,tau)}.
+  The state process is \eqn{X_{t+1} = K^{(1-R)} X_{t}^R \varepsilon_{t}}, where the \eqn{\varepsilon_t} are i.i.d. lognormal random deviates with variance \eqn{\sigma^2}.
+  The observed variables \eqn{Y_t} are distributed as \eqn{\mathrm{normal}(X_t,\tau)}.
 }
 \examples{
 data(gompertz)

Modified: pkg/man/nlf.Rd
===================================================================
--- pkg/man/nlf.Rd	2011-06-05 15:40:21 UTC (rev 510)
+++ pkg/man/nlf.Rd	2011-06-06 14:19:09 UTC (rev 511)
@@ -7,8 +7,10 @@
 }
 \usage{
  nlf(object, start, est, lags, period = NA, tensor = FALSE,
-     nconverge=1000, nasymp=1000, seed = 1066, transform = function (x) x,
-     nrbf = 4, method = "subplex", skip.se = FALSE, verbose = FALSE, gr = NULL,
+     nconverge=1000, nasymp=1000, seed = 1066,
+     transform = function (x) x,
+     nrbf = 4, method = "subplex", skip.se = FALSE,
+     verbose = FALSE, gr = NULL,
      bootstrap=FALSE, bootsamp = NULL,
      lql.frac = 0.1, se.par.frac = 0.1, eval.only = FALSE, \dots)
 }

Modified: pkg/man/plugins.Rd
===================================================================
--- pkg/man/plugins.Rd	2011-06-05 15:40:21 UTC (rev 510)
+++ pkg/man/plugins.Rd	2011-06-06 14:19:09 UTC (rev 511)
@@ -116,7 +116,8 @@
 \author{Aaron A. King \email{kingaa at umich dot edu}}
 \seealso{\code{\link{eulermultinom}}, \code{\link{pomp}}}
 \examples{
-## example showing how to use these functions are provided in the vignette "intro_to_pomp"
+## examples showing how to use these functions
+## are provided in the vignette "intro_to_pomp"
 \dontrun{
 vignette("intro_to_pomp")
 }

Modified: pkg/man/pomp-methods.Rd
===================================================================
--- pkg/man/pomp-methods.Rd	2011-06-05 15:40:21 UTC (rev 510)
+++ pkg/man/pomp-methods.Rd	2011-06-06 14:19:09 UTC (rev 511)
@@ -218,7 +218,7 @@
   \code{\link{rmeasure}},
   \code{\link{dmeasure}},
   \code{\link{init.state}},
-  \link{simulate-pomp}
+  \code{\link[=simulate-pomp]{simulate}}
 }
 \keyword{models}
 \keyword{ts}

Modified: pkg/man/pomp.Rd
===================================================================
--- pkg/man/pomp.Rd	2011-06-05 15:40:21 UTC (rev 510)
+++ pkg/man/pomp.Rd	2011-06-06 14:19:09 UTC (rev 511)
@@ -14,19 +14,22 @@
 }
 \usage{
 \S4method{pomp}{data.frame}(data, times, t0, \dots, rprocess, dprocess, rmeasure, dmeasure,
-       measurement.model, skeleton = NULL, skeleton.type = c("map","vectorfield"),
+       measurement.model,
+       skeleton = NULL, skeleton.type = c("map","vectorfield"),
        skeleton.map = NULL, skeleton.vectorfield = NULL,
        initializer, covar, tcovar,
        obsnames, statenames, paramnames, covarnames,
        PACKAGE)
 \S4method{pomp}{numeric}(data, times, t0, \dots, rprocess, dprocess, rmeasure, dmeasure,
-       measurement.model, skeleton = NULL, skeleton.type = c("map","vectorfield"),
+       measurement.model,
+       skeleton = NULL, skeleton.type = c("map","vectorfield"),
        skeleton.map = NULL, skeleton.vectorfield = NULL,
        initializer, covar, tcovar,
        obsnames, statenames, paramnames, covarnames,
        PACKAGE)
 \S4method{pomp}{matrix}(data, times, t0, \dots, rprocess, dprocess, rmeasure, dmeasure,
-       measurement.model, skeleton = NULL, skeleton.type = c("map","vectorfield"),
+       measurement.model,
+       skeleton = NULL, skeleton.type = c("map","vectorfield"),
        skeleton.map = NULL, skeleton.vectorfield = NULL,
        initializer, covar, tcovar,
        obsnames, statenames, paramnames, covarnames,
@@ -272,7 +275,8 @@
   }
 }
 \examples{
-## For examples, see the vignettes, the data()-loadable example \code{pomp} objects, and the provided example files.
+## For examples, see the vignettes, the data()-loadable
+## example \code{pomp} objects, and the provided example files.
 \dontrun{
 vignette("intro_to_pomp")
 vignette("advanced_topics_in_pomp")

Modified: pkg/man/ricker.Rd
===================================================================
--- pkg/man/ricker.Rd	2011-06-05 15:40:21 UTC (rev 510)
+++ pkg/man/ricker.Rd	2011-06-06 14:19:09 UTC (rev 511)
@@ -7,8 +7,8 @@
 }
 \usage{data(ricker)}
 \details{
-  The state process is \eqn{N_{t+1} = r N_{t} exp(-N_{t}+e_{t})}, where the \eqn{e_t} are i.i.d. normal random deviates with variance \eqn{sigma^2}.
-  The observed variables \eqn{y_t} are distributed as \eqn{Poisson(\phi N_t)}.
+  The state process is \eqn{N_{t+1} = r N_{t} exp(-N_{t}+e_{t})}, where the \eqn{e_t} are i.i.d. normal random deviates with variance \eqn{\sigma^2}.
+  The observed variables \eqn{y_t} are distributed as \eqn{\mathrm{Poisson}(\phi N_t)}.
 }
 \examples{
 data(ricker)



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