multiple lags can now be specified in 'ivp'

Author: kingaa

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: pomp
 Type: Package
 Title: Statistical Inference for Partially Observed Markov Processes
-Version: 6.0.4.1
-Date: 2024-12-30
+Version: 6.0.5.0
+Date: 2025-01-05
 Authors@R: c(person(given=c("Aaron","A."),family="King",role=c("aut","cre"),email="kingaa@umich.edu",comment=c(ORCID="0000-0001-6159-3207")),
 	  person(given=c("Edward","L."),family="Ionides",role="aut",comment=c(ORCID="0000-0002-4190-0174"))	,
 	  person(given="Carles",family="Bretó",role="aut",comment=c(ORCID="0000-0003-4695-4902")),

R/mif2.R

@@ -22,7 +22,7 @@
 ##' @param rw.sd specification of the magnitude of the random-walk perturbations that will be applied to some or all model parameters.
 ##' Parameters that are to be estimated should have positive perturbations specified here.
 ##' The specification is given using the \code{\link{rw_sd}} function, which creates a list of unevaluated expressions.
-##' The latter are evaluated in a context where the model time variable is defined (as \code{time}).
+##' The latter are evaluated in a context where vector of observation times is visible (as \sQuote{\code{time}}).
 ##' The expression \code{ivp(s)} can be used in this context as shorthand for \preformatted{ifelse(time==time[1],s,0).}
 ##' Likewise, \code{ivp(s,lag)} is equivalent to \preformatted{ifelse(time==time[lag],s,0).}
 ##' See below for some examples.
@@ -55,17 +55,26 @@
 ##'
 ##' @section Specifying the perturbations:
 ##' The \code{rw_sd} function simply returns a list containing its arguments as unevaluated expressions.
-##' These are then evaluated in a context containing the model \code{time} variable.
+##' These are then evaluated in a context in which the vector of observation times is defined (as \sQuote{\code{time}}).
 ##' This allows for easy specification of the structure of the perturbations that are to be applied.
 ##' For example,
 ##' \preformatted{
-##'     rw_sd(a=0.05, b=ifelse(time==time[1],0.2,0),
-##'           c=ivp(0.2), d=ifelse(time==time[13],0.2,0),
-##'           e=ivp(0.2,lag=13), f=ifelse(time<23,0.02,0))
+##'     rw_sd(
+##'       a=0.05,
+##'       b=ifelse(time==time[1], 0.2, 0),
+##'       c=ivp(0.2),
+##'       d=ifelse(time==time[13], 0.2, 0),
+##'       e=ivp(0.2, lag=13),
+##'       f=ifelse(time<23, 0.02, 0),
+##'       g=ifelse(time>=23 & time<50, 0.02, 0),
+##'       h=ivp(0.1,lags=3:8)
+##'     )
 ##' }
-##' results in perturbations of parameter \code{a} with s.d. 0.05 at every time step, while parameters \code{b} and \code{c} both get perturbations of s.d. 0.2 only just before the first observation.
-##' Parameters \code{d} and \code{e}, by contrast, get perturbations of s.d.  0.2 only just before the thirteenth observation.
-##' Finally, parameter \code{f} gets a random perturbation of size 0.02 before every observation falling before \eqn{t=23}.
+##' results in random perturbations of parameter \code{a} with s.d. 0.05 at every time step, while parameters \code{b} and \code{c} both get perturbations of s.d. 0.2 only before the first observation (i.e., at the zero-time).
+##' Parameters \code{d} and \code{e}, by contrast, get perturbations of s.d. 0.2 only before the thirteenth observation.
+##' Parameter \code{f} gets a random perturbation of size 0.02 before every observation falling before \eqn{t=23},
+##' while \code{g} gets perturbed before all observations that fall in the interval \eqn{23\le{t}<{50}}{23<=t<50}.
+##' Finally, the magnitude of the perturbation of parameter \code{h} is applied before the third through eighth observations.
 ##'
 ##' On the \eqn{m}-th IF2 iteration, prior to time-point \eqn{n}, the \eqn{d}-th parameter is given a random increment normally distributed with mean \eqn{0} and standard deviation \eqn{c_{m,n} \sigma_{d,n}}{c[m,n] sigma[d,n]}, where \eqn{c} is the cooling schedule and \eqn{\sigma}{sigma} is specified using \code{rw_sd}, as described above.
 ##' Let \eqn{N} be the length of the time series and \eqn{\alpha=}{alpha=}\code{cooling.fraction.50}.
@@ -546,7 +555,7 @@ perturbn_kernel_sd <- function (rw.sd, time, paramnames) {
       sQuote("params"),": ",paste(lapply(unrec,sQuote),collapse=","),".")
   }
   ivp <- function (sd, lag = 1L) {
-    sd*(seq_along(time)==lag)
+    sd*(seq_along(time) %in% lag)
   }
   sds <- lapply(rw.sd,eval,envir=list(time=time,ivp=ivp),enclos=enclos)
   for (n in names(sds)) {

inst/NEWS

@@ -1,5 +1,11 @@
 _N_e_w_s _f_o_r _p_a_c_k_a_g_e '_p_o_m_p'
 
+_C_h_a_n_g_e_s _i_n '_p_o_m_p' _v_e_r_s_i_o_n _6._0._5:
+
+        • It is now possible to specify more than one lag in the ‘ivp’
+          function, which is evaluated only when the ‘mif2’
+          perturbations are specified.  See ‘?mif2’.
+
 _C_h_a_n_g_e_s _i_n '_p_o_m_p' _v_e_r_s_i_o_n _6._0._4:
 
         • The new function ‘append_data’ appends a data frame to an

inst/NEWS.Rd

@@ -1,5 +1,11 @@
 \name{NEWS}
 \title{News for package `pomp'}
+\section{Changes in \pkg{pomp} version 6.0.5}{
+  \itemize{
+    \item It is now possible to specify more than one lag in the \code{ivp} function, which is evaluated only when the \code{mif2} perturbations are specified.
+    See \code{?mif2}.
+  }
+}
 \section{Changes in \pkg{pomp} version 6.0.4}{
   \itemize{
     \item The new function \code{append_data} appends a data frame to an existing CSV file (creating the file if it does not exist).