[Pomp-commits] r779 - in www: content vignettes
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed Aug 22 19:32:11 CEST 2012
Author: kingaa
Date: 2012-08-22 19:32:10 +0200 (Wed, 22 Aug 2012)
New Revision: 779
Added:
www/vignettes/advanced_topics_in_pomp.R
www/vignettes/intro_to_pomp.R
Removed:
www/content/pomp.bib
Modified:
www/content/vignettes.htm
www/vignettes/Makefile
Log:
Deleted: www/content/pomp.bib
===================================================================
--- www/content/pomp.bib 2012-08-22 17:18:04 UTC (rev 778)
+++ www/content/pomp.bib 2012-08-22 17:32:10 UTC (rev 779)
@@ -1,592 +0,0 @@
-% This file was created with JabRef 2.7b.
-% Encoding: UTF8
-
- at ARTICLE{Andrieu2010,
- author = {Andrieu, Christophe and Doucet, Arnaud and Holenstein, Roman},
- title = {{P}article {M}arkov chain {M}onte {C}arlo methods},
- journal = {Journal of the Royal Statistical Society, Series B},
- year = {2010},
- volume = {72},
- pages = {269--342},
- number = {3},
- abstract = {Markov chain Monte Carlo and sequential Monte Carlo methods have emerged
- as the two main tools to sample from high dimensional probability
- distributions. Although asymptotic convergence of Markov chain Monte
- Carlo algorithms is ensured under weak assumptions, the performance
- of these algorithms is unreliable when the proposal distributions
- that are used to explore the space are poorly chosen and/or if highly
- correlated variables are updated independently. We show here how
- it is possible to build efficient high dimensional proposal distributions
- by using sequential Monte Carlo methods. This allows us not only
- to improve over standard Markov chain Monte Carlo schemes but also
- to make Bayesian inference feasible for a large class of statistical
- models where this was not previously so. We demonstrate these algorithms
- on a non-linear state space model and a Levy-driven stochastic volatility
- model.},
- doi = {10.1111/j.1467-9868.2009.00736.x},
- file = {Andrieu2010.pdf:Andrieu2010.pdf:PDF},
- owner = {kingaa},
- timestamp = {2010.06.30}
-}
-
- at ARTICLE{Arulampalam2002,
- author = {Arulampalam, M. S. and Maskell, S. and Gordon, N. and Clapp, T.},
- title = {{A} {T}utorial on {P}article {F}ilters for {O}nline {N}onlinear,
- {N}on-{G}aussian {B}ayesian {T}racking},
- journal = {IEEE Transactions on Signal Processing},
- year = {2002},
- volume = {50},
- pages = {174 -- 188},
- doi = {10.1109/78.978374},
- file = {Arulampalam2002.pdf:Desktop/Arulampalam2002.pdf:PDF},
- owner = {kingaa},
- timestamp = {2007.07.20}
-}
-
- at ARTICLE{Bhadra2010,
- author = {Bhadra, Anindya},
- title = {{D}iscussion of `{P}article {M}arkov chain {M}onte {C}arlo methods'
- by {C}.\ {A}ndrieu, {A}.\ {D}oucet and {R}.\ {H}olenstein},
- journal = {Journal of the Royal Statistical Society, Series B},
- year = {2010},
- volume = {72},
- pages = {314--315},
- doi = {10.1111/j.1467-9868.2009.00736.x},
- textref = {Bhadra, A. (2010), Discussion of `Particle Markov chain Monte Carlo
- methods' by C. Andrieu, A. Doucet and R. Holenstein, J. Roy. Stat.
- Soc B 72:314-315}
-}
-
- at ARTICLE{Breto2009,
- author = {Carles Bret\'{o} and Daihai He and Edward L. Ionides and Aaron A.
- King},
- title = {{T}ime series analysis via mechanistic models},
- journal = {Annals of Applied Statistics},
- year = {2009},
- volume = {3},
- pages = {319--348},
- number = {1},
- abstract = {The purpose of time series analysis via mechanistic models is to reconcile
- the known or hypothesized structure of a dynamical system with observations
- collected over time. We develop a framework for constructing nonlinear
- mechanistic models and carrying out inference. Our framework permits
- the consideration of implicit dynamic models, meaning statistical
- models for stochastic dynamical systems which are specified by a
- simulation algorithm to generate sample paths. Inference procedures
- that operate on implicit models are said to have the plug-and-play
- property. Our work builds on recently developed plug-and-play inference
- methodology for partially observed Markov models. We introduce a
- class of implicitly specified Markov chains with stochastic transition
- rates, and we demonstrate its applicability to open problems in statistical
- inference for biological systems. As one example, these models are
- shown to give a fresh perspective on measles transmission dynamics.
- As a second example, we present a mechanistic analysis of cholera
- incidence data, involving interaction between two competing strains
- of the pathogen Vibrio cholerae.},
- doi = {10.1214/08-AOAS201},
- file = {Breto2009.pdf:Breto2009.pdf:PDF;:Breto2009_supp.pdf:PDF},
- owner = {kingaa},
- timestamp = {2009.09.15}
-}
-
- at ARTICLE{Breto2011,
- author = {Bret{\'o}, Carles and Ionides, Edward L.},
- title = {Compound Markov counting processes and their applications to modeling
- infinitesimally over-dispersed systems},
- journal = {Stochastic Processes and their Applications},
- year = {2011},
- volume = {121},
- pages = {2571--2591},
- number = {11},
- month = nov,
- __markedentry = {[kingaa:]},
- abstract = {We propose an infinitesimal dispersion index for Markov counting processes.
- We show that, under standard moment existence conditions, a process
- is infinitesimally (over-)equi-dispersed if, and only if, it is simple
- (compound), i.e. it increases in jumps of one (or more) unit(s),
- even though infinitesimally equi-dispersed processes might be under-,
- equi- or over-dispersed using previously studied indices. Compound
- processes arise, for example, when introducing continuous-time white
- noise to the rates of simple processes resulting in L{\'e}vy-driven
- SDEs. We construct multivariate infinitesimally over-dispersed compartment
- models and queuing networks, suitable for applications where moment
- constraints inherent to simple processes do not hold.},
- doi = {10.1016/j.spa.2011.07.005},
- file = {Breto2011.pdf:Breto2011.pdf:PDF},
- issn = {0304-4149},
- keywords = {Continuous time, Counting Markov process, Birth–death process, Environmental
- stochasticity, Infinitesimal over-dispersion, Simultaneous events},
- owner = {kingaa},
- timestamp = {2012.01.17}
-}
-
- at BOOK{Davison1997,
- title = {{B}ootstrap {M}ethods and their {A}pplication},
- publisher = {Cambridge University Press},
- year = {1997},
- author = {A.C. Davison and D.V. Hinkley},
- address = {New York},
- owner = {kingaa},
- timestamp = {2011.05.26}
-}
-
- at ARTICLE{Ellner1998,
- author = {S. P. Ellner and B. A. Bailey and G. V. Bobashev and A. R. Gallant
- and B. T. Grenfell and D. W. Nychka},
- title = {{N}oise and nonlinearity in measles epidemics: {C}ombining mechanistic
- and statistical approaches to population modeling},
- journal = {American Naturalist},
- year = {1998},
- volume = {151},
- pages = {425--440},
- abstract = {We present and evaluate an approach to analyzing population dynamics
- data using semimechanistic models. These models incorporate reliable
- information on population structure and underlying dynamic mechanisms
- but use nonparametric surface-fitting methods to avoid unsupported
- assumptions about the precise form of rate equations. Using historical
- data on measles epidemics as a case study, we show how this approach
- can lead to better forecasts, better characterizations of the dynamics,
- and a better understanding of the factors causing complex population
- dynamics relative to either mechanistic models or purely descriptive
- statistical time-series models. The semimechanistic models are found
- to have better forecasting accuracy than either of the model types
- used in previous analyses when tested on data not used to fit the
- models. The dynamics are characterized as being both nonlinear and
- noisy, and the global dynamics are clustered very tightly near the
- border of stability (dominant Lyapunov exponent lambda approximate
- to 0). However, locally in state space the dynamics oscillate between
- strong short-term stability and strong short-term chaos (i.e., between
- negative and positive local Lyapunov exponents). There is statistically
- significant evidence for short-term chaos in all data sets examined.
- Thus the nonlinearity in these systems is characterized by the variance
- over state space in local measures of chaos versus stability rather
- than a single summary measure of the overall dynamics as either chaotic
- or nonchaotic.},
- file = {Ellner1998.pdf:Ellner1998.pdf:PDF},
- owner = {kingaa},
- timestamp = {2009.09.22}
-}
-
- at ARTICLE{Gillespie1977a,
- author = {D. T. Gillespie},
- title = {{E}xact {S}tochastic {S}imulation of {C}oupled {C}hemical {R}eactions},
- journal = {Journal of Physical Chemistry},
- year = {1977},
- volume = {81},
- pages = {2340--2361},
- file = {Gillespie1977a.pdf:Gillespie1977a.pdf:PDF},
- owner = {kingaa},
- timestamp = {2007.03.13}
-}
-
- at BOOK{Gourieroux1996,
- title = {{S}imulation-based {E}conometric {M}ethods},
- publisher = {Oxford University Press},
- year = {1996},
- author = {C. Gouri\'{e}roux and A. Monfort},
- owner = {kingaa},
- timestamp = {2011.05.26}
-}
-
- at ARTICLE{He2010,
- author = {He, Daihai and Ionides, Edward L. and King, Aaron A.},
- title = {{P}lug-and-play inference for disease dynamics: measles in large
- and small populations as a case study},
- journal = {Journal of the Royal Society Interface},
- year = {2010},
- volume = {7},
- pages = {271--283},
- month = jun,
- abstract = {Statistical inference for mechanistic models of partially observed
- dynamic systems is an active area of research. Most existing inference
- methods place substantial restrictions upon the form of models that
- can be fitted and hence upon the nature of the scientific hypotheses
- that can be entertained and the data that can be used to evaluate
- them. In contrast, the so-called methods require only simulations
- from a model and are thus free of such restrictions. We show the
- utility of the plug-and-play approach in the context of an investigation
- of measles transmission dynamics. Our novel methodology enables us
- to ask and answer questions that previous analyses have been unable
- to address. Specifically, we demonstrate that plug-and-play methods
- permit the development of a modelling and inference framework applicable
- to data from both large and small populations. We thereby obtain
- novel insights into the nature of heterogeneity in mixing and comment
- on the importance of including extra-demographic stochasticity as
- a means of dealing with environmental stochasticity and model misspecification.
- Our approach is readily applicable to many other epidemiological
- and ecological systems.},
- doi = {10.1098/rsif.2009.0151},
- file = {He2010.pdf:He2010.pdf:PDF;:He2010_Supplement.pdf:PDF},
- owner = {kingaa},
- timestamp = {2009.06.26}
-}
-
- at ARTICLE{Ionides2011,
- author = {E. L. Ionides and A. Bhadra and Y. Atchad{\'e} and A. A. King},
- title = {Iterated filtering},
- journal = {Annals of Statistics},
- year = {2011},
- volume = {39},
- pages = {1776--1802},
- number = {3},
- abstract = {Inference for partially observed Markov process models has been a
- longstanding methodological challenge with many scientific and engineering
- applications. Iterated filtering algorithms maximize the likelihood
- function for partially observed Markov process models by solving
- a recursive sequence of filtering problems. We present new theoretical
- results pertaining to the convergence of iterated filtering algorithms
- implemented via sequential Monte Carlo filters. This theory complements
- the growing body of empirical evidence that iterated filtering algorithms
- provide an effective inference strategy for scientific models of
- nonlinear dynamic systems. The first step in our theory involves
- studying a new recursive approach for maximizing the likelihood function
- of a latent variable model, when this likelihood is evaluated via
- importance sampling. This leads to the consideration of an iterated
- importance sampling algorithm which serves as a simple special case
- of iterated filtering, and may have applicability in its own right.},
- doi = {10.1214/11-AOS886},
- file = {Ionides2011.pdf:Ionides2011.pdf:PDF},
- owner = {kingaa},
- timestamp = {2011.03.07}
-}
-
- at ARTICLE{Ionides2006,
- author = {E. L. Ionides and C. Bret{\'o} and Aaron A. King},
- title = {{I}nference for nonlinear dynamical systems},
- journal = {Proceedings of the National Academy of Sciences of the U.S.A.},
- year = {2006},
- volume = {103},
- pages = {18438--18443},
- number = {49},
- abstract = {Nonlinear stochastic dynamical systems are widely used to model systems
- across the sciences and engineering. Such models are natural to formulate
- and can be analyzed mathematically and numerically. However, difficulties
- associated with inference from time-series data about unknown parameters
- in these models have been a constraint on their application. We present
- a new method that makes maximum likelihood estimation feasible for
- partially-observed nonlinear stochastic dynamical systems (also known
- as state-space models) where this was not previously the case. The
- method is based on a sequence of filtering operations which are shown
- to converge to a maximum likelihood parameter estimate. We make use
- of recent advances in nonlinear filtering in the implementation of
- the algorithm. We apply the method to the study of cholera in Bangladesh.
- We construct confidence intervals, perform residual analysis, and
- apply other diagnostics. Our analysis, based upon a model capturing
- the intrinsic nonlinear dynamics of the system, reveals some effects
- overlooked by previous studies.},
- doi = {10.1073/pnas.0603181103},
- file = {Ionides2006.pdf:Ionides2006.pdf:PDF},
- owner = {kingaa},
- timestamp = {2006.10.06}
-}
-
- at ARTICLE{Kendall1999,
- author = {B. E. Kendall and C. J. Briggs and W. W. Murdoch and P. Turchin and
- S. P. Ellner and E. McCauley and R. M. Nisbet and S. N. Wood},
- title = {{W}hy do populations cycle? {A} synthesis of statistical and mechanistic
- modeling approaches},
- journal = {Ecology},
- year = {1999},
- volume = {80},
- pages = {1789--1805},
- abstract = {Population cycles have long fascinated ecologists. Even in the most-studied
- populations, however, scientists continue to dispute the relative
- importance of various potential causes of the cycles, Over the past
- three decades, theoretical ecologists have cataloged a large number
- of mechanisms that are capable of generating cycles in population
- models. At the same time, statisticians have developed new techniques
- both for characterizing time series and for fitting population models
- to time-series data. Both disciplines are now sufficiently advanced
- that great gains in understanding can be made by synthesizing these
- complementary, and heretofore mostly independent, quantitative approaches.
- In this paper we demonstrate how to apply this synthesis to the problem
- of population cycles, using both long-term population time series
- and the often-rich observational and experimental data on the ecology
- of the species in question. We quantify hypotheses by writing mathematical
- models that embody the interactions and forces that might cause cycles.
- Some hypotheses can be rejected out of hand, as being unable to generate
- even qualitatively appropriate dynamics, We finish quantifying the
- remaining hypotheses by estimating parameters, both from independent
- experiments and from fitting the models to the time-series data using
- modern statistical techniques, Finally, we compare simulated time
- series generated by the models to the observed time series, using
- a variety of statistical descriptors, which we refer to collectively
- as "probes." The model most similar to the data, as measured by these
- probes, is considered to be the most likely candidate to represent
- the mechanism underlying the population cycles. We illustrate this
- approach by analyzing one of Nicholson's blowfly populations, in
- which we know the "true" governing mechanism. Our analysis, which
- uses only a subset of the information available about the population,
- uncovers the correct answer, suggesting that this synthetic approach
- might be successfully applied to field populations as well.},
- file = {Kendall1999.pdf:Kendall1999.pdf:PDF},
- owner = {kingaa},
- timestamp = {2009.09.22}
-}
-
- at ARTICLE{Kendall2005,
- author = {Kendall, B. E. and Ellner, S. P. and McCauley, E. and Wood, S. N.
- and Briggs, C. J. and Murdoch, W. M. and Turchin, P.},
- title = {{P}opulation cycles in the pine looper moth: {D}ynamical tests of
- mechanistic hypotheses},
- journal = {Ecological Monographs},
- year = {2005},
- volume = {75},
- pages = {259--276},
- number = {2},
- abstract = {The forest insect pest Bupalus piniarius (pine looper moth) is a classic
- example of a natural population cycle. As is typical for Populations
- that exhibit regular oscillations in density, there are several biological
- mechanisms that are hypothesized to be responsible for the cycles;
- but despite several decades of detailed study there has been no definite
- conclusion as to which mechanism is most important. We evaluated
- three hypotheses for which there was direct experimental evidence:
- (1) food quality (nutritional value of pine needles affected by defoliation);
- (2) parasitoids (trophic interactions with specialist parasitoids),
- and (3) maternal effects (maternal body size affects the performance
- of offspring). We reviewed the empirical evidence for each of these
- hypotheses and expressed each hypothesis in the form of a mechanistic
- dynamic model. We used a nonlinear forecasting approach to fit each
- model to three long-term Population time series in Britain that exhibit
- some degree of regular cycling, and we used parametric bootstrap
- to evaluate the significance of differences between models in their
- goodness of fit to the data. The results differed among the three
- forests: at Culbin, the parasitoid and maternal effects models fit
- equally well; at Roseisle, the food quality and maternal effects
- models fit equally well; and at Tentsmuir, the parasitoid model fit
- best. However, the best-fit parasitism models required that the parasitism
- rate vary between nearly 0 and nearly 1 during a cycle, greatly exceeding
- the range of parasitism rates that have been observed in the field.
- In contrast, the required variation in the observable maternal quality
- variable (pupal mass) was within the range of empirical observations.
- Under mild constraints on the parasitism rate (though allowing a
- much wider range than has been measured in B. piniarius at any location),
- the fit of the parasitism model fell off dramatically. The maternal
- effects model then had uniformly strong support, outperforming the
- constrained parasitism model at all three sites and the food quality
- model at two; it performed slightly better than the food quality
- model at the remaining site. This represents the first system in
- which the maternal effects hypothesis for population cycles has been
- supported by both strong biological and dynamical evidence.},
- file = {Kendall2005.pdf:Kendall2005.pdf:PDF},
- owner = {kingaa},
- timestamp = {2009.09.22}
-}
-
- at ARTICLE{King2008,
- author = {King, Aaron A. and Ionides, Edward L. and Pascual, Mercedes and Bouma,
- Menno J.},
- title = {{I}napparent infections and cholera dynamics},
- journal = {Nature},
- year = {2008},
- volume = {454},
- pages = {877--880},
- number = {7206},
- month = aug,
- abstract = {In many infectious diseases, an unknown fraction of infections produce
- symptoms mild enough to go unrecorded, a fact that can seriously
- compromise the interpretation of epidemiological records. This is
- true for cholera, a pandemic bacterial disease, where estimates of
- the ratio of asymptomatic to symptomatic infections have ranged from
- 3 to 100. In the absence of direct evidence, understanding of fundamental
- aspects of cholera transmission, immunology and control has been
- based on assumptions about this ratio and about the immunological
- consequences of inapparent infections. Here we show that a model
- incorporating high asymptomatic ratio and rapidly waning immunity,
- with infection both from human and environmental sources, explains
- 50 yr of mortality data from 26 districts of Bengal, the pathogen's
- endemic home. We find that the asymptomatic ratio in cholera is far
- higher than had been previously supposed and that the immunity derived
- from mild infections wanes much more rapidly than earlier analyses
- have indicated. We find, too, that the environmental reservoir (free-living
- pathogen) is directly responsible for relatively few infections but
- that it may be critical to the disease's endemicity. Our results
- demonstrate that inapparent infections can hold the key to interpreting
- the patterns of disease outbreaks. New statistical methods, which
- allow rigorous maximum likelihood inference based on dynamical models
- incorporating multiple sources and outcomes of infection, seasonality,
- process noise, hidden variables and measurement error, make it possible
- to test more precise hypotheses and obtain unexpected results. Our
- experience suggests that the confrontation of time-series data with
- mechanistic models is likely to revise our understanding of the ecology
- of many infectious diseases.},
- doi = {10.1038/nature07084},
- file = {King2008.pdf:King2008.pdf:PDF;King2008_Supplement.pdf:King2008_Supplement.pdf:PDF},
- owner = {kingaa},
- publisher = {Macmillan Publishers Limited. All rights reserved},
- timestamp = {2008.08.13}
-}
-
- at ARTICLE{Knape2011,
- author = {Knape, Jonas and de Valpine, Perry},
- title = {Fitting complex population models by combining particle filters with
- Markov chain Monte Carlo},
- journal = {Ecology},
- year = {2011},
- volume = {93},
- pages = {256--263},
- number = {2},
- month = oct,
- __markedentry = {[kingaa:]},
- abstract = {We show how a recent framework combining Markov chain Monte Carlo
- (MCMC) with particle filters (PFMCMC) may be used to estimate population
- state-space models. With the purpose of utilizing the strengths of
- each method, PFMCMC explores hidden states by particle filters, while
- process and observation parameters are estimated using an MCMC algorithm.
- PFMCMC is exemplified by analyzing time series data on a red kangaroo
- (Macropus rufus) population in New South Wales, Australia, using
- MCMC over model parameters based on an adaptive Metropolis-Hastings
- algorithm. We fit three population models to these data; a density-dependent
- logistic diffusion model with environmental variance, an unregulated
- stochastic exponential growth model, and a random-walk model. Bayes
- factors and posterior model probabilities show that there is little
- support for density dependence and that the random-walk model is
- the most parsimonious model. The particle filter Metropolis-Hastings
- algorithm is a brute-force method that may be used to fit a range
- of complex population models. Implementation is straightforward and
- less involved than standard MCMC for many models, and marginal densities
- for model selection can be obtained with little additional effort.
- The cost is mainly computational, resulting in long running times
- that may be improved by parallelizing the algorithm.},
- booktitle = {Ecology},
- doi = {10.1890/11-0797.1},
- file = {:Knape2011.pdf:PDF},
- issn = {0012-9658},
- owner = {kingaa},
- publisher = {Ecological Society of America},
- timestamp = {2012.06.01}
-}
-
- at INCOLLECTION{Liu2001b,
- author = {Liu, J and West, M.},
- title = {{C}ombining {P}arameter and {S}tate {E}stimation in {S}imulation-{B}ased
- {F}iltering},
- booktitle = {Sequential {M}onte {C}arlo Methods in Practice},
- publisher = {Springer, New York},
- year = {2001},
- editor = {Doucet, A. and de Freitas, N. and Gordon, N. J.},
- pages = {197--224},
- owner = {kingaa},
- timestamp = {2009.09.22}
-}
-
- at ARTICLE{Reuman2006,
- author = {Reuman, D. C. and Desharnais, R. A. and Costantino, R. F. and Ahmad,
- O. S. and Cohen, J. E.},
- title = {{P}ower spectra reveal the influence of stochasticity on nonlinear
- population dynamics},
- journal = {Proceedings of the National Academy of Sciences of the U.S.A.},
- year = {2006},
- volume = {103},
- pages = {18860--18865 },
- url = {http://dx.doi.org/10.1073/pnas.0608571103}
-}
-
- at ARTICLE{Smith1993,
- author = {A. A. Smith},
- title = {{E}stimating nonlinear time-series models using simulated vector
- autoregression},
- journal = {Journal of Applied Econometrics},
- year = {1993},
- volume = {8},
- pages = {S63--S84},
- owner = {kingaa},
- timestamp = {2011.05.26}
-}
-
- at ARTICLE{Tidd1993,
- author = {Tidd, C. W. and Olsen, L. F. and Schaffer, W. M.},
- title = {{T}he {C}ase for {C}haos in {C}hildhood {E}pidemics. {II}. {P}redicting
- {H}istorical {E}pidemics from {M}athematical {M}odels},
- journal = {Proceedings of the Royal Society of London, Series B},
- year = {1993},
- volume = {254},
- pages = {257--273},
- number = {1341},
- month = {Dec.},
- abstract = {The case for chaos in childhood epidemics rests on two observations.
- The first is that historical epidemics show various `fieldmarks'
- of chaos, such as positive Lyapunov exponents. Second, phase portraits
- reconstructed from real-world epidemiological time series bear a
- striking resemblance to chaotic solutions obtained from certain epidemiological
- models. Both lines of evidence are subject to dispute: the algorithms
- used to look for the fieldmarks can be fooled by short, noisy time
- series, and the same fieldmarks can be generated by stochastic models
- in which there is demonstrably no chaos at all. In the present paper,
- we compare the predictive abilities of stochastic models with those
- of mechanistic scenarios that admit to chaotic solutions. The main
- results are as follows: (i) the mechanistic models outperform their
- stochastic counterparts; (ii) forecasting efficacy of the deterministic
- models is maximized by positing parameter values that induce chaotic
- behaviour; (iii) simple mechanistic models are equal if not superior
- to more detailed schemes that include age structure; and (iv) prediction
- accuracy for monthly notifications declines rapidly with time, so
- that, from a practical standpoint, the results are of little value.
- By way of contrast, next amplitude maps can successfully forecast
- successive changes in maximum incidence one or more years into the
- future.},
- file = {Tidd1993.pdf:Tidd1993.pdf:PDF},
- owner = {kingaa},
- timestamp = {2009.09.22},
- url = {http://links.jstor.org/sici?sici=0962-8452%2819931222%29254%3A1341%3C257%3ATCFCIC%3E2.0.CO%3B2-G}
-}
-
- at ARTICLE{Wearing2009,
- author = {Helen J Wearing and Pejman Rohani},
- title = {{E}stimating the duration of pertussis immunity using epidemiological
- signatures.},
- journal = {PLoS Pathogens},
- year = {2009},
- volume = {5},
- pages = {e1000647},
- number = {10},
- month = {Oct},
- abstract = {Case notifications of pertussis have shown an increase in a number
- of countries with high rates of routine pediatric immunization. This
- has led to significant public health concerns over a possible pertussis
- re-emergence. A leading proposed explanation for the observed increase
- in incidence is the loss of immunity to pertussis, which is known
- to occur after both natural infection and vaccination. Little is
- known, however, about the typical duration of immunity and its epidemiological
- implications. Here, we analyze a simple mathematical model, exploring
- specifically the inter-epidemic period and fade-out frequency. These
- predictions are then contrasted with detailed incidence data for
- England and Wales. We find model output to be most sensitive to assumptions
- concerning naturally acquired immunity, which allows us to estimate
- the average duration of immunity. Our results support a period of
- natural immunity that is, on average, long-lasting (at least 30 years)
- but inherently variable.},
- doi = {10.1371/journal.ppat.1000647},
- file = {:Wearing2009.pdf:PDF},
- institution = { Statistics, University of New Mexico, Albuquerque, New Mexico, USA.
- hwearing at unm.edu},
- language = {eng},
- medline-pst = {ppublish},
- owner = {kingaa},
- pmid = {19876392},
- timestamp = {2010.02.05}
-}
-
- at ARTICLE{Wood2010,
- author = {Wood, Simon N.},
- title = {{S}tatistical inference for noisy nonlinear ecological dynamic systems},
- journal = {Nature},
- year = {2010},
- volume = {466},
- pages = {1102--1104},
- month = aug,
- doi = {10.1038/nature09319},
- file = {Wood2010.pdf:Wood2010.pdf:PDF;Wood2010_Supplement.pdf:Wood2010_Supplement.pdf:PDF},
- issn = {1476-4687},
- owner = {kingaa},
- timestamp = {2010.08.17}
-}
-
- at comment{jabref-meta: selector_publisher:}
-
- at comment{jabref-meta: selector_author:}
-
- at comment{jabref-meta: selector_journal:}
-
- at comment{jabref-meta: selector_keywords:}
-
Modified: www/content/vignettes.htm
===================================================================
--- www/content/vignettes.htm 2012-08-22 17:18:04 UTC (rev 778)
+++ www/content/vignettes.htm 2012-08-22 17:32:10 UTC (rev 779)
@@ -1,5 +1,14 @@
<h2>Vignettes of package pomp</h2>
-<p><a href="vignettes/intro_to_pomp.pdf">Introduction to <b>pomp</b></a></p>
-
-<p><a href="vignettes/advanced_topics_in_pomp.pdf">Advanced topics in <b>pomp</b></a></p>
+<table>
+<tr>
+<td>Introduction to <strong>pomp</strong></td>
+<td><a target="_blank" href="vignettes/intro_to_pomp.pdf">(PDF)</a></td>
+<td><a target="_blank" href="vignettes/intro_to_pomp.R">(R code)</a></td>
+</tr>
+<tr>
+<td>Advanced topics in <strong>pomp</strong></td>
+<td><a target="_blank" href="vignettes/advanced_topics_in_pomp.pdf">(PDF)</a></td>
+<td><a target="_blank" href="vignettes/advanced_topics_in_pomp.R">(R code)</a></td>
+</tr>
+</table>
Modified: www/vignettes/Makefile
===================================================================
--- www/vignettes/Makefile 2012-08-22 17:18:04 UTC (rev 778)
+++ www/vignettes/Makefile 2012-08-22 17:32:10 UTC (rev 779)
@@ -6,8 +6,11 @@
default: vignettes clean
-vignettes: advanced_topics_in_pomp.pdf intro_to_pomp.pdf
+vignettes: advanced_topics_in_pomp.pdf advanced_topics_in_pomp.R intro_to_pomp.pdf intro_to_pomp.R
+%.R: %.Rnw
+ $(REXE) CMD Stangle $*
+
%.tex: %.Rnw
$(REXE) CMD Sweave $*
Added: www/vignettes/advanced_topics_in_pomp.R
===================================================================
--- www/vignettes/advanced_topics_in_pomp.R (rev 0)
+++ www/vignettes/advanced_topics_in_pomp.R 2012-08-22 17:32:10 UTC (rev 779)
@@ -0,0 +1,729 @@
+### R code from vignette source 'advanced_topics_in_pomp'
+### Encoding: UTF-8
+
+###################################################
+### code chunk number 1: set-opts
+###################################################
+ glop <- options(keep.source=TRUE,width=60,continue=" ",prompt=" ")
+ library(pomp)
+ pdf.options(useDingbats=FALSE)
+ set.seed(5384959)
+
+
+###################################################
+### code chunk number 2: plugin-R-code
+###################################################
+data(ou2)
+ou2.dat <- as.data.frame(ou2)
+
+pomp(
+ data=ou2.dat[c("time","y1","y2")],
+ times="time",
+ t0=0,
+ rprocess=discrete.time.sim(
+ step.fun=function (x, t, params, ...) {
+ eps <- rnorm(n=2,mean=0,sd=1) # noise terms
+ xnew <- c(
+ x1=params["alpha.1"]*x["x1"]+params["alpha.3"]*x["x2"]+
+ params["sigma.1"]*eps[1],
+ x2=params["alpha.2"]*x["x1"]+params["alpha.4"]*x["x2"]+
+ params["sigma.2"]*eps[1]+params["sigma.3"]*eps[2]
+ )
+ names(xnew) <- c("x1","x2")
+ xnew
+ }
+ )
+ ) -> ou2.Rplug
+
+
+###################################################
+### code chunk number 3: plugin-R-code-sim
+###################################################
+binary.file <- "plugin-R-code.rda"
+if (file.exists(binary.file)) {
+ load(binary.file)
+} else {
+ tic <- Sys.time()
+simdat.Rplug <- simulate(ou2.Rplug,params=coef(ou2),nsim=1000,states=T)
+toc <- Sys.time()
+etime.Rplug <- toc-tic
+n.Rplug <- dim(simdat.Rplug)[2]
+save(etime.Rplug,n.Rplug,file=binary.file,compress='xz')
+}
+
+
+###################################################
+### code chunk number 4: vectorized-R-code (eval = FALSE)
+###################################################
+## ou2.Rvect.rprocess <- function (xstart, times, params, ...) {
+## nrep <- ncol(xstart) # number of realizations
+## ntimes <- length(times) # number of timepoints
+## ## unpack the parameters (for legibility only)
+## alpha.1 <- params["alpha.1",]
+## alpha.2 <- params["alpha.2",]
+## alpha.3 <- params["alpha.3",]
+## alpha.4 <- params["alpha.4",]
+## sigma.1 <- params["sigma.1",]
+## sigma.2 <- params["sigma.2",]
+## sigma.3 <- params["sigma.3",]
+## ## x is the array of states to be returned: it must have rownames
+## x <- array(0,dim=c(2,nrep,ntimes))
+## rownames(x) <- rownames(xstart)
+## ## xnow holds the current state values
+## x[,,1] <- xnow <- xstart
+## tnow <- times[1]
+## for (k in seq.int(from=2,to=ntimes,by=1)) {
+## tgoal <- times[k]
+## while (tnow < tgoal) { # take one step at a time
+## eps <- array(rnorm(n=2*nrep,mean=0,sd=1),dim=c(2,nrep))
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/pomp -r 779
More information about the pomp-commits
mailing list