Help for package panelPomp

Type:

Package

Title:

Inference for Panel Partially Observed Markov Processes

Version:

1.7.0.0

Description:

Data analysis based on panel partially-observed Markov process (PanelPOMP) models. To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" <doi:10.1080/01621459.2019.1604367>.

License:

GPL-3

Depends:

R(≥ 4.1.0), pomp(≥ 4.5.2)

Imports:

methods

RoxygenNote:

7.3.2

Encoding:

UTF-8

Collate:

'panelPomp-package.R' 'aaa.R' 'contacts.R' 'generics.R' 'get_col_row.R' 'panel_loglik.R' 'panel_logmeanexp.R' 'panelPomp.R' 'panelPomp_methods.R' 'params.R' 'pfilter.R' 'pfilter_methods.R' 'mif2.R' 'mif2_methods.R' 'panelGompertz.R' 'panelGompertzLikelihood.R' 'panelMeasles.R' 'panelRandomWalk.R' 'panel_designs.R' 'plot.R' 'simulate.R' 'twentycities.R' 'uk_measles.R'

Suggests:

knitr, rmarkdown, bookdown

VignetteBuilder:

knitr

LazyData:

true

NeedsCompilation:

yes

Packaged:

2025-05-09 14:00:43 UTC; jeswheel

Author:

Carles Breto

[aut], Edward L. Ionides

[aut], Aaron A. King

[aut], Jesse Wheeler

[aut, cre], Aaron Abkemeier [ctb]

Maintainer:

Jesse Wheeler <jeswheel@umich.edu>

Repository:

CRAN

Date/Publication:

2025-05-09 14:40:02 UTC

Inference for PanelPOMPs (Panel Partially Observed Markov Processes)

Description

The panelPomp package provides facilities for inference on panel data using panel partially-observed Markov process (PanelPOMP) models. To do so, it relies on and extends a number of facilities that the pomp package provides for inference on time series data using partially-observed Markov process (POMP) models.

The panelPomp package extends to panel data some of the capabilities of the pomp package to fit nonlinear, non-Gaussian dynamic models. This is done accomodating both fixed and random effects. Currently, the focus is on likelihood-based approaches. In addition to these likelihood-based tools, panelPomp also provides a framework under which alternative statistical methods for PanelPOMP models can be developed (very much like pomp provides a platform upon which statistical inference methods for POMP models can be implemented).

Data analysis using panelPomp

The first step in using panelPomp is to encode one's model(s) and data in objects of class panelPomp. One does this via a call to the panelPomp constructor function.

panelPomp version 1.7.0.0 provides algorithms for

particle filtering of panel data (AKA sequential Monte Carlo or sequential importance sampling), as proposed in Bretó, Ionides and King (2020). This reference provides the fundamental theoretical support for the averaging of Monte Carlo replicates of panel unit likelihoods as implemented in panelPomp; see pfilter
the panel iterated filtering method of Bretó, Ionides and King (2020). This reference provides the fundamental theoretical support for the extensions of the iterated filtering ideas of Ionides et al. (2006, 2011, 2015) to panel data as implemented in panelPomp; see mif2

The package also provides various tools for handling and extracting information on models and data.

Extending the pomp platform for developing inference tools

panelPomp extends to panel data the general interface to the components of POMP models provided by pomp. In doing so, it contributes to the goal of the pomp project of facilitating the development of new algorithms in an environment where they can be tested and compared on a growing body of models and datasets.

Comments, bug reports, and requests

Contributions are welcome, as are suggestions for improvement, feature requests, and bug reports. Please submit these via the panelPomp issues page. We particularly welcome minimal working examples displaying uninformative, misleading or inacurate error messages. We also welcome suggestions for clarifying obscure passages in the documentation. Help requests are welcome, but please consider before sending requests whether they are regarding the use of panelPomp or that of pomp. For help with pomp, please visit pomp's FAQ.

Documentation

Examples are provided via the contacts(), panelGompertz() and panelRandomWalk() functions.

License

panelPomp is provided under the GPL-3 License.

Author(s)

Maintainer: Jesse Wheeler jeswheel@umich.edu (ORCID)

Authors:

Carles Breto carles.breto@uv.es (ORCID)
Edward L. Ionides (ORCID)
Aaron A. King (ORCID)

Other contributors:

Aaron Abkemeier aaronabk@umich.edu [contributor]

References

Bretó, C., Ionides, E. L. and King, A. A. (2020) Panel Data Analysis via Mechanistic Models. Journal of the American Statistical Association, 115(531), 1178–1188. doi:10.1080/01621459.2019.1604367

Internal function for modifying pparamArray in Mif2

Description

Internal function for modifying pparamArray in Mif2

Usage

.modifyOther(x, spnames, indices, unit)

Arguments

x

pparamArray in mif2 internal

spnames

names of parameters to update

indices

indices that are the output of a mfi2 call to pomp

unit

unit in the unit loop

Internal function for modifying pparamArray in Mif2

Description

Internal function for modifying pparamArray in Mif2

Usage

.modifySelf(x, spnames, M, unit)

Arguments

x

pparamArray in mif2 internal

spnames

names of parameters to update

M

Matrix of replacement values

unit

unit in the unit loop

Coercing `panelPomp` objects as `list`, `pompList` or `data.frame`

Description

When coercing to a data.frame, it coerces a panelPomp into a data.frame, assuming units share common variable names.

When coercing to a list, it extracts the unit_objects slot of panelPomp objects and attaches associated parameters.

When coercing to a pompList, it extracts the unit_objects slot of panelPomp objects and attaches associated parameters, converting the resulting list to a pompList to help the assignment of pomp methods.

Value

An object of class matching that specified in the second argument (to=).

Author(s)

Carles Bretó

Modifying parameters of filtered objects

Description

The setter functions for parameters of pfilterd.ppomp objects do not allow users to set parameters of panelPomp objects that have been filtered. This is done to avoid the possibility of having parameter values in an object that do not match other attributes of a filtered object to be saved together.

Usage

## S4 replacement method for signature 'pfilterd.ppomp'
coef(object, ...) <- value

## S4 replacement method for signature 'pfilterd.ppomp'
shared(object) <- value

## S4 replacement method for signature 'pfilterd.ppomp'
specific(object) <- value

Arguments

object

pfilterd.ppomp object

...

additional arguments.

value

New parameter value. This function does not allow users to set this value.

Contacts model

Description

A panel model for dynamic variation in sexual contacts, with data from Vittinghof et al (1999). The model was developed by Romero-Severson et al (2015) and discussed by Bretó et al (2020).

Usage

contacts(
  params = c(mu_X = 1.75, sigma_X = 2.67, mu_D = 3.81, sigma_D = 4.42, mu_R = 0.04,
    sigma_R = 0, alpha = 0.9)
)

Arguments

params

parameter vector.

Value

A panelPomp object.

Author(s)

Edward L. Ionides

References

Romero-Severson, E.O., Volz, E., Koopman, J.S., Leitner, T. and Ionides, E.L. (2015) Dynamic variation in sexual contact rates in a cohort of HIV-negative gay men. American journal of epidemiology, 182(3), 255–262. doi:10.1093/aje/kwv044

Vitinghoff, E., Douglas, J., Judon, F., McKiman, D., MacQueen, K. and Buchinder, S.P. (1999) Per-contact risk of human immunodificiency virus tramnsmision between male sexual partners. American journal of epidemiology, 150(3), 306–311. doi:10.1093/oxfordjournals.aje.a010003

Examples


contacts()

Get single column or row without dropping names

Description

Subset matrix dropping dimension but without dropping dimname (as done by `[` by default).

Usage

get_col(matrix, rows, col)

get_row(matrix, row, cols)

Arguments

matrix

matrix.

rows

numeric; rows to subset; like with `[`, this argument can be left empty to designate all rows.

col

integer; single column to subset.

row

integer; single row to subset.

cols

numeric; columns to subset; like with `[`, this argument can be left empty to designate all columns.

Value

A named vector object.

Author(s)

Carles Bretó

Examples

m <- matrix(NA,dimnames=list('r1','c1'))
m[1,1] # = NA; R removes both names
get_col(m,rows=1,col=1) # = c(r1=NA) keeps colname
get_row(m,row=1,cols=1) # = c(c1=NA) keeps rowname

PIF: Panel iterated filtering

Description

Tools for applying iterated filtering algorithms to panel data. The panel iterated filtering of Bretó et al. (2020) extends to panel models the improved iterated filtering algorithm (Ionides et al., 2015) for estimating parameters of a partially observed Markov process. Iterated filtering algorithms rely on extending a partially observed Markov process model of interest by introducing random perturbations to the model parameters. The space where the original parameters live is then explored at each iteration by running a particle filter. Convergence to a maximum likelihood estimate has been established for appropriately constructed procedures that iterate this search over the parameter space while diminishing the intensity of perturbations (Ionides et al. 2006, 2011, 2015).

Usage

## S4 method for signature 'panelPomp'
mif2(
  data,
  Nmif = 1,
  shared.start,
  specific.start,
  start,
  Np,
  rw.sd,
  cooling.type = c("geometric", "hyperbolic"),
  cooling.fraction.50,
  block = FALSE,
  verbose = getOption("verbose"),
  ...
)

## S4 method for signature 'mif2d.ppomp'
mif2(
  data,
  Nmif,
  shared.start,
  specific.start,
  start,
  Np,
  rw.sd,
  cooling.type,
  cooling.fraction.50,
  block,
  ...
)

## S4 method for signature 'mif2d.ppomp'
traces(object, pars, ...)

Arguments

data

An object of class panelPomp or inheriting class.

Nmif

The number of filtering iterations to perform.

shared.start

named numerical vector; the starting guess of the shared parameters.

specific.start

matrix with row parameter names and column unit names; the starting guess of the specific parameters.

start

A named numeric vector of parameters at which to start the IF2 procedure.

Np

the number of particles to use. This may be specified as a single positive integer, in which case the same number of particles will be used at each timestep. Alternatively, if one wishes the number of particles to vary across timesteps, one may specify Np either as a vector of positive integers of length

length(time(object,t0=TRUE))

or as a function taking a positive integer argument. In the latter case, Np(k) must be a single positive integer, representing the number of particles to be used at the k-th timestep: Np(0) is the number of particles to use going from timezero(object) to time(object)[1], Np(1), from timezero(object) to time(object)[1], and so on, while when T=length(time(object)), Np(T) is the number of particles to sample at the end of the time-series.

rw.sd

An unevaluated expression of the form quote(rw.sd()) to be used for all panel units. If a list of such expressions of the same length as the object argument is provided, each list element will be used for the corresponding panel unit.

cooling.type, cooling.fraction.50

specifications for the cooling schedule, i.e., the manner and rate with which the intensity of the parameter perturbations is reduced with successive filtering iterations. cooling.type specifies the nature of the cooling schedule. See below (under “Specifying the perturbations”) for more detail.

block

A logical variable determining whether to carry out block resampling of unit-specific parameters.

verbose

logical; if TRUE, diagnostic messages will be printed to the console.

...

....

object

an object resulting from the application of IF2 (i.e., of class mif2d.ppomp)

pars

names of parameters

Value

mif2() returns an object of class mif2d.ppomp.

traces() returns a matrix with estimated parameter values at different iterations of the IF2 algorithm in the natural scale. The default is to return values for all parameters but a subset of parameters can be passed via the optional argument pars.

Author(s)

Carles Bretó

References

Ionides, E. L., Bretó, C. and King, A. A. (2006) Inference for nonlinear dynamical systems. Proceedings of the National Academy of Sciences, 103(49), 18438–18443. doi:10.1073/pnas.0603181103

Ionides, E. L., Bhadra, A., Atchadé, Y. and King, A. A. (2011) Iterated filtering. Annals of Statistics, 39(3), 1776–1802. doi:10.1214/11-AOS886

Ionides, E. L., Nguyen, D., Atchadé, Y., Stoev, S. and King, A. A. (2015) Inference via iterated, perturbed Bayes maps. Proceedings of the National Academy of Sciences, 112(3), 719–724. doi:10.1073/pnas.1410597112

King, A. A., Nguyen, D. and Ionides, E. L. (2016) Statistical inference for partially observed Markov processes via the package pomp. Journal of Statistical Software 69(12), 1–43. DOI: 10.18637/jss.v069.i12. An updated version of this paper is available on the package website.

Examples

## start with a panelPomp object
p <- panelRandomWalk()
## specify which parameters to estimate via rw_sd() and how fast to cool
mp <- mif2(p,Np=10,rw.sd=rw_sd(X.0=0.2),cooling.fraction.50=0.5,cooling.type="geometric")
mp
## the object resulting from an initial estimation can be used as a new starting point
mmp <- mif2(mp,Np=10,rw.sd=rw_sd(X.0=0.2),cooling.fraction.50=0.5,cooling.type="geometric")
mmp
## convergence can be partly diagnosed by checking estimates and likelihoods at different iterations
traces(mmp)

#' Create design matrix for panelPomp calculations

Description

These functions are useful for generating design matrices for the exploration of parameter space.

Usage

runif_panel_design(
  lower = numeric(0),
  upper = numeric(0),
  nseq,
  specific_names,
  unit_names
)

Arguments

lower, upper

named numeric vectors giving the lower and upper bounds of the ranges, respectively.

nseq

Total number of points requested

specific_names

Character vector containing the names of unit-specific parameters. This argument must be used in conjunction with the argument unit_names; it is used if the search bounds for all unspecified unit-specific parameters are the same.

unit_names

Character vector containing the names of the units of the panel. If not used in conjunction with unit_names this argument is ignored.

Value

runif_panel_design returns a data.frame object with nseq rows. Each row corresponds to a parameter set drawn randomly from a multivariate uniform distribution specified by the lower, upper, specific_names and unit_names arguments.

Author(s)

Jesse Wheeler, Aaron A. King

Examples

runif_panel_design(
  lower = c('a' = 0, 'b' = 10, 'a[u2]' = 0.5),
  upper = c('a' = 1, 'b' = 15, 'a[u2]' = 0.75),
  specific_names = c('a'),
  unit_names = paste0(rep('u', 5), 1:5),
  nseq = 10
)

Panel Gompertz model

Description

Builds a collection of independent realizations from the Gompertz model.

Usage

panelGompertz(
  N = 100,
  U = 50,
  params = c(K = 1, r = 0.1, sigma = 0.1, tau = 0.1, X.0 = 1),
  seed = 12345678
)

Arguments

N

number of observations for each unit.

U

number of units.

params

parameter vector, assuming all units have the same parameters.

seed

passed to the random number generator for simulation.

Value

A panelPomp object.

Author(s)

Edward L. Ionides, Carles Bretó

References

Examples

panelGompertz()

Likelihood for a panel Gompertz model via a Kalman filter

Description

Evaluates the likelihood function for a panel Gompertz model, using a format convenient for maximization by optim() to obtain a maximum likelihood estimate. Specifically, estimated and fixed parameters are supplied by two different arguments.

Usage

panelGompertzLikelihood(x, panelPompObject, params)

Arguments

x

named vector for a subset of parameters, corresponding to those being estimated.

panelPompObject

a panel Gompertz model.

params

named vector containing all the parameters of the panel Gompertz model. Estimated parameters are overwritten by x.

Value

A numeric value.

Author(s)

Edward L. Ionides

Examples

pg <- panelGompertz(N=2,U=2)
panelGompertzLikelihood(coef(pg),pg,coef(pg))

Make a panelPomp model using UK measles data.

Description

The model is a modified panelPomp version of the model of He et al. 2010. The model is a stochastic SEIR model that accounts for population demographics in the form of births and deaths. Because of the increased transmission that results from school-aged children entering the susceptible pool once they begin attending classes for the first time, the model includes a birth-cohort effect, which moves a specified faction of the cohort into the susceptible pool all at once. The model also includes a seasonality in transmission rate that is larger during school terms thn it is during holidays.

Usage

panelMeasles(
  units = c("Bedwellty", "Birmingham", "Bradford", "Bristol", "Cardiff", "Consett",
    "Dalton.in.Furness", "Halesworth", "Hastings", "Hull", "Leeds", "Lees", "Liverpool",
    "London", "Manchester", "Mold", "Northwich", "Nottingham", "Oswestry", "Sheffield"),
  starting_pparams = NULL,
  interp_method = c("shifted_splines", "linear"),
  first_year = 1950,
  last_year = 1963,
  dt = 1/365.25
)

Arguments

units

Character vector of units in uk_measles to be used in the panel model.

starting_pparams

Parameters in the list format, having shared and specific components. Set to NULL to assign NA values.

interp_method

Method used to interpolate population and births. Possible options are "shifted_splines" and "linear".

first_year

Integer for the first full year of data desired.

last_year

Integer for the last full year of data desired.

dt

Size of the time step.

Value

A panelPomp object.

References

D. He, E.L. Ionides, and A.A. King. Plug-and-play inference for disease dynamics: measles in large and small populations as a case study. Journal of the Royal Society Interface 7, 271–283, 2010.

Examples

panelMeasles(units = "London")

Constructing `panelPomp` objects

Description

This function constructs panelPomp objects, representing PanelPOMP models (as defined in Bretó et al., 2020). PanelPOMP models involve multiple units, each of which can in turn be modeled by a POMP model. Such POMP models can be encoded as a list of pomp objects, a cornerstone that the panelPomp function can use to construct the corresponding panelPomp object.

Usage

panelPomp(object, shared, specific, params)

Arguments

object

required; either (i) a list of pomp objects; or (ii) an object of class panelPomp or inheriting class panelPomp.

If object is a list of pomps, the list must be named. All these pomps must either have no parameters or have the same parameter names. (This is just a format requirement. pomp codes can ignore any parameter that is irrelevant to any given panel unit.)

If object is a panelPomp object, the function allows modifying the shared and unit-specific configuration of object.

shared, specific

optional; these arguments depend on the type of object.

If object is a list of pomps, shared must be a numeric vector specifying parameter values shared among panel units. specific must be a matrix with parameter values that are unit-specific with rows naming parameters and columns naming units (these names must match those of object). If no values are specified and object has parameter values, these are set to be all unit-specific.

If object is a panelPomp object, these arguments can still be used as described above to modify the parameters of object. Alternatively, the parameter configuration of object can be modified providing only a character shared naming parameters of object that should be shared (with values for parameters not originally shared taken from the unit-specific parameters of the first panel unit of object). shared=NULL sets all parameters as unit-specific.

params

optional; a named numeric vector. In this case, the nature of parameters is determined via a naming convention: names ending in “[unit_name]” are assumed to denote unit-specific parameters; all other names specify shared parameters.

Value

A panelPomp object.

Author(s)

Carles Bretó

References

Examples

## recreate the 'panelRandomWalk()' example
prw <- panelRandomWalk()
prw2 <- panelPomp(unit_objects(prw), params = coef(prw))
identical(prw, prw2) # TRUE

Manipulating `panelPomp` objects

Description

Tools for manipulating panelPomp objects.

Usage

## S4 method for signature 'panelPomp'
coef(object, format = c("vector", "list"))

## S4 replacement method for signature 'panelPomp'
coef(object, ...) <- value

## S4 method for signature 'panelPomp'
length(x)

## S4 method for signature 'panelPomp'
names(x)

toParamList(value)

## S4 method for signature 'panelPomp'
print(x, ...)

## S4 method for signature 'panelPomp'
show(object)

## S4 method for signature 'panelPomp'
unit_objects(object)

## S4 method for signature 'panelPomp'
window(x, start, end)

## S4 method for signature 'panelPomp'
x[i]

## S4 method for signature 'panelPomp'
x[[i]]

## S4 method for signature 'panelPomp'
specific(object, ..., format = c("matrix", "vector"))

## S4 replacement method for signature 'panelPomp'
specific(object) <- value

## S4 method for signature 'panelPomp'
shared(object)

## S4 replacement method for signature 'panelPomp'
shared(object) <- value

Arguments

object, x

An object of class panelPomp or inheriting class panelPomp.

format

the format (data type) of the return value.

...

....

value

value to be assigned.

start, end

position in original times(pomp) at which to start.

i

unit index (indices) or name (names).

Value

coef() returns a numeric vector.

length() returns an integer.

names() returns a character vector.

toParamList() returns a list with the model parameters in list form.

When given objects of class panelPomp, unit_objects() returns a list of pomp objects.

window() returns a panelPomp object with adjusted times.

`[` returns a panelPomp object.

`[[` returns a pomp object.

specific() returns unit-specific parameters as a numeric matrix or vector

shared() returns shared parameters from a panelPomp object

Methods

coef: Extracts coefficients of panelPomp objects.
coef<-: Assign coefficients to panelPomp objects.
length: Count the number of units in panelPomp objects.
names: Get the unit names of panelPomp objects.
toParamList: Converts panel coefficients from vector form to list form.
window: Subset panelPomp objects by changing start time and end time.
[]: Take a subset of units.
[[]]: Select the pomp object for a single unit.
specific: Extracts the specific coefficients.
specific<-: Assigns the specific coefficients.
shared: Extracts the shared coefficients.
shared<-: Assigns the shared coefficients.

Author(s)

Carles Bretó, Aaron A. King, Edward L. Ionides, Jesse Wheeler

Examples

## access and manipulate model parameters and other features
prw <- panelRandomWalk()
coef(prw)
# replace coefficients
coef(prw) <- c(sigmaX=2,coef(prw)[-1])
coef(prw)
length(prw)
names(prw)
# convert vector-form parameters to list-form parameters
toParamList(coef(prw))
## summaries of objects
print(prw)
show(prw)
## access underlying pomp objects
unit_objects(prw)
## select windows of time
window(prw,start=2,end=4)
## subsetting panelPomp objects
prw[1] # panelPomp of 1 unit (first unit of prw)
prw[[2]] # pomp object corresponding to unit 2 of prw
# access and manipulate model parameters and other features
specific(prw)
# replace unit-specific coefficients
specific(prw) <- c("sigmaX[rw1]"=2)
specific(prw)
# access and manipulate model parameters and other features
shared(prw)
# replace unit-specific coefficients
shared(prw) <- c('sigmaY'=2)
shared(prw)

Panel random walk model

Description

Builds a collection of independent realizations from a random walk model.

Usage

panelRandomWalk(
  N = 5,
  U = 2,
  params = c(sigmaY = 1, sigmaX = 1, X.0 = 1),
  seed = 3141592
)

Arguments

N

number of observations for each unit.

U

number of units.

params

parameter vector, assuming all units have the same parameters.

seed

passed to the random number generator for simulation.

Value

A panelPomp object.

Author(s)

Edward L. Ionides, Carles Bretó

Examples

panelRandomWalk()

Handling of loglikelihood replicates

Description

Handling of loglikelihood replicates.

Usage

## S4 method for signature 'matrix'
logLik(object, repMargin, first = "aver", aver = "logmeanexp", se = FALSE)

Arguments

object

Matrix with the same number of replicated estimates for each panel unit loglikelihood.

repMargin

The margin of the matrix having the replicates (1 for rows, 2 for columns).

first

Wether to "aver"(age replicates) or "aggr"(egate units) before performing the other action.

aver

How to average: 'logmeanexp' to average on the likelihood scale before taking logs or 'mean' to average after taking logs (in which case, which action is performed first does not change the result).

se

logical; whether to give standard errors.

Details

When se = TRUE, the jackknife se's from pomp::logmeanexp are squared, summed and the squared root is taken.

Value

numeric vector with the average panel log likelihood and, when se = TRUE, the corresponding standard error.

Author(s)

Carles Bretó

Examples

ulls <- matrix(c(1,1.1,10.1,10),nr=2)
# when combining log likelihood estimates, the order in which aggregation and
# averaging are done can make a difference: panel_logmeanexp() implements the best
logLik(ulls,repMargin=1,first="aver",aver="logmeanexp")
logLik(ulls,repMargin=1,first="aggr",aver="mean",se=TRUE)

Log-mean-exp for panels

Description

This function computes the logmeanexp for each column or row of a numeric matrix and sums the result. Because the loglikelihood of a panelPomp object is the sum of the loglikelihoods of its units, this function can be used to summarize replicated estimates of the panelPomp model likelihood. If se = TRUE, the jackknife SE estimates from logmeanexp are squared, summed and the squared root is taken.

Usage

panel_logmeanexp(x, MARGIN, se = FALSE)

Arguments

x

Matrix with the same number of replicated estimates for each panel unit loglikelihood.

MARGIN

The dimension of the matrix that corresponds to a panel unit and over which averaging occurs (1 indicates rows, 2 indicates columns).

se

logical; whether to give standard errors.

Value

A numeric value with the average panel log likelihood or, when se = TRUE, a numeric vector adding the corresponding standard error.

Author(s)

Carles Bretó

Examples

ulls <- matrix(c(1,1,10,10),nr=2)
panel_logmeanexp(ulls,MARGIN=2,se=TRUE)

Manipulating `panelPomp` object parameter formats

Description

These facilitate keeping a record of evaluated log likelihoods.

Usage

toParamVec(pParams)

toMatrixPparams(listPparams)

Arguments

pParams

A list with both shared (vector) and unit-specific (matrix) parameters.

listPparams

PanelPomp parameters in list format

Value

toParamVec() returns model parameters in vector form. This function is the inverse of toParamList

toMatrixPparams() returns an object of class matrix with the model parameters in matrix form.

Author(s)

Carles Bretó

Examples

prw <- panelRandomWalk()
toParamVec(coef(prw, format = 'list'))
toMatrixPparams(coef(prw, format = 'list'))

Particle filtering for panel data

Description

Tools for applying particle filtering algorithms to panel data.

Usage

## S4 method for signature 'panelPomp'
pfilter(
  data,
  shared,
  specific,
  params,
  Np,
  verbose = getOption("verbose"),
  ...
)

## S4 method for signature 'pfilterd.ppomp'
logLik(object, ...)

## S4 method for signature 'pfilterd.ppomp'
unitLogLik(object, ...)

Arguments

data

An object of class panelPomp or inheriting class panelPomp.

shared, specific

optional; these arguments depend on the type of object.

params

Np

length(time(object,t0=TRUE))

verbose

logical; if TRUE, diagnostic messages will be printed to the console.

...

additional arguments, passed to the pfilter method of pomp.

object

required; either (i) a list of pomp objects; or (ii) an object of class panelPomp or inheriting class panelPomp.

If object is a panelPomp object, the function allows modifying the shared and unit-specific configuration of object.

Value

pfilter() returns an object of class pfilterd.ppomp that is also a panelPomp object (with the additional filtering details).

When applied to an object of class pfilterd.ppomp, logLik() returns a numeric value.

When given objects of class pfilterd.ppomp, unitLoglik() returns a numeric vector.

Methods

logLik: Extracts the estimated log likelihood for the entire panel.
unitLogLik: Extracts the estimated log likelihood for each panel unit.

Author(s)

Carles Bretó

References

Arulampalam, M. S., Maskell, S., Gordon, N. and Clapp, T. (2002) A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Trans. Sig. Proc., 50(2), 174–188. doi:10.1109/78.978374

Examples

# filter, which generates log likelihoods
pfrw <- pfilter(panelRandomWalk(),Np=10)
class(pfrw) # "pfilterd.ppomp"
is(pfrw,"panelPomp") # TRUE
pfrw
# extract single log likelihood for the entire panel
logLik(pfrw)
# extract log likelihood for each panel unit
unitLogLik(pfrw)

panelPomp plotting facilities

Description

Diagnostic plots for each unit in a panelPomp

Usage

## S4 method for signature 'panelPomp_plottable'
plot(
  x,
  variables,
  panel = lines,
  nc = NULL,
  yax.flip = FALSE,
  mar = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1),
  oma = c(6, 0, 5, 0),
  axes = TRUE,
  units = NULL,
  ...
)

Arguments

x

the object to plot

variables

optional character; names of variables to be displayed

panel

function of prototype panel(x, col, bg, pch, type, ...) which gives the action to be carried out in each panel of the display.

nc

the number of columns to use. Defaults to 1 for up to 4 series, otherwise to 2.

yax.flip

logical; if TRUE, the y-axis (ticks and numbering) should flip from side 2 (left) to 4 (right) from series to series.

mar, oma

the par mar and oma settings. Modify with care!

axes

logical; indicates if x- and y- axes should be drawn

units

The units of the PanelPOMP to be included in plots. The default of NULL will build a plot for all units in the object.

...

ignored or passed to low-level plotting functions

Value

No return value (the function returns NULL).

Author(s)

Edward L. Ionides

Examples

plot(panelRandomWalk())

Extract shared parameters from a panelPomp object

Description

This function is used to extract the shared parameters from a panelPomp object.

Usage

shared(object, ...)

Arguments

object

an object that contains shared parameters.

...

additional arguments.

Value

vector containing the shared parameters

Author(s)

Jesse Wheeler

Examples

prw <- panelRandomWalk()
# extract parameters in list form
shared(prw)

Set shared parameters of a panelPomp object

Description

This function is used to set the shared parameters of a panel pomp object.

Usage

shared(object) <- value

Arguments

object

an object that contains shared parameters.

value

a named numeric vector containing the desired values of the shared parameter vector.

Author(s)

Jesse Wheeler

Examples

prw <- panelRandomWalk()
# set parameters in list form
shared(prw) <- c("sigmaX" = 1, "sigmaY" = 2)

Simulations of a panel of partially observed Markov process

Description

simulate generates simulations of the state and measurement processes.

Usage

## S4 method for signature 'panelPomp'
simulate(object, nsim = 1, shared, specific)

Arguments

object

a panelPomp object.

nsim

The number of simulations to perform. Unlike the pomp simulate method, all simulations share the same parameters.

shared

Named vector of the shared parameters.

specific

Matrix of unit-specific parameters, with a column for each unit.

Value

A single panelPomp object (if nsim=1) or a list of panelPomp objects (if nsim>1).

Author(s)

Edward L. Ionides

Examples

simulate(panelRandomWalk())

Extract unit-specific parameters from a panelPomp object

Description

This function is used to extract the unit-specific parameters from a panel pomp object.

Usage

specific(object, ..., format = c("matrix", "vector"))

Arguments

object

an object that contains unit-specific parameters

...

additional arguments.

format

character representing how the parameters should be returned

Value

a matrix or vector containing the unit-specific parameters

Author(s)

Jesse Wheeler

Examples

prw <- panelRandomWalk()
# extract parameters in list form
specific(prw)

Set unit-specific parameters of a panelPomp object

Description

This function is used to set the unit-specific parameters of a panel pomp object.

Usage

specific(object) <- value

Arguments

object

an object that contains unit-specific parameters.

value

a numeric matrix with column names matching the names of the unit_objects slot, and row names matching the names of the unit-specific parameters. Alternatively, this can be a named vector following the naming convention ⁠<parameter>[<unit_name>]⁠.

Author(s)

Jesse Wheeler

Examples

# set parameters in list form
prw <- panelRandomWalk(U = 4)
new_pars <- matrix(c(1, 1, 3, 2), nrow = 1)
dimnames(new_pars) <- list(param = "X.0", unit = c("rw1", "rw2", "rw3", "rw4"))
specific(prw) <- new_pars

He et al. 2010 twenty UK cities weekly reported measles data

Description

He et al. 2010 twenty UK cities weekly reported measles data

Usage

twentycities

Format

`twentycities`

A list of 3 data frames.

`twentycities$measles`:

unit: City name
data: Date of observation
cases: Number of measles cases reported during the week

`twentycities$demog`:

unit: City name
year: Year that demography was recorded
pop: Population
births: Births

`twentycities$coord`:

unit: City name
long: Longitude of city
lat: Latitude of city

Source

https://kingaa.github.io/pomp/vignettes/twentycities.rda

References

D. He, E.L. Ionides, and A.A. King. Plug-and-play inference for disease dynamics: measles in large and small populations as a case study. Journal of the Royal Society Interface 7, 271–283, 2010.

Weekly reported measles data for 362 locations in the UK

Description

Weekly reported measles data for 362 locations in the UK

Usage

uk_measles

Format

`uk_measles`

A list of 3 data frames Unit names ending in .RD are for rural areas; other unit names are for urban areas. NOTE: not all units have coordinates.

`uk_measles$measles`:

unit: City name
data: Date of observation
cases: Number of measles cases reported during the week

`uk_measles$demog`:

unit: City name
year: Year that demography was recorded
pop: Population
births: Births

`uk_measles$coord`:

unit: City name
long: Longitude of city
lat: Latitude of city

Source

https://rs.figshare.com/collections/Supplementary_material_from_Structure_space_and_size_competing_drivers_of_variation_in_urban_and_rural_measles_transmission_/5036567/1

References

Korevaar H, Metcalf CJ, Grenfell BT. 2020 Structure, space and size: competing drivers of variation in urban and rural measles transmission. J. R. Soc. Interface 17: 20200010. http://dx.doi.org/10.1098/rsif.2020.0010

Extract log likelihood of units of panel models

Description

unitLogLik() is a generic function that extracts the log likelihood for each unit of panel objects returned by panel modeling functions. While the numeric value with the log likelihood for the entire panel is useful and possible via S4 methods logLik(), the contributions to it by panel units can be implemented via unitLogLik().

Usage

unitLogLik(object, ...)

Arguments

object

an object for which log likelihood values for units can be extracted.

...

additional arguments.

Details

This is a generic function: methods can be defined for it.

Value

Log likelihood extracted for each unit of the panel model object.

When given objects of class pfilterd.ppomp, unitLoglik() returns a numeric vector.

Author(s)

Carles Bretó

Examples

# filter, which generates log likelihoods
pfrw <- pfilter(panelRandomWalk(),Np=10)
# extract log likelihood for each panel unit
unitLogLik(pfrw)

Extract units of a panel model

Description

unit_objects() is a generic function that extracts a list of objects corresponding to units of panel objects returned by panel modeling functions.

Usage

unit_objects(object, ...)

Arguments

object

an object for which extraction of panel units is meaningful.

...

additional arguments.

Details

This is a generic function: methods can be defined for it.

Value

Units extracted from the panel model object.

When given objects of class panelPomp, unit_objects() returns a list of pomp objects.

Author(s)

Carles Bretó

Examples

prw <- panelRandomWalk()
## access underlying pomp objects
unit_objects(prw)

Interpret shortcuts for `sQuote()`s and `dQuote()`s in character objects

Description

Concatenate character objects and replace double quotes with sQuote() (write ''x'' instead of dQuote("x")) and replace asterisks with dQuote().

Usage

wQuotes(...)

Arguments

...

objects to be passed to strsplit.

Value

A character object.

Author(s)

Carles Bretó

Examples

paste0("in ",sQuote("fn_name"),": ",dQuote("object")," is 'a' required argument")
wQuotes("in ''fn_name'': *object* is 'a' required argument") # same but shorter

Inference for PanelPOMPs (Panel Partially Observed Markov Processes)

Description

Data analysis using panelPomp

Extending the pomp platform for developing inference tools

Comments, bug reports, and requests

Documentation

License

Author(s)

References

See Also

Internal function for modifying pparamArray in Mif2

Description

Usage

Arguments

Internal function for modifying pparamArray in Mif2

Description

Usage

Arguments

Coercing panelPomp objects as list, pompList or data.frame

Description

Value

Author(s)

See Also

Modifying parameters of filtered objects

Description

Usage

Arguments

Contacts model

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Get single column or row without dropping names

Description

Usage

Arguments

Value

Author(s)

Examples

PIF: Panel iterated filtering

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

#' Create design matrix for panelPomp calculations

Description

Usage

Arguments

Value

Author(s)

Examples

Panel Gompertz model

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Likelihood for a panel Gompertz model via a Kalman filter

Description

Usage

Arguments

Value

Author(s)

Examples

Make a panelPomp model using UK measles data.

Description

Usage

Arguments

Value

Coercing `panelPomp` objects as `list`, `pompList` or `data.frame`

Constructing `panelPomp` objects

Manipulating `panelPomp` objects

Manipulating `panelPomp` object parameter formats