,
Edward L. Ionides [aut]
, Aaron A. King [aut]
, Jesse Wheeler [aut,
cre] , Aaron Abkemeier
[ctb]| Title: | Inference for Panel Partially Observed Markov Processes |
|---|---|
| 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>. |
| Authors: | Carles Breto [aut] ,
Edward L. Ionides [aut]
, Aaron A. King [aut]
, Jesse Wheeler [aut,
cre] , Aaron Abkemeier
[ctb] |
| Maintainer: | Jesse Wheeler <[email protected]> |
| License: | GPL-3 |
| Version: | 1.5.0.0 |
| Built: | 2024-12-09 09:39:30 UTC |
| Source: | CRAN |
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).
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 1.5.0.0 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.
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.
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.
Examples are provided via the contacts(), panelGompertz()
and panelRandomWalk() functions.
panelPomp is provided under the GPL-3 License.
Maintainer: Jesse Wheeler [email protected] (ORCID)
Authors:
Carles Breto [email protected] (ORCID)
Edward L. Ionides (ORCID)
Aaron A. King (ORCID)
Other contributors:
Aaron Abkemeier [email protected] [contributor]
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
panelPomp objects as list, pompList or
data.frame
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.
An object of class matching that specified in the second argument (to=).
Carles Bretó
Other panelPomp methods:
panelPomp_methods
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.
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.
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.
## 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## 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
object |
|
... |
additional arguments. |
value |
New parameter value. This function does not allow users to set this value. |
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).
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) )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) )
params |
parameter vector. |
A panelPomp object.
Edward L. Ionides
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
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
Other panelPomp examples:
panelGompertz(),
panelMeasles(),
panelRandomWalk()
contacts()contacts()
Subset matrix dropping dimension but without dropping dimname
(as done by `[` by default).
get_col(matrix, rows, col) get_row(matrix, row, cols)get_col(matrix, rows, col) get_row(matrix, row, cols)
matrix |
matrix. |
rows |
numeric; rows to subset; like with |
col |
integer; single column to subset. |
row |
integer; single row to subset. |
cols |
numeric; columns to subset; like with |
A named vector object.
Carles Bretó
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 rownamem <- 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
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).
## 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, ...)## 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, ...)
data |
An object of 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 length(time(object,t0=TRUE)) or as a function taking a positive integer argument.
In the latter case, |
rw.sd |
An unevaluated expression of the form |
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.
|
block |
A logical variable determining whether to carry out block resampling of unit-specific parameters. |
verbose |
logical; if |
... |
.... |
object |
an object resulting from the application of IF2 (i.e., of
class |
pars |
names of parameters |
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.
Carles Bretó
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
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.
pomp's mif2 at mif2, panel_loglik
Other panelPomp workhorse functions:
panelPomp,
panel_loglik,
pfilter()
## 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)## 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)
Handling of loglikelihood replicates.
## S4 method for signature 'matrix' logLik(object, repMargin, first = "aver", aver = "logmeanexp", se = FALSE)## S4 method for signature 'matrix' logLik(object, repMargin, first = "aver", aver = "logmeanexp", se = FALSE)
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 |
How to average: |
se |
logical; whether to give standard errors. |
When se = TRUE, the jackknife se's from
pomp::logmeanexp are squared, summed and the squared root is taken.
numeric vector with the average panel log likelihood and, when
se = TRUE, the corresponding standard error.
Carles Bretó
Other panelPomp workhorse functions:
mif2(),
panelPomp,
pfilter()
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)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)
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.
panel_logmeanexp(x, MARGIN, se = FALSE)panel_logmeanexp(x, MARGIN, se = FALSE)
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. |
A numeric value with the average panel log likelihood or, when
se = TRUE, a numeric vector adding the corresponding standard error.
Carles Bretó
panel_loglik
ulls <- matrix(c(1,1,10,10),nr=2) panel_logmeanexp(ulls,MARGIN=2,se=TRUE)ulls <- matrix(c(1,1,10,10),nr=2) panel_logmeanexp(ulls,MARGIN=2,se=TRUE)
These functions are useful for generating design matrices for the exploration of parameter space.
runif_panel_design( lower = numeric(0), upper = numeric(0), nseq, specific_names, unit_names )runif_panel_design( lower = numeric(0), upper = numeric(0), nseq, specific_names, unit_names )
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 |
Character vector containing the names of the units of the
panel. If not used in conjunction with |
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.
Jesse Wheeler, Aaron A. King
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 )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 )
Builds a collection of independent realizations from the Gompertz model.
panelGompertz( N = 100, U = 50, params = c(K = 1, r = 0.1, sigma = 0.1, tau = 0.1, X.0 = 1), seed = 12345678 )panelGompertz( N = 100, U = 50, params = c(K = 1, r = 0.1, sigma = 0.1, tau = 0.1, X.0 = 1), seed = 12345678 )
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. |
A panelPomp object.
Edward L. Ionides, Carles Bretó
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
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.
Other panelPomp examples:
contacts(),
panelMeasles(),
panelRandomWalk()
panelGompertz()panelGompertz()
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.
panelGompertzLikelihood(x, panelPompObject, params)panelGompertzLikelihood(x, panelPompObject, params)
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. |
A numeric value.
Edward L. Ionides
pg <- panelGompertz(N=2,U=2) panelGompertzLikelihood(coef(pg),pg,coef(pg))pg <- panelGompertz(N=2,U=2) panelGompertzLikelihood(coef(pg),pg,coef(pg))
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.
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 )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 )
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 |
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. |
A panelPomp object.
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.
Other panelPomp examples:
contacts(),
panelGompertz(),
panelRandomWalk()
panelMeasles(units = "London")panelMeasles(units = "London")
panelPomp objectsThis 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.
panelPomp(object, shared, specific, params)panelPomp(object, shared, specific, params)
object |
required; either (i) a If If |
shared, specific
|
optional; these arguments depend on the type
of If If |
params |
optional; a named numeric vector. In this case, the nature of
parameters is determined via a naming convention: names ending in
“ |
A panelPomp object.
Carles Bretó
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
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.
pomp's constructor at pomp
Other panelPomp workhorse functions:
mif2(),
panel_loglik,
pfilter()
## recreate the 'panelRandomWalk()' example prw <- panelRandomWalk() prw2 <- panelPomp(unit_objects(prw), params = coef(prw)) identical(prw, prw2) # TRUE## recreate the 'panelRandomWalk()' example prw <- panelRandomWalk() prw2 <- panelPomp(unit_objects(prw), params = coef(prw)) identical(prw, prw2) # TRUE
panelPomp objectsTools for manipulating panelPomp objects.
## 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## 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
object, x
|
An object of class |
format |
the format (data type) of the return value. |
... |
.... |
value |
value to be assigned. |
start, end
|
position in original |
i |
unit index (indices) or name (names). |
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
Extracts coefficients of panelPomp objects.
Assign coefficients to panelPomp objects.
Count the number of units in panelPomp objects.
Get the unit names of panelPomp objects.
Converts panel coefficients from vector form to list form.
Subset panelPomp objects by changing start time and
end time.
[]Take a subset of units.
[[]]Select the pomp object for a single unit.
Extracts the specific coefficients.
Assigns the specific coefficients.
Extracts the shared coefficients.
Assigns the shared coefficients.
Carles Bretó, Aaron A. King, Edward L. Ionides, Jesse Wheeler
Other panelPomp methods:
as()
## 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)## 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)
Builds a collection of independent realizations from a random walk model.
panelRandomWalk( N = 5, U = 2, params = c(sigmaY = 1, sigmaX = 1, X.0 = 1), seed = 3141592 )panelRandomWalk( N = 5, U = 2, params = c(sigmaY = 1, sigmaX = 1, X.0 = 1), seed = 3141592 )
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. |
A panelPomp object.
Edward L. Ionides, Carles Bretó
Other panelPomp examples:
contacts(),
panelGompertz(),
panelMeasles()
panelRandomWalk()panelRandomWalk()
panelPomp object parameter formatsThese facilitate keeping a record of evaluated log likelihoods.
toParamVec(pParams) toMatrixPparams(listPparams)toParamVec(pParams) toMatrixPparams(listPparams)
pParams |
A list with both shared (vector) and unit-specific (matrix) parameters. |
listPparams |
PanelPomp parameters in list format |
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.
Carles Bretó
prw <- panelRandomWalk() toParamVec(coef(prw, format = 'list')) toMatrixPparams(coef(prw, format = 'list'))prw <- panelRandomWalk() toParamVec(coef(prw, format = 'list')) toMatrixPparams(coef(prw, format = 'list'))
Tools for applying particle filtering algorithms to panel data.
## 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, ...)## 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, ...)
data |
An object of class |
shared, specific
|
optional; these arguments depend on the type
of If If |
params |
optional; a named numeric vector. In this case, the nature of
parameters is determined via a naming convention: names ending in
“ |
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 length(time(object,t0=TRUE)) or as a function taking a positive integer argument.
In the latter case, |
verbose |
logical; if |
... |
additional arguments, passed to the |
object |
required; either (i) a If If |
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.
Extracts the estimated log likelihood for the entire panel.
Extracts the estimated log likelihood for each panel unit.
Carles Bretó
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
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
pomp's pfilter at pfilter, panel_loglik
Other panelPomp workhorse functions:
mif2(),
panelPomp,
panel_loglik
# 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)# 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)
Diagnostic plots for each unit in a panelPomp
## 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, ... )## 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, ... )
x |
the object to plot |
variables |
optional character; names of variables to be displayed |
panel |
function of prototype |
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 |
axes |
logical; indicates if x- and y- axes should be drawn |
... |
ignored or passed to low-level plotting functions |
No return value (the function returns NULL).
Edward L. Ionides
plot(panelRandomWalk())plot(panelRandomWalk())
simulate generates simulations of the state and measurement
processes.
## S4 method for signature 'panelPomp' simulate(object, nsim = 1, shared, specific)## S4 method for signature 'panelPomp' simulate(object, nsim = 1, shared, specific)
object |
a |
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. |
A single panelPomp object (if nsim=1) or a list of panelPomp objects
(if nsim>1).
Edward L. Ionides
simulate(panelRandomWalk())simulate(panelRandomWalk())
He et al. 2010 twenty UK cities weekly reported measles data
twentycitiestwentycities
twentycitiesA list of 3 data frames.
twentycities$measles:City name
Date of observation
Number of measles cases reported during the week
twentycities$demog:City name
Year that demography was recorded
Population
Births
twentycities$coord:City name
Longitude of city
Latitude of city
https://kingaa.github.io/pomp/vignettes/twentycities.rda
D. He, E.L. Ionides, and A.A. King. Plug-and-play inference for disease dynamics: measles in large and small populations as a case study. Journal of the Royal Society Interface 7, 271–283, 2010.
Weekly reported measles data for 362 locations in the UK
uk_measlesuk_measles
uk_measlesA list of 3 data frames Unit names ending in .RD are for rural areas; other
unit names are for urban areas. NOTE: not all units have coordinates.
uk_measles$measles:City name
Date of observation
Number of measles cases reported during the week
uk_measles$demog:City name
Year that demography was recorded
Population
Births
uk_measles$coord:City name
Longitude of city
Latitude of city
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
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## S4 method for signature 'pfilterd.ppomp' unitlogLik(object, ...)## S4 method for signature 'pfilterd.ppomp' unitlogLik(object, ...)
object |
an object for which log likelihood values for units can be extracted. |
... |
additional arguments. |
When given objects of class pfilterd.ppomp, unitloglik() returns a numeric vector.
# filter, which generates log likelihoods pfrw <- pfilter(panelRandomWalk(),Np=10) # extract log likelihood for each panel unit unitlogLik(pfrw)# filter, which generates log likelihoods pfrw <- pfilter(panelRandomWalk(),Np=10) # extract log likelihood for each panel unit unitlogLik(pfrw)