Implementing a POMP model from scratch

Introduction
A stochastic Ricker model
Implementing the Ricker model in pomp
Working with the Ricker model in pomp.
Exercises
References

The R codes for this document are provided as a script.

This document has its origins in the Getting Started vignette.

Introduction

The R package pomp provides facilities for modeling POMPs, a toolbox of statistical inference methods for analyzing data using POMPs, and a development platform for implmenting new POMP inference methods. The basic data-structure provided by pomp is the object of class pomp, alternatively known as a pomp object. It is a container that holds real or simulated data and a POMP model, possibly together with other information such as model parameters, that may be needed to do things with the model and data. A real pomp data analysis begins with constructing one or more pomp objects to hold the data and the model or models under consideration. Here, we’ll illustrate this process a dataset of Parus major abundance in Wytham Wood, near Oxford (McCleery and Perrins 1991).

Download and plot the data:

loc <- url("https://kingaa.github.io/sbied/intro/parus.csv")
dat <- read.csv(loc)
head(dat)

plot(pop~year,data=dat,type='o')

A stochastic Ricker model

Let’s suppose we wish to investigate the extent to which the Ricker model (Ricker 1954) explains these data. First, let’s recall the details of the Ricker (1954) model.

The deterministic Ricker map

The Ricker map describes the deterministic dynamics of a simple population: \[N_{t+1} = r\,N_{t}\,\exp(-c\,N_{t}).\] Here, \(N_t\) is the population density at time \(t\) and \(r\) is a fixed value, related to the population’s intrinsic capacity to increase. \(N\) is a state variable, \(r\) and \(c\) are parameters. If we know \(r\) and the initial condition \(N_0\), the deterministic Ricker equation predicts the future population density at all times \(t=1,2,\dots\). We can view the initial condition, \(N_0\) as a special kind of parameter, an initial-value parameter.

Process noise

We can model process noise in this system by making the growth rate \(r\) into a random variable. For example, if we assume that the intrinsic growth rate is log-normally distributed, \(N\) becomes a stochastic process governed by \[N_{t+1} = r\,N_{t}\,\exp(-c\,N_{t}+\varepsilon_{t}), \qquad \varepsilon_{t}\;\sim\;\mathrm{Normal}\left(0,\sigma\right),\] where the new parameter \(\sigma\) is the standard deviation of the noise process \(\varepsilon\).

Measurement error

Let’s suppose that the Ricker model is our model for the dynamics of a real population. However, we cannot know the exact population density at any time, but only estimate it through sampling.

Let’s model measurement error by assuming the measurements, \(y_t\), are Poisson with mean \(\phi\,N_t\): \[y_{t}\;\sim\;\mathrm{Poisson}\left(\phi\,N_{t}\right)\]

In this equation,

\(N_t\) is the true population density at time \(t\),
\(y_t\) is the number of individuals sampled at time \(t\),
the choice of units for \(N\) is peculiar and depends on the parameters (e.g., \(N=\log(r)/c\) is the equilibrium of the deterministic model),
the parameter \(\phi\) is proportional to our sampling effort, and also has peculiar units.

Implementing the Ricker model in pomp

The call to construct a pomp object is, naturally enough, pomp. Documentation on this function can be had by doing ?pomp. Learn about the various things you can do once you have a pomp object by doing methods?pomp and following the links therein. Read an overview of the package as a whole with links to its main features by doing package?pomp. A complete index of the functions in pomp is returned by the command library(help=pomp). Finally, the home page for the pomp project is https://kingaa.github.io/pomp/; there you have access to the complete source code, tutorials, manuals, a news blog, a facility for reporting issues and making help requests, etc.

Now, to construct our pomp object:

library(pomp)
parus <- pomp(dat,times="year",t0=1959)

The times argument specifies that the column of dat labelled “year” gives the measurement times; t0 is the “zero-time”, the time at which the state process will be initialized. We’ve set it to one year prior to the beginning of the data. Plot the new pomp object:

plot(parus)

Adding in the process model simulator

We can add the stochastic Ricker model to parus by writing a Csnippet that simulates one realization of the stochastic process, from an arbitary time \(t\) to \(t+1\), given arbitrary states and parameters. We provide this to pomp in the form of a Csnippet, a little snippet of C code that performs the computation. The following does this.

stochStep <- Csnippet("
  e = rnorm(0,sigma);
  N = r*N*exp(-c*N+e);
")
pomp(parus,rprocess=discrete_time(step.fun=stochStep,delta.t=1),
     paramnames=c("r","c","sigma"),statenames=c("N","e")) -> parus

Note that in the above, we use the exp and rnorm functions from the R API. In general any C function provided by R is available to you. pomp also provides a number of C functions that are documented in the header file, pomp.h, that is installed with the package. See the Csnippet documentation (?Csnippet) to read more about how to write them. Note too that we use discrete.time.sim here because the model is a stochastic map. We specify that the time step of the discrete-time process is delta.t, here, 1 yr.

At this point, we have what we need to simulate the state process:

sim <- simulate(parus,params=c(N_0=1,e_0=0,r=12,c=1,sigma=0.5),
                format="data.frame")
plot(N~year,data=sim,type='o')

Adding in the measurement model and parameters

We complete the specification of the POMP by specifying the measurement model. To obtain the Poisson measurement model described above, we write two Csnippets. The first simulates:

rmeas <- Csnippet("pop = rpois(phi*N);")

The second computes the likelihood of observing pop birds given a true density of N:

dmeas <- Csnippet("lik = dpois(pop,phi*N,give_log);")

[Note the give_log argument. When this code is evaluated, give_log will be set to 1 if the log likelihood is desired, and 0 else.] We add these into the pomp object:

pomp(parus,rmeasure=rmeas,dmeasure=dmeas,statenames=c("N"),paramnames=c("phi")) -> parus

Now we can simulate the whole POMP. First, let’s add some parameters:

coef(parus) <- c(N_0=1,e_0=0,r=20,c=1,sigma=0.1,phi=200)

library(ggplot2)
sims <- simulate(parus,nsim=3,format="d",include.data=TRUE)
ggplot(data=sims,mapping=aes(x=year,y=pop))+geom_line()+
  facet_wrap(~.id,ncol=1,scales="free_y")

Adding in the deterministic skeleton

We can add the Ricker model deterministic skeleton to the parus pomp object. Since the Ricker model is a discrete-time model, its skeleton is a map that takes \(N_t\) to \(N_{t+1}\) according to the Ricker model equation \[N_{t+1} = r\,N_{t}\,\exp(-c\,N_{t}).\] The following implements this.

skel <- Csnippet("DN = r*N*exp(-c*N);")

We then add this to the pomp object:

parus <- pomp(parus,skeleton=map(skel),paramnames=c("r","c"),statenames=c("N"))

Note that we have to inform pomp as to which of the variables we’ve referred to in skel is a state variable (statenames) and which is a parameter (paramnames). In writing a Csnippet for the deterministic skeleton, we use D to designate the map’s value. The map call tells pomp that the skeleton is a discrete-time dynamical system (a map) rather than a continuous-time system (a vectorfield).

With just the skeleton defined, we are in a position to compute the trajectories of the deterministic skeleton at any point in parameter space. For example, here we compute the trajectory and superimpose it on a plot of one simulation:

traj <- trajectory(parus,params=c(N_0=1,r=12,c=1),format="data.frame")
plot(N~year,data=sim,type='o')
lines(N~year,data=traj,type='l',col='red')

Working with the Ricker model in pomp.

Let’s see what can be done with a pomp object.

We can plot the data by doing

plot(parus)

We can simulate by doing

x <- simulate(parus)

What kind of object have we created with this call to simulate?

class(x)

[1] "pomp"
attr(,"package")
[1] "pomp"

plot(x)

Why do we see more time series in the simulated pomp object?

We can turn a pomp object into a data frame:

y <- as.data.frame(parus)
head(y)

head(simulate(parus,format="data.frame"))

 year .id          N            e  pop
 1960   1 8.69198241  0.166668765 1763
 1961   1 0.02908044 -0.003840424    5
 1962   1 0.61948706  0.092174215  120
 1963   1 7.02941443  0.052721660 1413
 1964   1 0.12235442 -0.017254626   28
 1965   1 1.86835232 -0.147487548  395

We can also run multiple simulations simultaneously:

x <- simulate(parus,nsim=10)
class(x)

[1] "pompList"
attr(,"package")
[1] "pomp"

sapply(x,class)

 [1] "pomp" "pomp" "pomp" "pomp" "pomp" "pomp" "pomp" "pomp"
 [9] "pomp" "pomp"

x <- simulate(parus,nsim=10,format="data.frame")
head(x)

 year .id        N           e  pop
 1960   1 6.946406 -0.05750784 1343
 1960   2 6.522694 -0.12044483 1243
 1960   3 8.594441  0.15538332 1691
 1960   4 6.946813 -0.05744933 1327
 1960   5 7.924353  0.07420837 1578
 1960   6 6.930349 -0.05982213 1391

str(x)

'data.frame':   270 obs. of  5 variables:
 $ year: num  1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 ...
 $ .id : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 1 2 3 4 5 6 7 8 9 10 ...
 $ N   : num  6.95 6.52 8.59 6.95 7.92 ...
 $ e   : num  -0.0575 -0.1204 0.1554 -0.0574 0.0742 ...
 $ pop : num  1343 1243 1691 1327 1578 ...

Also,

library(ggplot2)
x <- simulate(parus,nsim=9,format="data.frame",include.data=TRUE)
ggplot(data=x,aes(x=year,y=pop,group=.id,color=(.id=="data")))+
  geom_line()+guides(color="none")+
  facet_wrap(~.id,ncol=2)

We refer to the deterministic map as the “skeleton” of the stochastic process. We can compute a trajectory of the the deterministic skeleton using trajectory:

y <- trajectory(parus,format="data.frame")
plot(N~year,data=y,type="l")

Notice that parus has parameters associated with it:

coef(parus)

  N_0   e_0     r     c sigma   phi 
  1.0   0.0  20.0   1.0   0.1 200.0

These are the parameters at which the simulations and deterministic trajectory computations above were done. We can run these at different parameters:

theta <- coef(parus)
theta[c("r","N_0")] <- c(5,3)
y <- trajectory(parus,params=theta)
plot(N~year,data=as.data.frame(y),type='l')

x <- simulate(parus,params=theta)
plot(x,var="pop")

We can also change the parameters stored inside of parus:

coef(parus,c("r","N_0","sigma")) <- c(39,0.5,1)
coef(parus)

  N_0   e_0     r     c sigma   phi 
  0.5   0.0  39.0   1.0   1.0 200.0

plot(simulate(parus),var="pop")

A note on terminology

If we know the state, \(x(t_0)\), of the system at time \(t_0\), it makes sense to speak about the entire trajectory of the system for all \(t>t_0\). This is true whether we are thinking of the system as deterministic or stochastic. Of course, in the former case, the trajectory is uniquely determined by \(x(t_0)\), while in the stochastic case, only the probability distribution of \(x(t)\), \(t>t_0\) is determined. To avoid confusion, we use the term “trajectory” exclusively to refer to trajectories of a deterministic process. Thus, the trajectory command iterates or integrates the deterministic skeleton forward in time, returning the unique trajectory determined by the specified parameters. When we want to speak about sample paths of a stochastic process, we use the term simulation. Accordingly, the simulate command always returns individual sample paths from the POMP. In particular, we avoid “simulating a set of differential equations”, preferring instead to speak of “integrating” the equations, or “computing trajectories”.

Exercises

Exercise: Ricker model parameters

Fiddle with the parameters to try and make the simulations look more like the data. This will help you build some intuition for what the various parameters do.

Exercise: Reformulating the Ricker model

One can reparameterize the Ricker model according to \[N_{t+1} = N_{t}\,e^{r\,\left(1-\frac{N_{t}}{K}\right)+\varepsilon_t}.\]

Modify the pomp object we created above to reflect this reparameterization.

Modify the measurement model so that \[\mathrm{pop}_t \sim \mathrm{Negbin}\left(\phi\,N_t,k\right),\] i.e., \(\mathrm{pop}_t\) is negative-binomially distributed with mean \(\phi\,N_t\) and clumping parameter \(k\). See ?NegBinomial for documentation on the negative binomial distribution and the R Extensions Manual section on distribution functions for information on how to access these in C.

Exercise: Beverton-Holt

Construct a pomp object for the Parus major data and the stochastic Beverton-Holt model \[N_{t+1} = \frac{a\,N_t}{1+b\,N_t}\,\varepsilon_t,\] where \(a\) and \(b\) are parameters and \[\varepsilon_t \sim \mathrm{Lognormal}\left(-\tfrac{1}{2}\sigma^2,\sigma\right).\] Assume the same measurement model as before.

References

McCleery RH, Perrins CM (1991). “Effects of Predation on the Numbers of Great Tits Parus Major.” In Bird population studies. Relevence to conservation and management 129–147. Oxford University Press, Oxford.

Ricker WE (1954). “Stock and Recruitment.” Journal of the Fisheries Research Board of Canada, 11, 559–623. https://doi.org/10.1139/f54-039.

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