likelihoods.fit: Computing Likelihoods for Occupancy Detection Models

In sustainablefarms/msod: Multispecies Occupancy Detection Models

Computing Likelihoods for Occupancy Detection Models

Description

Computing Likelihoods for Occupancy Detection Models

Usage

lppd_newdata(fit, Xocc, yXobs, ModelSite, chains = 1, ...)

likelihood(fit, ...)

Arguments

chains	is a vector indicator which mcmc chains to extract draws from. If NULL then all chains used.
numlvsims	the number of simulated latent variable values to use for computing likelihoods
cl	a cluster created by parallel::makeCluster()

Details

Any predictinve accuracy measure requires a choice of

1. the part of the model that is considered the 'likelihood' and

2. factorisation of the likelihood into 'data points' Vehtari 2017

On 1: New data will look like a new location or visit for a new season in our exisitng region, and observing only the species included in the model. This means we have zero knowledge of the latent variable value at the new ModelSite. This means likelihood: * conditional on the covariates occ.b and det.b (not using the fitted values of mu.occ.b, tau.occ.b etc) * is conditional on the lv.b values of each species * is conditional on the latent variable value for (each) new ModelSite being drawn from a standard Gaussian distribution.

On 2: Factoring the likelihood using the inbuilt independence properties of the model means a single 'data point' is all the data for all visits of a single ModelSite. The likelihood could also be partitioned by each visit, but then data points are dependent (they have the same occupancy value).

The output of likelihoods.fit() can be easily passed to loo::waic() and loo::loo().

Value

lppd.newdata returns a list with components lpds: a list of the log predictive density for each ModelSite in the supplied data (for each model site this is the average of the predictive density conditioned on each draw from the posterior). lppd: the computed log pointwise predictive density (sum of the lpds). This is equation (5) in Gelman et al 2014

Returns a matrix. Each row corresponds to a draw of the parameters from the posterior. Each column to a ModelSite. The value in each cell is the probability density, given the parameters from the draw, evaluated at the observations for the model site.

Functions

• lppd_newdata: Compute the log pointwise posterior density of new (out-of-sample) data

• likelihood: Compute the likelihood of observations at each ModelSite. At data in the fitted model, or on new data supplied.

References

A. Vehtari, A. Gelman, and J. Gabry, "Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC," Stat Comput, vol. 27, pp. 1413-1432, Sep. 2017, doi: 10.1007/s11222-016-9696-4.

Examples

# simulate data
covars <- artificial_covar_data(nsites = 50, nvisitspersite = 2)
y <- simulate_iid_detections(3, nrow(covars$Xocc))

fittedmodel <- run.detectionoccupancy(
  Xocc = covars$Xocc,
  yXobs = cbind(covars$Xobs, y),
  species = colnames(y),
  ModelSite = "ModelSite",
  OccFmla = "~ UpSite + Sine1",
  ObsFmla = "~ UpVisit + Step",
  nlv = 2,
  MCMCparams = list(n.chains = 1, adapt = 0, burnin = 0, sample = 3, thin = 1)
)

# run likelihood computations, waic, and psis-loo
insamplell <- likelihoods.fit(fittedmodel)
waic <- loo::waic(log(insamplell))
looest <- loo::loo(log(insamplell), cores = 2)



outofsample_covars <- artificial_covar_data(nsites = 10, nvisitspersite = 2)
outofsample_y <- simulate_iid_detections(3, nrow(outofsample_covars$Xocc))
outofsample_lppd <- lppd.newdata(fittedmodel,
             Xocc = outofsample_covars$Xocc,
             yXobs = cbind(outofsample_covars$Xobs, outofsample_y),
             ModelSite = "ModelSite")

# Recommend using multiple cores:
cl <- parallel::makeCluster(2)
insamplell <- likelihoods.fit(fittedmodel, cl = cl)

outofsample_lppd <- lppd.newdata(fittedmodel,
                                 Xocc = outofsample_covars$Xocc,
                                 yXobs = cbind(outofsample_covars$Xobs, outofsample_y),
                                 ModelSite = "ModelSite",
                                 cl = cl)
parallel::stopCluster(cl)
model2lv <- readRDS("../Experiments/7_4_modelrefinement/fittedmodels/7_4_13_model_2lv_e13.rds")
model2lv_new <- translatefit(model2lv)
lkl <- likelihood(model2lv_new, nlvsim = 2)

sustainablefarms/msod documentation built on March 6, 2023, 7:17 a.m.