occuMulti {unmarked} | R Documentation

## Fit the Rota et al. (2016) Multi-species Occupancy Model

### Description

This function fits the multispecies occupancy model of Rota et al (2016).

### Usage

    occuMulti(detformulas, stateformulas, data, maxOrder, starts, method="BFGS",
        se=TRUE, engine=c("C","R"), silent=FALSE, ...)

### Arguments

`detformulas` | Character vector of formulas for the detection models, one per species.
`stateformulas` | Character vector of formulas for the natural parameters. To fix a natural parameter at 0, specify the corresponding formula as `"0"` or `"~0"`.
`data` | An `unmarkedFrameOccuMulti` object
`maxOrder` | Optional; specify maximum interaction order. Defaults to number of species (all possible interactions). Reducing this value may speed up optimization if you aren't interested in higher-order interactions.
`starts` | Vector of parameter starting values.
`method` | Optimization method used by `optim`.
`se` | Logical specifying whether or not to compute standard errors.
`engine` | Either "C" to use fast C++ code or "R" to use native R code during the optimization.
`silent` | Boolean; if `TRUE`, suppress warnings.
`...` | Additional arguments to optim, such as lower and upper bounds

### Details

See `unmarkedFrame` and `unmarkedFrameOccuMulti` for a description of how to supply data to the `data` argument.

`occuMulti` fits the multispecies occupancy model from Rota et al. (2016), for two or more interacting species. The model generalizes the standard single-species occupancy model from MacKenzie et al. (2002). The latent occupancy state at site i for a set of s potentially interacting species is a vector Z_i of length s containing a sequence of the values 0 or 1. For example, when s = 2, the possible states are [11], [10], [01], or [00], corresponding to both species present, only species 1 or species 2 present, or both species absent, respectively. The latent state modeled as a multivariate Bernoulli random variable:

Z_i ~ MVB(psi_i)

where ψ_i is a vector of length 2^s containing the probability of each possible combination of 0s and 1s, such that sum(psi_i) = 1.

For s = 2, the corresponding natural parameters f are

f_1 = log(psi_10/psi_00)

f_2 = log(psi_01/psi_00)

f_12 = log((psi_11 * psi_00)/(psi_10 * psi_01))

The natural parameters can then be modeled as linear functions of covariates. Covariates for each f must be specified with the `stateformulas` argument, which takes a character vector of individual formulas of length equal to the number of natural parameters (which in turn depends on the number of species in the model).

The observation process is similar to the standard single-species occupancy model, except that the observations y_ij at site i on occasion j are vectors of length s and there are independent values of detection probability p for each species s:

y_ij | Z_i ~ Bernoulli(Z_i * p_sij)

Independent detection models (potentially containing different covariates) must be provided for each species with the `detformulas` argument, which takes a character vector of individual formulas with length equal to the number of species s.

### Value

unmarkedFitOccuMulti object describing the model fit.

### Author(s)

Ken Kellner contact@kenkellner.com

### References

MacKenzie, D. I., J. D. Nichols, G. B. Lachman, S. Droege, J. Andrew Royle, and C. A. Langtimm. 2002. Estimating Site Occupancy Rates When Detection Probabilities Are Less Than One. Ecology 83: 2248-2255.

Rota, C.T., et al. 2016. A multi-species occupancy model for two or more interacting species. Methods in Ecology and Evolution 7: 1164-1173.

### See Also

`unmarked`, `unmarkedFrameOccuMulti`

### Examples

    #Simulate 3 species data
    N <- 1000
    nspecies <- 3
    J <- 5

    occ_covs <- as.data.frame(matrix(rnorm(N * 10),ncol=10))
    names(occ_covs) <- paste('par',1:10,sep='')

    det_covs <- list()
    for (i in 1:nspecies){
      det_covs[[i]] <- matrix(rnorm(N*J),nrow=N)
    }
    names(det_covs) <- paste('par',1:nspecies,sep='')

    #True vals
    beta <- c(0.5,0.2,0.4,0.5,-0.1,-0.3,0.2,0.1,-1,0.1)
    f1 <- beta[1] + beta[2]*occ_covs$par1
    f2 <- beta[3] + beta[4]*occ_covs$par2
    f3 <- beta[5] + beta[6]*occ_covs$par3
    f4 <- beta[7]
    f5 <- beta[8]
    f6 <- beta[9]
    f7 <- beta[10]
    f <- cbind(f1,f2,f3,f4,f5,f6,f7)
    z <- expand.grid(rep(list(1:0),nspecies))[,nspecies:1]
    colnames(z) <- paste('sp',1:nspecies,sep='')
    dm <- model.matrix(as.formula(paste0("~.^",nspecies,"-1")),z)

    psi <- exp(f %*% t(dm))
    psi <- psi/rowSums(psi)

    #True state
    ztruth <- matrix(NA,nrow=N,ncol=nspecies)
    for (i in 1:N){
      ztruth[i,] <- as.matrix(z[sample(8,1,prob=psi[i,]),])
    }

    p_true <- c(0.6,0.7,0.5)

    # fake y data
    y <- list()

    for (i in 1:nspecies){
      y[[i]] <- matrix(NA,N,J)
      for (j in 1:N){
        for (k in 1:J){
          y[[i]][j,k] <- rbinom(1,1,ztruth[j,i]*p_true[i])
        }
      }
    }
    names(y) <- c('coyote','tiger','bear')

    #Create the unmarked data object
    data = unmarkedFrameOccuMulti(y=y,siteCovs=occ_covs,obsCovs=det_covs)

    #Summary of data object
    summary(data)
    plot(data)

    # Look at f parameter design matrix
    data@fDesign

    # Formulas for state and detection processes

    # Length should match number/order of columns in fDesign
    occFormulas <- c('~par1','~par2','~par3','~1','~1','~1','~1')

    #Length should match number/order of species in data@ylist
    detFormulas <- c('~1','~1','~1')

    fit <- occuMulti(detFormulas,occFormulas,data)

    #Look at output
    fit

    ## Not run:
    plot(fit)

    #Compare with known values
    cbind(c(beta,log(p_true/(1-p_true))),fit@opt$par)

    #predict method
    lapply(predict(fit,'state'),head)
    lapply(predict(fit,'det'),head)

    #marginal occupancy
    head(predict(fit,'state',species=2))
    head(predict(fit,'state',species='bear'))
    head(predict(fit,'det',species='coyote'))

    #conditional occupancy
    head(predict(fit,'state',species=2,cond=3)) #tiger | bear present
    head(predict(fit,'state',species='tiger',cond='bear')) #tiger | bear present
    head(predict(fit,'state',species='tiger',cond='-bear')) #bear absent
    head(predict(fit,'state',species='tiger',cond=c('coyote','-bear')))

    #residuals (by species)
    lapply(residuals(fit),head)

    #ranef (by species)
    ranef(fit, species='coyote')

    #parametric bootstrap
    bt <- parboot(fit,nsim=30)

    #update model
    occFormulas <- c('~par1','~par2','~par2+par3','~1','~1','~1','~1')
    fit2 <- update(fit,stateformulas=occFormulas)

    #List of fitted models
    fl <- fitList(fit,fit2)
    coef(fl)

    #Model selection
    modSel(fl)

    #Fit model while forcing some natural parameters to be 0
    #For example: fit model with no species interactions
    occFormulas <- c('~par1','~par2','~par2+par3','0','0','0','0')
    fit3 <- occuMulti(detFormulas,occFormulas,data)

    #Alternatively, you can force all interaction parameters above a certain
    #order to be zero with maxOrder. This will be faster.
    occFormulas <- c('~par1','~par2','~par2+par3')
    fit4 <- occuMulti(detFormulas,occFormulas,data,maxOrder=1)

    ## End(Not run)

[Package unmarked version 0.12-3 Index]