R: Organize data for the multispecies occupancy model fit by...

unmarkedFrameOccuMulti {unmarked} | R Documentation

## Organize data for the multispecies occupancy model fit by occuMulti

### Description

Organizes detection, non-detection data for multiple species along with the covariates. This S4 class is required by the data argument of `occuMulti`

### Usage

    unmarkedFrameOccuMulti(y, siteCovs=NULL, obsCovs=NULL,
                                  maxOrder, mapInfo)

### Arguments

`y` | A list (optionally a named list) of length S where each element is an MxJ matrix of the detection, non-detection data for one species, where M is the number of sites, J is the maximum number of sampling periods per site, and S is the number of species in the analysis.
`siteCovs` | A `data.frame` of covariates that vary at the site level. This should have M rows and one column per covariate
`obsCovs` | Either a named list of `data.frame`s of covariates that vary within sites, or a `data.frame` with MxJ rows in site-major order.
`maxOrder` | Optional; specify maximum interaction order. Defaults to number of species (all possible interactions). Reducing this value may speed up creation of unmarked frame if you aren't interested in higher-order interactions.
`mapInfo` | Currently ignored

### Details

unmarkedFrameOccuMulti is the S4 class that holds data to be passed to the `occuMulti` model-fitting function.

### Value

an object of class unmarkedFrameOccuMulti

### Author(s)

Ken Kellner contact@kenkellner.com

### See Also

`unmarkedFrame-class`, `unmarkedFrame`, `occuMulti`

### Examples

    # Fake data
    S <- 3 # number of species
    M <- 4 # number of sites
    J <- 3 # number of visits

    y <- list(matrix(rbinom(M*J,1,0.5),M,J), # species 1
              matrix(rbinom(M*J,1,0.5),M,J), # species 2
              matrix(rbinom(M*J,1,0.2),M,J)) # species 3

    site.covs <- data.frame(x1=1:4, x2=factor(c('A','B','A','B')))
    site.covs

    umf <- unmarkedFrameOccuMulti(y=y, siteCovs=site.covs,
        obsCovs=NULL)   # organize data
    umf                     # look at data
    summary(umf)            # summarize
    plot(umf)               # visualize
    #fm <- occu(~1 ~1, umf)  # fit a model

[Package unmarked version 1.0.0 Index]