R: Organize data for the multinomial-Poisson mixture model of...

unmarkedFrameMPois {unmarked} | R Documentation

## Organize data for the multinomial-Poisson mixture model of Royle (2004) fit by multinomPois

### Description

Organizes count data along with the covariates. This S4 class is required by the data argument of `multinomPois`

### Usage

    unmarkedFrameMPois(y, siteCovs=NULL, obsCovs=NULL, type, obsToY,
        mapInfo, piFun)

### Arguments

`y` | An RxJ matrix of count data, where R is the number of sites (transects) and J is the maximum number of observations per site.
`siteCovs` | A `data.frame` of covariates that vary at the site level. This should have R rows and one column per covariate
`obsCovs` | Either a named list of RxJ `data.frame`s or a `data.frame` with RxJ rows and one column per covariate. For the latter format, the covariates should be in site-major order.
`type` | Either "removal" for removal sampling, "double" for standard double observer sampling, or "depDouble" for dependent double observer sampling. If this argument not specified, the user must provide an `obsToY` matrix. See details.
`obsToY` | A matrix describing the relationship between `obsCovs` and `y`. This is necessary because under some sampling designs the dimensions of `y` do not equal the dimensions of each observation level covariate. For example, in double observer sampling there are 3 observations (seen only by observer A, detected only by observer B, and detected by both), but each observation-level covariate can only have 2 columns, one for each observer. This matrix is created automatically if `type` is specified.
`mapInfo` | Currently ignored
`piFun` | Function used to compute the multinomial cell probabilities from a matrix of detection probabilities. This is created automatically if `type` is specified.

### Details

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

### Value

an object of class unmarkedFrameMPois

### References

Royle, J. A. (2004). Generalized estimators of avian abundance from count survey data. Animal Biodiversity and Conservation, 27(1), 375-386.

### See Also

`unmarkedFrame-class`, `unmarkedFrame`, `multinomPois`, `piFuns`

### Examples

    # Fake doulbe observer data
    R <- 4 # number of sites
    J <- 2 # number of observers

    y <- matrix(c(
       1,0,3,
       0,0,0,
       2,0,1,
       0,0,2), nrow=R, ncol=J+1, byrow=TRUE)
    y

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

    obs.covs <- list(
       x3 = matrix(c(
          -1,0,
          -2,0,
          -3,1,
           0,0),
          nrow=R, ncol=J, byrow=TRUE),
       x4 = matrix(c(
          'a','b',
          'a','b',
          'a','b',
          'a','b'),
          nrow=R, ncol=J, byrow=TRUE))
    obs.covs

    # Create unmarkedFrame
    umf <- unmarkedFrameMPois(y=y, siteCovs=site.covs, obsCovs=obs.covs,
        type="double")

    # The above is the same as:
    o2y <- matrix(1, 2, 3)
    pifun <- function(p)
    {
        M <- nrow(p)
        pi <- matrix(NA, M, 3)
        pi[, 1] <- p[, 1] * (1 - p[, 2])
        pi[, 2] <- p[, 2] * (1 - p[, 1])
        pi[, 3] <- p[, 1] * p[, 2]
        return(pi)
    }

    umf <- unmarkedFrameMPois(y=y, siteCovs=site.covs, obsCovs=obs.covs,
        obsToY=o2y, piFun="pifun")

    # Fit a model
    fm <- multinomPois(~1 ~1, umf)

[Package unmarked version 1.0.0 Index]