R: Create an object of class unmarkedFrameMMO that contains data...

unmarkedFrameMMO {unmarked} | R Documentation

## Create an object of class unmarkedFrameMMO that contains data used by multmixOpen.

### Description

Organizes count data and experimental design information from multiple primary periods along with associated covariates. This S4 class is required by the data argument of `multmixOpen`

### Usage

    unmarkedFrameMMO(y, siteCovs=NULL, obsCovs=NULL, yearlySiteCovs=NULL,
           numPrimary, type, primaryPeriod)

### Arguments

`y` | An MxJT matrix of the repeated count data, where M is the number of sites (i.e., points or transects), J is the number of distance classes and T is the maximum number of primary sampling periods per site
`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 MxJT rows in site-major order.
`yearlySiteCovs` | Either a named list of MxT `data.frame`s, or a site-major `data.frame` with MT rows and 1 column per covariate
`numPrimary` | Maximum number of observed primary periods for each site
`type` | Either "removal" for removal sampling, "double" for standard double observer sampling, or "depDouble" for dependent double observer sampling
`primaryPeriod` | An MxJT matrix of integers indicating the primary period of each observation

### Details

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

Options for the detection process (`type`) include equal-interval removal sampling (`"removal"`), double observer sampling (`"double"`), or dependent double-observer sampling (`"depDouble"`). Note that unlike the related functions `multinomPois` and `gmultmix`, custom functions for the detection process (i.e., `piFun`s) are not supported. To request additional options contact the author.

When gamma or omega are modeled using year-specific covariates, the covariate data for the final year will be ignored; however, they must be supplied.

If the time gap between primary periods is not constant, an M by T matrix of integers should be supplied using the `primaryPeriod` argument.

### Value

an object of class unmarkedFrameMMO

### See Also

`unmarkedFrame-class`, `unmarkedFrame`, `multmixOpen`

### Examples

      #Generate some data
      set.seed(123)
      lambda=4; gamma=0.5; omega=0.8; p=0.5
      M <- 100; T <- 5
      y <- array(NA, c(M, 3, T))
      N <- matrix(NA, M, T)
      S <- G <- matrix(NA, M, T-1)

      for(i in 1:M) {
        N[i,1] <- rpois(1, lambda)
        y[i,1,1] <- rbinom(1, N[i,1], p)    # Observe some
        Nleft1 <- N[i,1] - y[i,1,1]         # Remove them
        y[i,2,1] <- rbinom(1, Nleft1, p)   # ...
        Nleft2 <- Nleft1 - y[i,2,1]
        y[i,3,1] <- rbinom(1, Nleft2, p)

        for(t in 1:(T-1)) {
          S[i,t] <- rbinom(1, N[i,t], omega)
          G[i,t] <- rpois(1, gamma)
          N[i,t+1] <- S[i,t] + G[i,t]
          y[i,1,t+1] <- rbinom(1, N[i,t+1], p)    # Observe some
          Nleft1 <- N[i,t+1] - y[i,1,t+1]         # Remove them
          y[i,2,t+1] <- rbinom(1, Nleft1, p)   # ...
          Nleft2 <- Nleft1 - y[i,2,t+1]
          y[i,3,t+1] <- rbinom(1, Nleft2, p)
        }
      }
      y=matrix(y, M)

      #Create some random covariate data
      sc <- data.frame(x1=rnorm(100))

      #Create unmarked frame
      umf <- unmarkedFrameMMO(y=y, numPrimary=5, siteCovs=sc, type="removal")

      summary(umf)

[Package unmarked version 1.0.0 Index]