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

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

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.frames 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.frames, 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., piFuns) 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)
#> unmarkedFrame Object
#> 
#> 100 sites
#> Maximum number of observations per site: 15 
#> Mean number of observations per site: 15 
#> Number of primary survey periods: 5 
#> Number of secondary survey periods: 3 
#> Sites with at least one detection: 100 
#> 
#> Tabulation of y observations:
#>   0   1   2   3   4   5   6 
#> 621 460 255 104  44  11   5 
#> 
#> Site-level covariates:
#>        x1          
#>  Min.   :-2.08984  
#>  1st Qu.:-0.84632  
#>  Median :-0.10159  
#>  Mean   :-0.02812  
#>  3rd Qu.: 0.72915  
#>  Max.   : 2.69840