View source: R/unmarkedFrame.R
unmarkedFrameMMO | R Documentation |
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)
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 |
obsCovs |
Either a named list of |
yearlySiteCovs |
Either a named list of MxT |
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 |
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.
an object of class unmarkedFrameMMO
unmarkedFrame-class
, unmarkedFrame
,
multmixOpen
#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)
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