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unmarkedMultFrame(y, siteCovs, obsCovs, numPrimary, yearlySiteCovs)
unmarkedFrameGMM(y, siteCovs, obsCovs, numPrimary, yearlySiteCovs, type,
obsToY, piFun)
unmarkedFrameGDS(y, siteCovs, numPrimary, yearlySiteCovs, dist.breaks,
survey, unitsIn, tlength)unmarkedFrameDSunmarkedFrameDSunmarkedFrameDSunmarkedFrameDScolext.
unmarkedFrameGMM objects are used by gmultmix.
unmarkedFrameGDS objects are used by gdistsamp.
For a study with M sites, T years, and a maximum of
J observations per site-year, the data are shaped as follows.
y is an $M \times TJ$ matrix, with each row
corresponding to a site. siteCovs is a data frame with $M$
rows. yearlySiteCovs is a data frame with $MT$ rows which
are in site-major, year-minor order. obsCovs is a data frame
with $MTJ$ rows, which are ordered by site-year-observation, so that
a column of obsCovs corresponds to numPrimary.
If the data are in long format, the convenience function
formatMult is useful for creating the unmarkedMultFrame.
unmarkedFrameGMM and unmarkedFrameGDS are superclasses of
unmarkedMultFrame containing information on
the survey design used that resulted in multinomial outcomes. For
unmarkedFrameGMM and constant-interval removal sampling, you can set
type="removal" and ignore
the arguments obsToY and piFun. Similarly, for double-observer sampling,
setting type="double" will automatically create an appropiate obsToY matrix and
piFuns. For all other situations, the type argument of
unmarkedFrameGMM should be
ignored and the obsToY and piFun arguments must be specified. piFun must be a
function that converts an MxJ matrix of detection probabilities into an MxJ
matrix of multinomial cell probabilities. obsToY is a matrix describing how
the obsCovs relate to the observed counts y. For further discussion and examples
see the help page for multinomPois and piFuns.
unmarkedFrameGMM and unmarkedFrameGDS objects can be created from an
unmarkedMultFrame using the
"as" conversion method. See examples.formatMultn <- 50 # number of sites
T <- 4 # number of primary periods
J <- 3 # number of secondary periods
site <- 1:50
years <- data.frame(matrix(rep(2010:2013, each=n), n, T))
years <- data.frame(lapply(years, as.factor))
occasions <- data.frame(matrix(rep(1:(J*T), each=n), n, J*T))
y <- matrix(0:1, n, J*T)
umf <- unmarkedMultFrame(y=y,
siteCovs = data.frame(site=site),
obsCovs=list(occasion=occasions),
yearlySiteCovs=data.frame(year=years),
numPrimary=T)
umfGMM1 <- unmarkedFrameGMM(y=y,
siteCovs = data.frame(site=site),
obsCovs=list(occasion=occasions),
yearlySiteCovs=data.frame(year=c(t(years))),
# or: yearlySiteCovs=list(year=years),
numPrimary=T, type="removal")
# A user-defined piFun calculating removal probs when time intervals differ.
instRemPiFun <- function(p) {
M <- nrow(p)
J <- ncol(p)
pi <- matrix(NA, M, J)
p[,1] <- pi[,1] <- 1 - (1 - p[,1])^2
p[,2] <- 1 - (1 - p[,2])^3
p[,3] <- 1 - (1 - p[,3])^5
for(i in 2:J) {
pi[,i] <- pi[, i - 1]/p[, i - 1] * (1 - p[, i - 1]) * p[, i]
}
return(pi)
}
# Associated obsToY matrix required by unmarkedFrameMPois
o2y <- diag(ncol(y))
o2y[upper.tri(o2y)] <- 1
o2y
umfGMM2 <- unmarkedFrameGMM(y=y,
siteCovs = data.frame(site=site),
obsCovs=list(occasion=occasions),
yearlySiteCovs=data.frame(year=years),
numPrimary=T, obsToY=o2y, piFun="instRemPiFun")
str(umfGMM2)Run the code above in your browser using DataLab