# Organize data for the multinomial-Poisson mixture model of Royle (2004) fit by multinomPois Organizes count data along with the covariates. This S4 class is required by the data argument of `multinomPois` ```r unmarkedFrameMPois(y, siteCovs=NULL, obsCovs=NULL, type, obsToY, 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. - `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. ## Returns 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 ```r # 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) ```

unmarkedFrameMPois function