# Create an unmarkedMultFrame, unmarkedFrameGMM, unmarkedFrameGDS, or unmarkedFrameGPC object These functions construct unmarkedFrames for data collected during primary and secondary sampling periods. ```r 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) unmarkedFrameGPC(y, siteCovs, obsCovs, numPrimary, yearlySiteCovs) ``` ## Details unmarkedMultFrame objects are used by `colext`. unmarkedFrameGMM objects are used by `gmultmix`. unmarkedFrameGDS objects are used by `gdistsamp`. unmarkedFrameGPC objects are used by `gpcount`. For a study with **M** sites, **T** years, and a maximum of **J** observations per site-year, the data can be supplied in a variety of ways but are stored as follows. `y` is an $M by TJ$ 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 as.vector(t(y)) , element-by-element. The number of years must be specified in `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" or type="depDouble" 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. ## Note Data used with colext , gmultmix , and gdistsamp may be collected during a single year, so yearlySiteCovs may be a misnomer is some cases. ## Returns an unmarkedMultFrame or unmarkedFrameGMM object ## Arguments - `y`: A matrix of the observed data. - `siteCovs`: Data frame of covariates that vary at the site level. - `obsCovs`: Data frame of covariates that vary within site-year-observation level. - `numPrimary`: Number of primary time periods (seasons in the multiseason model). - `yearlySiteCovs`: Data frame containing covariates at the site-year level. - `type`: Set to "removal" for constant-interval removal sampling, "double" for standard double observer sampling, or "depDouble" for dependent double observer sampling. This should be not be specified for other types of survey designs. - `obsToY`: A matrix specifying relationship between observation-level covariates and response matrix - `piFun`: A function converting an MxJ matrix of detection probabilities into an MxJ matrix of multinomial cell probabilities. - `dist.breaks`: see `unmarkedFrameDS` - `survey`: see `unmarkedFrameDS` - `unitsIn`: see `unmarkedFrameDS` - `tlength`: see `unmarkedFrameDS` ## See Also `formatMult`, `colext`, `gmultmix`, `gpcount` ## Examples ```r n <- 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=list(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=c(t(years))), numPrimary=T, obsToY=o2y, piFun="instRemPiFun") str(umfGMM2) ```

unmarkedMultFrame function