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getDesign.R
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getDesign.R
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setGeneric("getDesign", function(umf, ...) standardGeneric("getDesign"))
setGeneric("handleNA", function(umf, ...) standardGeneric("handleNA"))
# unmarkedFrame
setMethod("getDesign", "unmarkedFrame",
function(umf, formula, na.rm=TRUE)
{
detformula <- lme4::nobars(as.formula(formula[[2]]))
stateformula <- lme4::nobars(as.formula(paste("~", formula[3], sep="")))
detVars <- all.vars(detformula)
M <- numSites(umf)
R <- obsNum(umf)
## Compute state design matrix
if(is.null(siteCovs(umf))) {
siteCovs <- data.frame(placeHolder = rep(1, M))
} else {
siteCovs <- siteCovs(umf)
}
miss_vars <-all.vars(stateformula)[!all.vars(stateformula) %in% names(siteCovs)]
if(length(miss_vars) > 0){
stop(paste("Variable(s)", paste(miss_vars, collapse=", "),
"not found in siteCovs"), call.=FALSE)
}
X.mf <- model.frame(stateformula, siteCovs, na.action = NULL)
X <- model.matrix(stateformula, X.mf)
X.offset <- as.vector(model.offset(X.mf))
if (!is.null(X.offset)) {
X.offset[is.na(X.offset)] <- 0
}
## Compute detection design matrix
if(is.null(obsCovs(umf))) {
obsCovs <- data.frame(placeHolder = rep(1, M*R))
} else {
obsCovs <- obsCovs(umf)
}
## Record future column names for obsCovs
colNames <- c(colnames(obsCovs), colnames(siteCovs))
## add site Covariates at observation-level
obsCovs <- cbind(obsCovs, siteCovs[rep(1:M, each = R),])
colnames(obsCovs) <- colNames
## add observation number if not present
if(!("obsNum" %in% names(obsCovs))) {
obsCovs <- cbind(obsCovs, obsNum = as.factor(rep(1:R, M)))
}
miss_vars <-all.vars(detformula)[!all.vars(detformula) %in% names(obsCovs)]
if(length(miss_vars) > 0){
stop(paste("Variable(s)", paste(miss_vars, collapse=", "),
"not found in obsCovs"), call.=FALSE)
}
V.mf <- model.frame(detformula, obsCovs, na.action = NULL)
V <- model.matrix(detformula, V.mf)
V.offset <- as.vector(model.offset(V.mf))
if (!is.null(V.offset)) {
V.offset[is.na(V.offset)] <- 0
}
#Generate random effects indicator matrices
sf_rand <- as.formula(paste("~", formula[3], sep=""))
df_rand <- as.formula(formula[[2]])
Z_state <- get_Z(sf_rand, siteCovs)
Z_det <- get_Z(df_rand, obsCovs)
if (na.rm) {
out <- handleNA(umf, X, X.offset, V, V.offset, Z_state, Z_det)
y <- out$y
X <- out$X
X.offset <- out$X.offset
V <- out$V
V.offset <- out$V.offset
Z_state <- out$Z_state
Z_det <- out$Z_det
removed.sites <- out$removed.sites
} else {
y=getY(umf)
removed.sites=integer(0)
}
if (is.null(X.offset)) X.offset <- rep(0, nrow(X))
if (is.null(V.offset)) V.offset <- rep(0, nrow(V))
return(list(y = y, X = X, X.offset = X.offset, V = V,
V.offset = V.offset,
Z_state = Z_state, Z_det = Z_det,
removed.sites = removed.sites))
})
setMethod("handleNA", "unmarkedFrame",
function(umf, X, X.offset, V, V.offset, Z_state, Z_det)
{
obsToY <- obsToY(umf)
if(is.null(obsToY)) stop("obsToY cannot be NULL to clean data.")
J <- numY(umf)
R <- obsNum(umf)
M <- numSites(umf)
X.long <- X[rep(1:M, each = J),]
X.long.na <- is.na(X.long)
V.long.na <- apply(V, 2, function(x) {
x.mat <- matrix(x, M, R, byrow = TRUE)
x.mat <- is.na(x.mat)
x.mat <- x.mat %*% obsToY
x.long <- as.vector(t(x.mat))
x.long > 0
})
V.long.na <- apply(V.long.na, 1, any)
y.long <- as.vector(t(getY(umf)))
y.long.na <- is.na(y.long)
covs.na <- apply(cbind(X.long.na, V.long.na), 1, any)
## are any NA in covs not in y already?
y.new.na <- covs.na & !y.long.na
if(sum(y.new.na) > 0) {
y.long[y.new.na] <- NA
warning("Some observations have been discarded because corresponding covariates were missing.", call. = FALSE)
}
y <- matrix(y.long, M, J, byrow = TRUE)
sites.to.remove <- apply(y, 1, function(x) all(is.na(x)))
num.to.remove <- sum(sites.to.remove)
if(num.to.remove > 0) {
y <- y[!sites.to.remove, ,drop = FALSE]
X <- X[!sites.to.remove, ,drop = FALSE]
X.offset <- X.offset[!sites.to.remove]
V <- V[!sites.to.remove[rep(1:M, each = R)], ,drop = FALSE]
V.offset <- V.offset[!sites.to.remove[rep(1:M, each = R)], ]
if(nrow(Z_state)>0) Z_state <- Z_state[!sites.to.remove, , drop=FALSE]
if(nrow(Z_det)>0) Z_det <- Z_det[!sites.to.remove[rep(1:M, each = R)],,drop=FALSE]
warning(paste(num.to.remove,"sites have been discarded because of missing data."), call. = FALSE)
}
list(y = y, X = X, X.offset = X.offset, V = V, V.offset = V.offset,
Z_state = Z_state, Z_det = Z_det,
removed.sites = which(sites.to.remove))
})
# unmarkedFrameOccuFP
# like the occuFP but there are 3 osbservation formula which are stored in V (true positive detections),
# U (false positive detections), and W (b or probability detetion is certain)
setMethod("getDesign", "unmarkedFrameOccuFP",
function(umf, detformula,FPformula,Bformula = ~.,stateformula, na.rm=TRUE)
{
M <- numSites(umf)
R <- obsNum(umf)
## Compute state design matrix
if(is.null(siteCovs(umf))) {
siteCovs <- data.frame(placeHolder = rep(1, M))
} else {
siteCovs <- siteCovs(umf)
}
X.mf <- model.frame(stateformula, siteCovs, na.action = NULL)
X <- model.matrix(stateformula, X.mf)
X.offset <- as.vector(model.offset(X.mf))
if (!is.null(X.offset)) {
X.offset[is.na(X.offset)] <- 0
}
## Compute detection design matrix
if(is.null(obsCovs(umf))) {
obsCovs <- data.frame(placeHolder = rep(1, M*R))
} else {
obsCovs <- obsCovs(umf)
}
## Record future column names for obsCovs
colNames <- c(colnames(obsCovs), colnames(siteCovs))
## add site Covariates at observation-level
obsCovs <- cbind(obsCovs, siteCovs[rep(1:M, each = R),])
colnames(obsCovs) <- colNames
## add observation number if not present
if(!("obsNum" %in% names(obsCovs))) {
obsCovs <- cbind(obsCovs, obsNum = as.factor(rep(1:R, M)))
}
V.mf <- model.frame(detformula, obsCovs, na.action = NULL)
V <- model.matrix(detformula, V.mf)
V.offset <- as.vector(model.offset(V.mf))
if (!is.null(V.offset)) {
V.offset[is.na(V.offset)] <- 0
}
U.mf <- model.frame(FPformula, obsCovs, na.action = NULL)
U <- model.matrix(FPformula, U.mf)
U.offset <- as.vector(model.offset(U.mf))
if (!is.null(U.offset)) {
U.offset[is.na(U.offset)] <- 0
}
W.mf <- model.frame(Bformula, obsCovs, na.action = NULL)
W <- model.matrix(Bformula, W.mf)
W.offset <- as.vector(model.offset(W.mf))
if (!is.null(W.offset)) {
W.offset[is.na(W.offset)] <- 0
}
if (na.rm) {
out <- handleNA(umf, X, X.offset, V,V.offset, U, U.offset, W, W.offset)
y <- out$y
X <- out$X
X.offset <- out$X.offset
V <- out$V
V.offset <- out$V.offset
U <- out$U
U.offset <- out$U.offset
U <- out$U
U.offset <- out$U.offset
removed.sites <- out$removed.sites
} else {
y=getY(umf)
removed.sites=integer(0)
}
return(list(y = y, X = X, X.offset = X.offset, V = V,
V.offset = V.offset,U = U, U.offset = U.offset,W = W,
W.offset = W.offset, removed.sites = removed.sites))
})
setMethod("handleNA", "unmarkedFrameOccuFP",
function(umf, X, X.offset, V, V.offset, U, U.offset, W, W.offset)
{
obsToY <- obsToY(umf)
if(is.null(obsToY)) stop("obsToY cannot be NULL to clean data.")
J <- numY(umf)
R <- obsNum(umf)
M <- numSites(umf)
X.long <- X[rep(1:M, each = J),]
X.long.na <- is.na(X.long)
V.long.na <- apply(V, 2, function(x) {
x.mat <- matrix(x, M, R, byrow = TRUE)
x.mat <- is.na(x.mat)
x.mat <- x.mat %*% obsToY
x.long <- as.vector(t(x.mat))
x.long > 0
})
V.long.na <- apply(V.long.na, 1, any)
U.long.na <- apply(U, 2, function(x) {
x.mat <- matrix(x, M, R, byrow = TRUE)
x.mat <- is.na(x.mat)
x.mat <- x.mat %*% obsToY
x.long <- as.vector(t(x.mat))
x.long > 0
})
U.long.na <- apply(U.long.na, 1, any)
W.long.na <- apply(W, 2, function(x) {
x.mat <- matrix(x, M, R, byrow = TRUE)
x.mat <- is.na(x.mat)
x.mat <- x.mat %*% obsToY
x.long <- as.vector(t(x.mat))
x.long > 0
})
W.long.na <- apply(W.long.na, 1, any)
y.long <- as.vector(t(getY(umf)))
y.long.na <- is.na(y.long)
covs.na <- apply(cbind(X.long.na, V.long.na, U.long.na, W.long.na), 1, any)
## are any NA in covs not in y already?
y.new.na <- covs.na & !y.long.na
if(sum(y.new.na) > 0) {
y.long[y.new.na] <- NA
warning("Some observations have been discarded because corresponding covariates were missing.", call. = FALSE)
}
y <- matrix(y.long, M, J, byrow = TRUE)
sites.to.remove <- apply(y, 1, function(x) all(is.na(x)))
num.to.remove <- sum(sites.to.remove)
if(num.to.remove > 0) {
y <- y[!sites.to.remove, ,drop = FALSE]
X <- X[!sites.to.remove, ,drop = FALSE]
X.offset <- X.offset[!sites.to.remove]
V <- V[!sites.to.remove[rep(1:M, each = R)], ,drop = FALSE]
V.offset <- V.offset[!sites.to.remove[rep(1:M, each = R)], ]
U <- U[!sites.to.remove[rep(1:M, each = R)], ,drop = FALSE]
U.offset <- U.offset[!sites.to.remove[rep(1:M, each = R)], ]
W <- W[!sites.to.remove[rep(1:M, each = R)], ,drop = FALSE]
W.offset <- W.offset[!sites.to.remove[rep(1:M, each = R)], ]
warning(paste(num.to.remove,"sites have been discarded because of missing data."), call. = FALSE)
}
list(y = y, X = X, X.offset = X.offset, V = V, V.offset = V.offset,
U = U, U.offset = U.offset, W = W, W.offset = W.offset,
removed.sites = which(sites.to.remove))
})
# UnmarkedMultFrame
setMethod("getDesign", "unmarkedMultFrame",
function(umf, formula, na.rm = TRUE) {
aschar1 <- as.character(formula)
aschar2 <- as.character(formula[[2]])
aschar3 <- as.character(formula[[2]][[2]])
detformula <- as.formula(paste(aschar1[1], aschar1[3]))
epsformula <- as.formula(paste(aschar2[1], aschar2[3]))
gamformula <- as.formula(paste(aschar3[1], aschar3[3]))
psiformula <- as.formula(formula[[2]][[2]][[2]])
detVars <- all.vars(detformula)
M <- numSites(umf)
R <- obsNum(umf)
nY <- umf@numPrimary
J <- R / nY
## Compute phi design matrices
if(is.null(umf@yearlySiteCovs)) {
yearlySiteCovs <- data.frame(placeHolder = rep(1, M*nY))
} else {
yearlySiteCovs <- umf@yearlySiteCovs
}
## add siteCovs in so they can be used as well
if(!is.null(umf@siteCovs)) {
sC <- umf@siteCovs[rep(1:M, each = nY),,drop=FALSE]
yearlySiteCovs <- cbind(yearlySiteCovs, sC)
}
## Compute site-level design matrix for psi
if(is.null(siteCovs(umf)))
siteCovs <- data.frame(placeHolder = rep(1, M))
else
siteCovs <- siteCovs(umf)
W.mf <- model.frame(psiformula, siteCovs, na.action = NULL)
if(!is.null(model.offset(W.mf)))
stop("offsets not currently allowed in colext", call.=FALSE)
W <- model.matrix(psiformula, W.mf)
## Compute detection design matrix
if(is.null(obsCovs(umf)))
obsCovs <- data.frame(placeHolder = rep(1, M*R))
else
obsCovs <- obsCovs(umf)
## add site and yearlysite covariates, which contain siteCovs
cnames <- c(colnames(obsCovs), colnames(yearlySiteCovs))
obsCovs <- cbind(obsCovs, yearlySiteCovs[rep(1:(M*nY), each = J),])
colnames(obsCovs) <- cnames
## add observation number if not present
if(!("obsNum" %in% names(obsCovs)))
obsCovs <- cbind(obsCovs, obsNum = as.factor(rep(1:R, M)))
V.mf <- model.frame(detformula, obsCovs, na.action = NULL)
if(!is.null(model.offset(V.mf)))
stop("offsets not currently allowed in colext", call.=FALSE)
V <- model.matrix(detformula, V.mf)
## in order to drop factor levels that only appear in last year,
## replace last year with NAs and use drop=TRUE
yearlySiteCovs[seq(nY,M*nY,by=nY),] <- NA
yearlySiteCovs <- as.data.frame(lapply(yearlySiteCovs, function(x) {
x[,drop = TRUE]
}))
X.mf.gam <- model.frame(gamformula, yearlySiteCovs, na.action = NULL)
if(!is.null(model.offset(X.mf.gam)))
stop("offsets not currently allowed in colext", call.=FALSE)
X.gam <- model.matrix(gamformula, X.mf.gam)
X.mf.eps <- model.frame(epsformula, yearlySiteCovs, na.action = NULL)
if(!is.null(model.offset(X.mf.eps)))
stop("offsets not currently allowed in colext", call.=FALSE)
X.eps <- model.matrix(epsformula, X.mf.eps)
if(na.rm)
out <- handleNA(umf, X.gam, X.eps, W, V)
else
out <- list(y=getY(umf), X.gam=X.gam, X.eps=X.eps, W=W, V=V,
removed.sites=integer(0))
return(list(y = out$y, X.eps = out$X.eps, X.gam = out$X.gam, W = out$W,
V = out$V, removed.sites = out$removed.sites))
})
setMethod("handleNA", "unmarkedMultFrame",
function(umf, X.gam, X.eps, W, V)
{
obsToY <- obsToY(umf)
if(is.null(obsToY)) stop("obsToY cannot be NULL to clean data.")
R <- obsNum(umf)
M <- numSites(umf)
nY <- umf@numPrimary
J <- numY(umf) / nY
## treat both X's #######no: and W together
# X <- cbind(X.gam, X.eps, W[rep(1:M, each = nY), ])
X <- cbind(X.gam, X.eps)
X.na <- is.na(X)
X.na[seq(nY,M*nY,by=nY),] <- FALSE ## final years are unimportant.
## not true for W covs!!!
W.expand <- W[rep(1:M, each=nY),,drop=FALSE]
W.na <- is.na(W.expand)
X.na <- cbind(X.na, W.na) # NAs in siteCovs results in removed site
X.long.na <- X.na[rep(1:(M*nY), each = J),]
V.long.na <- apply(V, 2, function(x) {
x.mat <- matrix(x, M, R, byrow = TRUE)
x.mat <- is.na(x.mat)
x.mat <- x.mat %*% obsToY
x.long <- as.vector(t(x.mat))
x.long > 0
})
V.long.na <- apply(V.long.na, 1, any)
y.long <- as.vector(t(getY(umf)))
y.long.na <- is.na(y.long)
# It doesn't make sense to combine X.gam/eps with W here b/c
# a X.eps does not map correctly to y
covs.na <- apply(cbind(X.long.na, V.long.na), 1, any)
## are any NA in covs not in y already?
y.new.na <- covs.na & !y.long.na
if(sum(y.new.na) > 0) {
y.long[y.new.na] <- NA
warning("Some observations have been discarded because correspoding covariates were missing.", call. = FALSE)
}
y <- matrix(y.long, M, numY(umf), byrow = TRUE)
sites.to.remove <- apply(y, 1, function(x) all(is.na(x)))
# Perhaps we need to remove sites that have no data in T=1
# noDataT1 <- apply(is.na(y[,1:J]), 1, all) #
# sites.to.remove <- sites.to.remove | noDataT1
num.to.remove <- sum(sites.to.remove)
if(num.to.remove > 0) {
y <- y[!sites.to.remove, ,drop = FALSE]
X.gam <- X.gam[!sites.to.remove[rep(1:M, each = nY)],,drop = FALSE]
X.eps <- X.eps[!sites.to.remove[rep(1:M, each = nY)],,drop = FALSE]
W <- W[!sites.to.remove,, drop = FALSE] # !!! Recent bug fix
V <- V[!sites.to.remove[rep(1:M, each = R)], ,drop = FALSE]
warning(paste(num.to.remove,"sites have been discarded because of missing data."), call. = FALSE)
}
list(y = y, X.gam = X.gam, X.eps = X.eps, W = W, V = V,
removed.sites = which(sites.to.remove))
})
# occuMulti
setMethod("getDesign", "unmarkedFrameOccuMulti",
function(umf, detformulas, stateformulas, maxOrder, na.rm=TRUE, warn=FALSE,
newdata=NULL, type="state")
{
#Format formulas
#Workaround for parameters fixed at 0
fixed0 <- stateformulas %in% c("~0","0")
stateformulas[fixed0] <- "~1"
stateformulas <- lapply(stateformulas,as.formula)
detformulas <- lapply(detformulas,as.formula)
#Generate some indices
S <- length(umf@ylist) # of species
if(missing(maxOrder)){
maxOrder <- S
}
z <- expand.grid(rep(list(1:0),S))[,S:1] # z matrix
colnames(z) <- names(umf@ylist)
M <- nrow(z) # of possible z states
# f design matrix
if(maxOrder == 1){
dmF <- as.matrix(z)
} else {
dmF <- model.matrix(as.formula(paste0("~.^",maxOrder,"-1")),z)
}
nF <- ncol(dmF) # of f parameters
J <- ncol(umf@ylist[[1]]) # max # of samples at a site
N <- nrow(umf@ylist[[1]]) # of sites
#Check formulas
if(length(stateformulas) != nF)
stop(paste(nF,"formulas are required in stateformulas list"))
if(length(detformulas) != S)
stop(paste(S,"formulas are required in detformulas list"))
if(is.null(siteCovs(umf))) {
site_covs <- data.frame(placeHolderSite = rep(1, N))
} else {
site_covs <- siteCovs(umf)
}
if(is.null(obsCovs(umf))) {
obs_covs <- data.frame(placeHolderObs = rep(1, J*N))
} else {
obs_covs <- obsCovs(umf)
}
#Add site covs to obs covs if we aren't predicting with newdata
# Record future column names for obsCovs
col_names <- c(colnames(obs_covs), colnames(site_covs))
# add site covariates at observation-level
obs_covs <- cbind(obs_covs, site_covs[rep(1:N, each = J),])
colnames(obs_covs) <- col_names
#Re-format ylist
index <- 1
ylong <- lapply(umf@ylist, function(x) {
colnames(x) <- 1:J
x <- cbind(x,site=1:N,species=index)
index <<- index+1
x
})
ylong <- as.data.frame(do.call(rbind,ylong))
ylong <- reshape(ylong, idvar=c("site", "species"), varying=list(1:J),
v.names="value", direction="long")
ylong <- reshape(ylong, idvar=c("site","time"), v.names="value",
timevar="species", direction="wide")
ylong <- ylong[order(ylong$site, ylong$time), ]
#Remove missing values
if(na.rm){
naSiteCovs <- which(apply(site_covs, 1, function(x) any(is.na(x))))
if(length(naSiteCovs>0)){
stop(paste("Missing site covariates at sites:",
paste(naSiteCovs,collapse=", ")))
}
naY <- apply(ylong, 1, function(x) any(is.na(x)))
naCov <- apply(obs_covs, 1, function(x) any(is.na(x)))
navec <- naY | naCov
sites_with_missingY <- unique(ylong$site[naY])
sites_with_missingCov <- unique(ylong$site[naCov])
ylong <- ylong[!navec,,drop=FALSE]
obs_covs <- obs_covs[!navec,,drop=FALSE]
no_data_sites <- which(! 1:N %in% ylong$site)
if(length(no_data_sites>0)){
stop(paste("All detections and/or detection covariates are missing at sites:",
paste(no_data_sites,collapse=", ")))
}
if(sum(naY)>0&warn){
warning(paste("Missing detections at sites:",
paste(sites_with_missingY,collapse=", ")))
}
if(sum(naCov)>0&warn){
warning(paste("Missing detection covariate values at sites:",
paste(sites_with_missingCov,collapse=", ")))
}
}
#Start-stop indices for sites
yStart <- c(1,1+which(diff(ylong$site)!=0))
yStop <- c(yStart[2:length(yStart)]-1,nrow(ylong))
y <- as.matrix(ylong[,3:ncol(ylong)])
#Indicator matrix for no detections at a site
Iy0 <- do.call(cbind, lapply(umf@ylist,
function(x) as.numeric(rowSums(x, na.rm=T)==0)))
#Save formatted covariate frames for use in model frames
#For predicting with formulas etc
site_ref <- site_covs
obs_ref <- obs_covs
#Assign newdata as the covariate frame if it is provided
if(!is.null(newdata)){
if(type == "state"){
site_covs <- newdata
} else if(type == "det"){
obs_covs <- newdata
}
}
#Design matrices + parameter counts
#For f/occupancy
fInd <- c()
sf_no0 <- stateformulas[!fixed0]
var_names <- colnames(dmF)[!fixed0]
dmOcc <- lapply(seq_along(sf_no0),function(i){
fac_col <- site_ref[, sapply(site_ref, is.factor), drop=FALSE]
mf <- model.frame(sf_no0[[i]], site_ref)
xlevs <- lapply(fac_col, levels)
xlevs <- xlevs[names(xlevs) %in% names(mf)]
out <- model.matrix(sf_no0[[i]],
model.frame(stats::terms(mf), site_covs, na.action=stats::na.pass, xlev=xlevs))
colnames(out) <- paste('[',var_names[i],'] ',
colnames(out), sep='')
fInd <<- c(fInd,rep(i,ncol(out)))
out
})
fStart <- c(1,1+which(diff(fInd)!=0))
fStop <- c(fStart[2:length(fStart)]-1,length(fInd))
occParams <- unlist(lapply(dmOcc,colnames))
nOP <- length(occParams)
#For detection
dInd <- c()
dmDet <- lapply(seq_along(detformulas),function(i){
fac_col <- obs_ref[, sapply(obs_ref, is.factor), drop=FALSE]
mf <- model.frame(detformulas[[i]], obs_ref)
xlevs <- lapply(fac_col, levels)
xlevs <- xlevs[names(xlevs) %in% names(mf)]
out <- model.matrix(detformulas[[i]],
model.frame(stats::terms(mf), obs_covs, na.action=stats::na.pass, xlev=xlevs))
colnames(out) <- paste('[',names(umf@ylist)[i],'] ',
colnames(out),sep='')
dInd <<- c(dInd,rep(i,ncol(out)))
out
})
dStart <- c(1,1+which(diff(dInd)!=0)) + nOP
dStop <- c(dStart[2:length(dStart)]-1,length(dInd)+nOP)
detParams <- unlist(lapply(dmDet,colnames))
#nD <- length(detParams)
#Combined
paramNames <- c(occParams,detParams)
nP <- length(paramNames)
mget(c("N","S","J","M","nF","fStart","fStop","fixed0","dmF","dmOcc","dmDet",
"dStart","dStop","y","yStart","yStop","Iy0","z","nOP","nP","paramNames"))
})
## occuMS
setMethod("getDesign", "unmarkedFrameOccuMS",
function(umf, psiformulas, phiformulas, detformulas, prm, na.rm=TRUE,
newdata=NULL, type="psi")
{
N <- numSites(umf)
S <- umf@numStates
T <- umf@numPrimary
R <- obsNum(umf)
J <- R / T
npsi <- S-1 #Number of free psi values
nphi <- S^2 - S #Number of free phi values
np <- S * (S-1) / 2 #Number of free p values
if(length(psiformulas) != npsi){
stop(paste(npsi,'formulas are required in psiformulas vector'))
}
if(is.null(phiformulas)){
if(prm == 'condbinom') {
phiformulas <- rep('~1',6)
} else {
phiformulas <- rep('~1',S^2-S)
}
} else if(T>1){
if(length(phiformulas)!=nphi){
stop(paste(nphi,'formulas are required in phiformulas vector. See data@phiOrder for help'))
}
}
if(length(detformulas) != np){
stop(paste(np,'formulas are required in detformulas vector'))
}
#get placeholder for empty covs if necessary
get_covs <- function(covs, length){
if(is.null(covs)){
return(data.frame(placeHolder = rep(1, length)))
}
return(covs)
}
#Function to create list of design matrices from list of formulas
get_dm <- function(formulas, covs, namevec, old_covs=NULL){
ref_covs <- covs
if(!is.null(old_covs)) ref_covs <- old_covs
fac_col <- ref_covs[, sapply(ref_covs, is.factor), drop=FALSE]
xlevs_all <- lapply(fac_col, levels)
apply_func <- function(i){
mf <- model.frame(as.formula(formulas[i]), ref_covs)
xlevs <- xlevs_all[names(xlevs_all) %in% names(mf)]
out <- model.matrix(as.formula(formulas[i]),
model.frame(stats::terms(mf), covs,
na.action=stats::na.pass, xlev=xlevs))
#improve these names
colnames(out) <- paste(namevec[i], colnames(out))
out
}
out <- lapply(seq_along(formulas), apply_func)
names(out) <- namevec
out
}
#Generate informative names for p
get_p_names <- function(S, prm){
if(prm=='condbinom'){
return(c('p[1]','p[2]','delta'))
}
inds <- matrix(NA,nrow=S,ncol=S)
inds <- lower.tri(inds,diag=TRUE)
inds[,1] <- FALSE
inds <- which(inds,arr.ind=TRUE) - 1
paste0('p[',inds[,2],inds[,1],']')
}
#Informative names for phi
get_phi_names <- function(np, prm){
if(prm=='condbinom'){
return(c(paste0('phi[',0:(S-1),']'),paste0('R[',0:(S-1),']')))
}
vals <- paste0('phi[',rep(0:(S-1),each=S),rep(0:(S-1),S),']')
vals <- matrix(vals,nrow=S)
diag(vals) <- NA
c(na.omit(as.vector(vals)))
}
#Informative names for psi
get_psi_names <- function(np, prm){
if(prm=='condbinom'){
return(c('psi','R'))
}
paste0('psi[',1:np,']')
}
#Get vector of parameter count indices from a design matrix list
get_param_inds <- function(dm_list, offset=0){
apply_func <- function(i){
rep(i, ncol(dm_list[[i]]))
}
ind_vec <- unlist(lapply(seq_along(dm_list), apply_func))
start_ind <- c(1,1+which(diff(ind_vec)!=0)) + offset
stop_ind <- c(start_ind[2:length(start_ind)]-1,length(ind_vec)+offset)
cbind(start_ind, stop_ind)
}
#Get param names from dm_list
get_param_names <- function(dm_list){
unlist(lapply(dm_list,colnames))
}
#Get observations with NAs across design matrices
get_na_inds <- function(formulas, covs){
dm_list <- get_dm(formulas, covs, rep("", length(formulas)))
dm_mat <- Reduce(cbind, dm_list)
which(apply(dm_mat, 1, function(x) any(is.na(x))))
}
site_covs <- get_covs(siteCovs(umf), N)
y_site_covs <- get_covs(yearlySiteCovs(umf), N*T)
#Add site covs to yearly site covs
if(!is.null(umf@siteCovs)){
y_site_covs <- cbind(y_site_covs,
site_covs[rep(1:N, each=T),,drop=FALSE])
}
obs_covs <- get_covs(obsCovs(umf), N*R)
#Add yearly site covs to obs covs
if(!is.null(umf@siteCovs) | !is.null(umf@yearlySiteCovs)){
obs_covs <- cbind(obs_covs,
y_site_covs[rep(1:(N*T), each=J),,drop=FALSE])
}
## in order to drop factor levels that only appear in last year,
## replace last year with NAs and use drop=TRUE
y_site_covs[seq(T,N*T,by=T),] <- NA
y_site_covs <- as.data.frame(lapply(y_site_covs, function(x) x[,drop = TRUE]))
#Actually just remove last year
y_site_covs <- y_site_covs[-seq(T,N*T,by=T),,drop=FALSE]
y <- getY(umf)
#Handle NAs
removed.sites <- NA
if(na.rm){
#Det
ylong <- as.vector(t(y))
miss_det_cov <- get_na_inds(detformulas, obs_covs)
miss_y <- which(is.na(ylong))
new_na <- miss_det_cov[!miss_det_cov%in%miss_y]
if(length(new_na)>0){
warning('Some observations removed because covariates were missing')
ylong[new_na] <- NA
y <- matrix(ylong,nrow=N,ncol=R,byrow=T)
}
#State
check_site_na <- function(yrow){
if(T==1) return(all(is.na(yrow)))
y_mat <- matrix(yrow, nrow=J)
pp_na <- apply(y_mat,2,function(x) all(is.na(x)))
if(any(pp_na)){
return(TRUE)
}
return(FALSE)
}
all_y_na <- which(apply(y,1, check_site_na ))
if(length(all_y_na)>0){
warning("Some sites removed because all y values in a primary period were missing")
}
miss_covs <- get_na_inds(psiformulas, site_covs)
if(length(miss_covs)>0){
warning("Some sites removed because site covariates were missing")
}
removed.sites <- sort(unique(c(all_y_na,miss_covs)))
if(T>1){
ysc_na <- get_na_inds(phiformulas, y_site_covs)
if(length(ysc_na) > 0){
stop("Some sites are missing yearly site covs")
}
}
if(length(removed.sites)>0){
ymap <- as.vector(t(matrix(rep(1:N,each=R),ncol=R,byrow=T)))
site_covs <- site_covs[-removed.sites,,drop=FALSE]
obs_covs <- obs_covs[!ymap%in%removed.sites,,drop=FALSE]
if(T>1){
ysc_map <- as.vector(t(matrix(rep(1:N,each=(T-1)),ncol=(T-1),byrow=T)))
y_site_covs <- y_site_covs[!ysc_map%in%removed.sites,,drop=FALSE]
}
y <- y[-removed.sites,,drop=FALSE]
N <- nrow(y)
}
}
site_ref <- site_covs
ysc_ref <- y_site_covs
obs_ref <- obs_covs
#Assign newdata as the covariate frame if it is provided
if(!is.null(newdata)){
if(type == "psi"){
site_covs <- newdata
} else if(type == "phi"){
y_site_covs <- newdata
} else if(type == "det"){
obs_covs <- newdata
}
}
dm_state <- get_dm(psiformulas, site_covs,
get_psi_names(length(psiformulas),prm), site_ref)
nSP <- length(get_param_names(dm_state))
state_ind <- get_param_inds(dm_state) #generate ind matrix in function
nPP <- 0; dm_phi <- list(); phi_ind <- c()
if(T>1){
dm_phi <- get_dm(phiformulas, y_site_covs,
get_phi_names(length(phiformulas),prm), ysc_ref)
nPP <- length(get_param_names(dm_phi))
phi_ind <- get_param_inds(dm_phi, offset=nSP)
}
dm_det <- get_dm(detformulas, obs_covs, get_p_names(S,prm), obs_ref)
det_ind <- get_param_inds(dm_det, offset=(nSP+nPP))
param_names <- c(get_param_names(dm_state),
get_param_names(dm_phi),
get_param_names(dm_det))
mget(c("y","dm_state","state_ind","nSP",
"dm_phi","phi_ind","nPP",
"dm_det","det_ind","param_names","removed.sites"))
})
# pcountOpen
#setMethod("getDesign", "unmarkedFramePCOorMMO",
setMethod("getDesign", "unmarkedFramePCO",
function(umf, formula, na.rm = TRUE)
{
aschar1 <- as.character(formula)
aschar2 <- as.character(formula[[2]])
aschar3 <- as.character(formula[[2]][[2]])
aschar4 <- as.character(formula[[2]][[2]][[2]])
iotaformula <- as.formula(paste(aschar1[1], aschar1[3]))
pformula <- as.formula(paste(aschar2[1], aschar2[3]))
omformula <- as.formula(paste(aschar3[1], aschar3[3]))
gamformula <- as.formula(paste(aschar4[1], aschar4[3]))
lamformula <- as.formula(formula[[2]][[2]][[2]][[2]])
y <- getY(umf)
M <- nrow(y)
T <- umf@numPrimary
J <- ncol(y) / T
R <- obsNum(umf) / T
delta <- umf@primaryPeriod
if(is.null(umf@yearlySiteCovs))
yearlySiteCovs <- data.frame(placeHolder = rep(1, M*T))
else
yearlySiteCovs <- umf@yearlySiteCovs
## add siteCovs in so they can be used as well
if(!is.null(umf@siteCovs)) {
sC <- umf@siteCovs[rep(1:M, each = T),,drop=FALSE]
yearlySiteCovs <- cbind(yearlySiteCovs, sC)
}
if(is.null(siteCovs(umf)))
siteCovs <- data.frame(placeHolder = rep(1, M))
else
siteCovs <- siteCovs(umf)
Xlam.mf <- model.frame(lamformula, siteCovs, na.action = NULL)
Xlam <- model.matrix(lamformula, Xlam.mf)
Xlam.offset <- as.vector(model.offset(Xlam.mf))
if(!is.null(Xlam.offset))
Xlam.offset[is.na(Xlam.offset)] <- 0
if(is.null(obsCovs(umf)))
obsCovs <- data.frame(placeHolder = rep(1, M*R*T))
else
obsCovs <- obsCovs(umf)
colNames <- c(colnames(obsCovs), colnames(yearlySiteCovs))
# Add yearlySiteCovs, which contains siteCovs
obsCovs <- cbind(obsCovs, yearlySiteCovs[rep(1:(M*T), each = R),])
colnames(obsCovs) <- colNames
if(!("obsNum" %in% names(obsCovs)))
obsCovs <- cbind(obsCovs, obsNum = as.factor(rep(1:(R*T), M)))
# Ignore last year of data
transCovs <- yearlySiteCovs[-seq(T, M*T, by=T),,drop=FALSE]
for(i in 1:ncol(transCovs))
if(is.factor(transCovs[,i]))
transCovs[,i] <- factor(transCovs[,i]) # drop unused levels
Xiota.mf <- model.frame(iotaformula, transCovs, na.action = NULL)
Xiota <- model.matrix(iotaformula, Xiota.mf)
Xiota.offset <- as.vector(model.offset(Xiota.mf))
if(!is.null(Xiota.offset))
Xiota.offset[is.na(Xiota.offset)] <- 0
Xp.mf <- model.frame(pformula, obsCovs, na.action = NULL)
Xp <- model.matrix(pformula, Xp.mf)
Xp.offset <- as.vector(model.offset(Xp.mf))
if(!is.null(Xp.offset))
Xp.offset[is.na(Xp.offset)] <- 0
Xgam.mf <- model.frame(gamformula, transCovs, na.action = NULL)
Xgam <- model.matrix(gamformula, Xgam.mf)
Xgam.offset <- as.vector(model.offset(Xgam.mf))