cmulti {detect} R Documentation

## Conditional Multinomial Maximum Likelihood Estimation

### Description

Conditional Multinomial Maximum Likelihood Estimation for different sampling methodologies.

### Usage

cmulti(formula, data, type = c("rem", "mix", "dis", "fmix"),
inits = NULL, method = "Nelder-Mead", ...)
cmulti.fit(Y, D, X=NULL, type=c("rem", "mix", "dis", "fmix"),

cmulti2.fit(Y, D1, D2, X1=NULL, X2=NULL,

## S3 method for class 'cmulti'
fitted(object, ...)
## S3 method for class 'cmulti'
model.frame(formula, ...)
## S3 method for class 'cmulti'
model.matrix(object, ...)
## S3 method for class 'cmulti'
predict(object, newdata = NULL,


### Arguments

 formula formula, LHS takes 2 matrices in the form of Y | D, RHS is either 1 or some covariates, see Examples. data data. type character, one of "rem" (removal sampling, homogeneous singing rates), "mix" and "fmix" (removal sampling, heterogeneous singing rates, "mix" implies that 'phi' is constant but 'c' can vary; "fmix" implies that 'c' is constant but 'phi' can vary), "dis" (distance sampling, half-normal detection function for point counts, circular area). For the predict method it is the type of prediction required; the default is on the scale of the linear predictors; the alternative "response" is on the scale of the response variable. Y this contains the cell counts. cmulti.fit requires that Y is a matrix (observations x intervals), dimensions and pattern in NAs must match that of D. cmulti2.fit requires that Y is a 3-dimensional array (observations x time intervals x distance intervals), dimensions and pattern in NAs must match that of D1 and D2. D, D1, D2 design matrices, that describe the interval endpoints for the sampling methodology, dimensions must match dimensions of Y. X, X1, X2 design matrices, X is the matrix with covariates for the removal/distance sampling parameters. X1 is the matrix with covariates for the removal, X2 is the matrix with covariates for the distance sampling parameters. inits optional initial values. method method for optim. object fitted model object. newdata optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used. ... additional options for optim.

### Details

Conditional Multinomial Maximum Likelihood Estimation for different sampling methodologies.

### Value

An object of class 'cmulti'.

Peter Solymos

### References

Solymos, P., Matsuoka, S. M., Bayne, E. M., Lele, S. R., Fontaine, P., Cumming, S. G., Stralberg, D., Schmiegelow, F. K. A. & Song, S. J., 2013. Calibrating indices of avian density from non-standardized survey data: making the most of a messy situation. Methods in Ecology and Evolution, 4, 1047–1058. <doi:10.1111/2041-210X.12106>

Solymos, P., Matsuoka, S. M., Cumming, S. G., Stralberg, D., Fontaine, P., Schmiegelow, F. K. A., Song, S. J., and Bayne, E. M., 2018. Evaluating time-removal models for estimating availability of boreal birds during point-count surveys: sample size requirements and model complexity. Condor, 120, 765–786. <doi:10.1650/CONDOR-18-32.1>

### Examples

simfun1 <- function(n = 10, phi = 0.1, c=1, tau=0.8, type="rem") {
if (type=="dis") {
Dparts <- matrix(c(0.5, 1, NA,
0.5, 1, Inf,
1, Inf, NA), 3, 3, byrow=TRUE)
D <- Dparts[sample.int(3, n, replace=TRUE),]
CP <- 1-exp(-(D/tau)^2)
} else {
Dparts <- matrix(c(5, 10, NA,
3, 5, 10,
3, 5, NA), 3, 3, byrow=TRUE)
D <- Dparts[sample.int(3, n, replace=TRUE),]
CP <- 1-c*exp(-D*phi)
}
k <- ncol(D)
P <- CP - cbind(0, CP[, -k, drop=FALSE])
Psum <- rowSums(P, na.rm=TRUE)
PPsum <- P / Psum
Pok <- !is.na(PPsum)
N <- rpois(n, 10)
Y <- matrix(NA, ncol(PPsum), nrow(PPsum))
Ypre <- sapply(1:n, function(i) rmultinom(1, N, PPsum[i,Pok[i,]]))
Y[t(Pok)] <- unlist(Ypre)
Y <- t(Y)
list(Y=Y, D=D)
}

n <- 200
x <- rnorm(n)
X <- cbind(1, x)

## removal, constant
vv <- simfun1(n=n, phi=exp(-1.5))
m1 <- cmulti(vv$Y | vv$D ~ 1, type="rem")
coef(m1)
## mixture, constant (mix and fmix are identical)
vv <- simfun1(n=n, phi=exp(-1.5), c=plogis(0.8))
m2 <- cmulti(vv$Y | vv$D ~ 1, type="mix")
coef(m2)
m2f <- cmulti(vv$Y | vv$D ~ 1, type="fmix")
coef(m2f)
## dist, constant
vv <- simfun1(n=n, tau=exp(-0.2), type="dis")
m3 <- cmulti(vv$Y | vv$D ~ 1, type="dis")
coef(m3)

## removal, not constant
log.phi <- crossprod(t(X), c(-2,-1))
vv <- simfun1(n=n, phi=exp(cbind(log.phi, log.phi, log.phi)))
m1 <- cmulti(vv$Y | vv$D ~ x, type="rem")
coef(m1)
## mixture, fixed phi, varying c
logit.c <- crossprod(t(X), c(-2,1))
vv <- simfun1(n=n, phi=exp(-1.5), c=plogis(cbind(logit.c, logit.c, logit.c)))
m2 <- cmulti(vv$Y | vv$D ~ x, type="mix")
coef(m2)
## mixture, varying phi, fixed c
log.phi <- crossprod(t(X), c(-2,-1))
vv <- simfun1(n=n, phi=exp(cbind(log.phi, log.phi, log.phi)), c=plogis(0.8))
m2f <- cmulti(vv$Y | vv$D ~ x, type="fmix")
coef(m2f)
## dist, not constant
log.tau <- crossprod(t(X), c(-0.5,-0.2))
vv <- simfun1(n=n, tau=exp(cbind(log.tau, log.tau, log.tau)), type="dis")
m3 <- cmulti(vv$Y | vv$D ~ x, type="dis")
coef(m3)

summary(m3)
coef(m3)
vcov(m3)
AIC(m3)
confint(m3)
logLik(m3)

## fitted values
plot(exp(log.tau), fitted(m3))

## prediction for new locations (type = 'rem')
ndf <- data.frame(x=seq(-1, 1, by=0.1))
summary(pr1 <- predict(m1, newdata=ndf, type="response"))
## turing singing rates into probabilities requires total duration
## 5 minutes used here
psing <- 1-exp(-5*pr1)
plot(ndf$x, psing, type="l", ylim=c(0,1)) ## prediction for new locations (type = 'dis') summary(predict(m3, newdata=ndf, type="link")) summary(pr3 <- predict(m3, newdata=ndf, type="response")) ## turing EDR into probabilities requires finite truncation distances ## r=0.5 used here (50 m) r <- 0.5 pdet <- pr3^2*(1-exp(-r^2/pr3^2))/r^2 plot(ndf$x, pdet, type="l", ylim=c(0,1))

## joint removal-distance estimation
## is not different from 2 orthogonal estimations

simfun12 <- function(n = 10, phi = 0.1, c=1, tau=0.8, type="rem") {
Flat <- function(x, DIM, dur=TRUE) {
x <- array(x, DIM)
if (!dur) {
x <- aperm(x,c(1,3,2))
}
dim(x) <- c(DIM[1], DIM[2]*DIM[3])
x
}
Dparts1 <- matrix(c(5, 10, NA,
3, 5, 10,
3, 5, NA), 3, 3, byrow=TRUE)
D1 <- Dparts1[sample.int(3, n, replace=TRUE),]
CP1 <- 1-c*exp(-D1*phi)
Dparts2 <- matrix(c(0.5, 1, NA,
0.5, 1, Inf,
1, Inf, NA), 3, 3, byrow=TRUE)
D2 <- Dparts2[sample.int(3, n, replace=TRUE),]
CP2 <- 1-exp(-(D2/tau)^2)
k1 <- ncol(D1)
k2 <- ncol(D2)
DIM <- c(n, k1, k2)
P1 <- CP1 - cbind(0, CP1[, -k1, drop=FALSE])
P2 <- CP2 - cbind(0, CP2[, -k2, drop=FALSE])
Psum1 <- rowSums(P1, na.rm=TRUE)
Psum2 <- rowSums(P2, na.rm=TRUE)
Pflat <- Flat(P1, DIM, dur=TRUE) * Flat(P2, DIM, dur=FALSE)
PsumFlat <- Psum1 * Psum2
PPsumFlat <- Pflat / PsumFlat
PokFlat <- !is.na(PPsumFlat)
N <- rpois(n, 10)
Yflat <- matrix(NA, ncol(PPsumFlat), nrow(PPsumFlat))
YpreFlat <- sapply(1:n, function(i) rmultinom(1, N, PPsumFlat[i,PokFlat[i,]]))
Yflat[t(PokFlat)] <- unlist(YpreFlat)
Yflat <- t(Yflat)
Y <- array(Yflat, DIM)
k1 <- dim(Y)[2]
k2 <- dim(Y)[3]
Y1 <- t(sapply(1:n, function(i) {
count <- rowSums(Y[i,,], na.rm=TRUE)
nas <- rowSums(is.na(Y[i,,]))
count[nas == k2] <- NA
count
}))
Y2 <- t(sapply(1:n, function(i) {
count <- colSums(Y[i,,], na.rm=TRUE)
nas <- colSums(is.na(Y[i,,]))
count[nas == k2] <- NA
count
}))
list(Y=Y, D1=D1, D2=D2, Y1=Y1, Y2=Y2)
}

## removal and distance, constant
vv <- simfun12(n=n, phi=exp(-1.5), tau=exp(-0.2))
res <- cmulti2.fit(vv$Y, vv$D1, vv$D2) res1 <- cmulti.fit(vv$Y1, vv$D1, NULL, "rem") res2 <- cmulti.fit(vv$Y2, vv$D2, NULL, "dis") ## points estimates are identical cbind(res$coef, c(res1$coef, res2$coef))
## standard errors are identical
cbind(sqrt(diag(res$vcov)), c(sqrt(diag(res1$vcov)),sqrt(diag(res2$vcov)))) ## removal and distance, not constant vv <- simfun12(n=n, phi=exp(cbind(log.phi, log.phi, log.phi)), tau=exp(cbind(log.tau, log.tau, log.tau))) res <- cmulti2.fit(vv$Y, vv$D1, vv$D2, X1=X, X2=X)
res1 <- cmulti.fit(vv$Y1, vv$D1, X, "rem")
res2 <- cmulti.fit(vv$Y2, vv$D2, X, "dis")

## points estimates are identical
cbind(res$coef, c(res1$coef, res2$coef)) ## standard errors are identical cbind(sqrt(diag(res$vcov)),
c(sqrt(diag(res1$vcov)),sqrt(diag(res2$vcov))))


[Package detect version 0.4-4 Index]