Description Usage Arguments Details Value Author(s) References See Also Examples
Fits univariate N-mixture models
1 |
sp1 |
observation matrix for the species |
start |
initial values for the optimization process |
method |
optimization method passed to |
K |
truncation number of the infinite summations in the log-likelihood. Defaults to |
mixture |
latent abundance distribution specification. |
Xp |
model matrix for detection probabilities |
Xl |
model matrix for abundance parameter |
The function fits Royle's (2004) N-mixture model to data on species abundance collected at R sites over T time occasions. The model for observation on site i at time t can be specified as
Y_{it}|N_i ~ Bin(N_i,p_{it})
N_i ~ a count distribution with mean λ_i.
Here, users may define a Poisson, negative binomial or Neyman-A distributions for the latent abundances N_i.
An object of class uniNmix
and Nmix
, for which many methods are available (see methods(class = "uniNmix")
and methods(class = "Nmix")
)
Rafael A. Moral <rafael_moral@yahoo.com.br>, Clarice G. B. Demétrio and John Hinde
Royle, J.A. (2004) Models for estimating population size from spatially replicated counts. Biometrics 60:108-105.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ## simulating data with negative binomial latent abundances
R <- 10 # sites
T <- 10 # time occasions
lambda <- 5 # abundance parameter
p <- .3 # probability of detection
phi <- 1 # dispersion parameter
set.seed(1234); Ni <- rnbinom(R, mu=lambda, size=phi) # latent abundances
y <- matrix(0, ncol=T, nrow=R)
set.seed(1234); for(i in 1:R) y[,i] <- rbinom(T, Ni, p) # observed abundances
## fitting the Poisson N-mixture model
fitp <- Nmix(y, Xp=cbind(rep(1, R*T)), Xl=cbind(rep(1, R)), mixture="P", K=25)
## fitting the negative binomial N-mixture model
fitnb <- Nmix(y, Xp=cbind(rep(1, R*T)), Xl=cbind(rep(1, R)), mixture="NB", K=25)
## fitting the Neyman-A N-mixture model
fitna <- Nmix(y, Xp=cbind(rep(1, R*T)), Xl=cbind(rep(1, R)), mixture="NeymanA", K=25)
## likelihood-ratio test between Poisson and negbin models
anova(fitp, fitnb)
## comparing using AIC
lapply(list(fitp, fitnb, fitna), AIC)
## conditional posterior probability functions for abundances
plot(fitnb, posterior = TRUE)
## estimated abundances vs. true abundances
data.frame(getranef.uniNmix(fitnb), Ni)
|
Univariate N-mixture model fit by maximum likelihood
Univariate N-mixture model fit by maximum likelihood
Univariate N-mixture model fit by maximum likelihood
logLik(object) = -79.3753 (df = 2)
logLik(object2) = -77.3264 (df = 3)
Chisq d.f. p.value
1 4.097875 1 0.0429
[[1]]
[1] 162.7507
[[2]]
[1] 160.6528
[[3]]
[1] 163.071
Ni1 Ni
1 0.01 0
2 1.10 2
3 1.02 1
4 2.63 5
5 0.01 0
6 0.01 0
7 1.05 1
8 3.41 4
9 8.03 14
10 1.02 1
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