Description Usage Arguments Value Note Author(s) References Examples
For use with 'nmix'
and 'nmixnb'
models. This function takes the
information contained in an object returned by inla()
and uses the contents to create
fitted lambda values using the linear predictor for log(lambda), the input covariate values,
and samples from the posteriors of the model hyperparameters. Fitted values from the linear
predictor are exponentiated, by default, before being returned.
1 2 | inla.nmix.lambda.fitted(result, sample.size = 1000,
return.posteriors = FALSE, scale = "exp")
|
result |
The output object from a call to |
sample.size |
The size of the sample from the posteriors of the model hyperparameters. This sample size ends up being the size of the estimated posterior for a fitted lambda value. Default is 1000. Larger values are recommended. |
return.posterior |
A logical value for whether or not to return the full estimated
posteriors for each fitted value ( |
scale |
A character string, where the default string, |
fitted.summary |
A data frame with summaries of estimated posteriors of fitted lambda
values. The number of rows equals the number of rows in the data used to create the
|
fitted.posteriors |
A data frame containing samples that comprise the full estimated
posteriors of fitted values. The number of rows equals the number of rows in the data used to
create the |
This function is experimental.
Tim Meehan <tmeehan@audubon.org>
See documentation for families "nmix" and "nmixmb": inla.doc("nmix")
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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ## an example analysis of an N-mixture model using simulated data
## set parameters
n <- 75 # number of study sites
nrep.max <- 5 # number of surveys per site
b0 <- 0.5 # lambda intercept, expected abundance
b1 <- 2.0 # effect of x1 on lambda
a0 <- 1.0 # p intercept, detection probability
a2 <- 0.5 # effect of x2 on p
size <- 3.0 # size of theta
overdispersion <- 1 / size # for negative binomial distribution
## make empty vectors and matrix
x1 <- c(); x2 <- c()
lambdas <- c(); Ns <- c()
y <- matrix(NA, n, nrep.max)
## fill vectors and matrix
for(i in 1:n) {
x1.i <- runif(1) - 0.5
lambda <- exp(b0 + b1 * x1.i)
N <- rnbinom(1, mu = lambda, size = size)
x2.i <- runif(1) - 0.5
eta <- a0 + a2 * x2.i
p <- exp(eta) / (exp(eta) + 1)
nr <- sample(1:nrep.max, 1)
y[i, 1:nr] <- rbinom(nr, size = N, prob = p)
x1 <- c(x1, x1.i); x2 <- c(x2, x2.i)
lambdas <- c(lambdas, lambda); Ns <- c(Ns, N)
}
## bundle counts, lambda intercept, and lambda covariates
Y <- inla.mdata(y, 1, x1)
## run inla and summarize output
result <- inla(Y ~ 1 + x2,
data = list(Y=Y, x2=x2),
family = "nmixnb",
control.fixed = list(mean = 0, mean.intercept = 0, prec = 0.01,
prec.intercept = 0.01),
control.family = list(hyper = list(theta1 = list(param = c(0, 0.01)),
theta2 = list(param = c(0, 0.01)),
theta3 = list(prior = "flat",
param = numeric()))),
control.compute=list(config = TRUE)) # important argument
summary(result)
## get and evaluate fitted values
lam.fits <- inla.nmix.lambda.fitted(result, 5000)$fitted.summary
plot(lam.fits$median.lambda, lambdas)
round(sum(lam.fits$median.lambda), 0); sum(Ns)
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