detect (version 0.4-0)

svabu: Single visit N-mixture abundance models

Description

Binomial-Poisson, Binomial-NegBin, Binomial-ZIP, and BinomialZINB models with single visit.

Usage

svabu(formula, data, zeroinfl = TRUE, area = 1, N.max = NULL, inits, link.det = "logit", link.zif = "logit", model = TRUE, x = FALSE, distr = c("P", "NB"), ...)
svabu.fit(Y, X, Z, Q = NULL, zeroinfl = TRUE, area = 1, N.max = NULL, inits, link.det = "logit", link.zif = "logit", ...)
svabu_nb.fit(Y, X, Z, Q = NULL, zeroinfl = TRUE, area = 1, N.max = NULL, inits, link.det = "logit", link.zif = "logit", ...)
zif(x)
is.present(object, ...)
predictMCMC(object, ...)
svabu.step(object, model, trace = 1, steps = 1000, criter = c("AIC", "BIC"), test = FALSE, k = 2, control, ...)

Arguments

formula

formula of the form y ~ x | z, where y is a vector of observations, x is the set of covariates for the occurrence model, z is the set of covariates for the detection model. x can further expanded as x1 + zif(x2) into terms for the nonzero count data part (x1) and the zero inflation component (zif(x2)) using the zif special.

Y, X, Z, Q

vector of observation, design matrix for abundance model, design matrix for detection and design matrix for zero inflation model

data

data

area

area

N.max

maximum of true count values (for calculating the integral)

zeroinfl

logical, if the Binomial-ZIP model should be fitted

inits

initial values used by link{optim}

link.det, link.zif

link function for the detection and zero inflation parts of the model

model

a logical value indicating whether model frame should be included as a component of the returned value, or true state or detection model

x

logical values indicating whether the response vector and model matrix used in the fitting process should be returned as components of the returned value. For the function zif it is any object to be returned.

object

a fitted object.

trace

info returned during the procedure

steps

max number of steps

criter

criterion to be minimized (cAUC=1-AUC)

test

logical, if decrease in deviance should be tested

k

penalty to be used with AIC

control

controls for optimization, if missing taken from object

distr

character, abundance distribution: "P" for Poisson, "NB" for Negative Binomial.

...

other arguments passed to the functions

Value

An object of class 'svabu'.

Details

See Examples.

The right hand side of the formula must contain at least one continuous (i.e. non discrete/categorical) covariate. This is the necessary condition for the single-visit method to be valid and parameters to be identifiable. See References for more detailed description.

The Binomial-Poisson model is the single visit special case of the N-mixture model proposed by Royle (2004) and explained in Solymos et a. (2012) and Solymos and Lele (2016).

References

Royle, J. A. 2004. N-Mixture Models for Estimating Population Size from Spatially Replicated Counts. Biometrics, 60(1), 108--115.

Solymos, P., Lele, S. R. and Bayne, E. 2012. Conditional likelihood approach for analyzing single visit abundance survey data in the presence of zero inflation and detection error. Environmetrics, 23, 197--205.

Solymos, P., Lele, S. R. 2016. Revisiting resource selection probability functions and single-visit methods: clarification and extensions. Methods in Ecology and Evolution, 7, 196--205.

Examples

data(databu)

## fit BZIP and BP models
m00 <- svabu(Y ~ x1 + x5 | x2 + x5, databu[1:200,])

## print method
m00
## summary: CMLE
summary(m00)
## coef
coef(m00)
coef(m00, model="sta") ## state (abundance)
coef(m00, model="det") ## detection
coef(m00, model="zif") ## zero inflation (this is part of the 'true state'!)

## Not run: 
# ## Diagnostics and model comparison
# 
# m01 <- svabu(Y ~ x1 + x5 | x2 + x5, databu[1:200,], zeroinfl=FALSE)
# ## compare estimates (note, zero inflation is on the logit scale!)
# cbind(truth=c(2,-0.8,0.5, 1,2,-0.5, plogis(0.3)),
# "B-ZIP"=coef(m00), "B-P"=c(coef(m01), NA))
# 
# ## fitted
# plot(fitted(m00), fitted(m01))
# abline(0,1)
# 
# ## compare models
# AIC(m00, m01)
# BIC(m00, m01)
# logLik(m00)
# logLik(m01)
# ## diagnostic plot
# plot(m00)
# plot(m01)
# 
# ## Bootstrap
# 
# ## non parametric bootstrap
# ## - initial values are the estimates
# m02 <- bootstrap(m00, B=25)
# attr(m02, "bootstrap")
# extractBOOT(m02)
# summary(m02)
# summary(m02, type="cmle")
# summary(m02, type="boot")
# ## vcov
# vcov(m02, type="cmle")
# vcov(m02, type="boot")
# vcov(m02, model="sta")
# vcov(m02, model="det")
# ## confint
# confint(m02, type="cmle") ## Wald-type
# confint(m02, type="boot") ## quantile based
# ## parametric bootstrap
# simulate(m00, 5)
# m03 <- bootstrap(m00, B=5, type="param")
# extractBOOT(m03)
# summary(m03)
# 
# ## Model selection
# 
# m04 <- svabu(Y ~ x1 + x5 | x2 + x5 + x3, databu[1:200,], phi.boot=0)
# m05 <- drop1(m04, model="det")
# m05
# m06 <- svabu.step(m04, model="det")
# summary(m06)
# m07 <- update(m04, . ~ . | . - x3)
# m07
# 
# ## Controls
# 
# m00$control
# getOption("detect.optim.control")
# getOption("detect.optim.method")
# options("detect.optim.method"="BFGS")
# m08 <- svabu(Y ~ x1 + x5 | x2 + x5, databu[1:100,])
# m08$control ## but original optim method is retained during model selection and bootstrap
# ## fitted models can be used to provide initial values
# options("detect.optim.method"="Nelder-Mead")
# m09 <- svabu(Y ~ x1 + x5 | x2 + x5, databu[1:100,], inits=coef(m08))
# 
# ## Ovenbirds dataset
# 
# data(oven)
# ovenc <- oven
# ovenc[, c(4:8,10:11)][] <- lapply(ovenc[, c(4:8,10:11)], scale)
# moven <- svabu(count ~ pforest | observ + pforest + julian + timeday, ovenc)
# summary(moven)
# drop1(moven, model="det")
# moven2 <- update(moven, . ~ . | . - timeday)
# summary(moven2)
# moven3 <- update(moven2, . ~ . | ., zeroinfl=FALSE)
# summary(moven3)
# BIC(moven, moven2, moven3)
# ## End(Not run)