Description Usage Arguments Details Value Author(s) References Examples
Binomial-Poisson and Binomial-ZIP models with single visit
1 2 3 4 5 6 7 8 9 10 11 12 | svabu(formula, data, zeroinfl = TRUE, area = 1, N.max = NULL,
inits, link.det = "logit", link.zif = "logit",
model = TRUE, x = FALSE, ...)
svabu.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, ...)
|
formula |
formula of the form |
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.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 |
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 |
... |
other arguments passed to the functions |
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).
An object of class 'svabu'.
Peter Solymos and Subhash Lele
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. 2011. 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 and Bayne, E. 2011. Abundance estimation in the presence of zero inflation and detection error using single visit data. Alberta Biodiversity Monitoring Institute, Alberta, Canada. Technical Report No. ABMI-20061, August 24, 2011. Available at: http://www.abmi.ca
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 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | 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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.