Description Usage Arguments Value TODO Author(s) References See Also Examples
Zero-truncated Poisson and Negative Bionomial regression models are used for estimating the unknown population size using a single registration file in the stuides of Heijden (2003) and Cruffy (2008). Zero-truncated Negative Binomial is an alternative of zero-truncated Poisson model when an overdispersion is suspected.
1 2 3 |
formula |
a symbolic description of the model to be fit. |
data |
a data frame containg the variables in the model. |
dist |
a description of sampling model to be used in the model. |
method |
the method to be used in fitting model.
The default method uses |
hessian |
logical, Should a numerically differentiated Hessian matrix about regression parameters be returned? |
ztrunc |
logical, Is the sampling model used in the model assumed that zeros are truncated? |
... |
other arguments |
An object of class ztpr
with components including
formula |
formula used to be fitted, |
converged |
integer code which indicates a successful completion of optimization process, |
niters |
integer that indicates a number of iterations until convergence to estimates, |
cfs |
estimated regression coefficients, |
vcv |
estimated variance-covariance matrix of regression coefficients which is obtained by the inverse of Hessian matrix |
llk |
value of log-likelihood
function at |
Currently, predict
,
extractAIC
, residuals
, fitted
,
residual plot
, deviance
are not supported.
Support another method of 'L-BFGS-B'.
Chel Hee Lee <gnustats@gmail.com>
Peter GM van der Heijden, Rami Bustami, Maarten JLF Cruyff, Godfried Engbersen and Hans C van Houwelingen (2003), Point and interval estimation of the population size using the truncated Poisson regression model, Statistical Modelling, 3, pp. 305-322.
Cruyff MJ and van der Heijden (2008) Point and interval estimation of the population size using a zero-truncated negative binomial regression model, Biometrical Journal, 50(6), pp. 1035-1050.
Dankmar Boehning and Peter G. M. van der Heijden (2009), A Covarite adjustment for zero-truncated approaches to estimating the size of hidden and elusive populations, The Annals of Applied Statistics, 3(2), pp. 595-610.
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# dat <- simulateYX(N=1e3, Xreg=FALSE, param=2, ztrunc=TRUE)
# y <- dat$y
# m <- ztpr(formula=y~1, ztrunc=TRUE, dist="poisson")
# fit <- summary(m, HT.est=TRUE, LM.test=TRUE)
# mhat <- exp(fit$cfs)
# print(fit$LM.chisq)
# print(c(fit$N, fit$cil, fit$ciu))
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