Object summaries for Zero-truncated Poisson and Negative Binomial Regression models and its dispaly

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Description

summary is a generic function for summarizing results produced from Zero-truncated Poisson and Negative Binomial models with maximum likelihood estimation. Using the estimates of fitted object obj, the size of unknown population N and its 100*(1-alpha) confidence interval are calculated.

Usage

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  ## S3 method for class 'ztpreg'
 summary(object, alpha = 0.05, k = 2,
    j = 1, LM.test = FALSE, HT.est = FALSE, ...)

Arguments

object

a fitted model object,

alpha

a confidence level required (alpha=0.05 by default),

k

an integer, the penalty per parameter to be used; the k=2 is used for classical AIC (Akaike's information criteria),

j

an integer, for Lagrange multiplier test

...

other arguments

LM.test

Lagrange multiplier test

HT.est

Horvitz-Thompson estimator

Value

An object of class summary.ztpr with components including

tab

Formatted object to be printed including coefficients, standard errors, etc. Significance star is also printed with stars,

aic

Akaike Information Criteria,

N

Estimate of unknown population size,

VarN

Variance of N,

ciu

Upper bound of confidence interval,

cil

Lower bound of confidence interval.

Note

print.summary.ztpr is a generic function that displays all summaries of object generated from summary.ztpr.

Author(s)

Chel Hee Lee <gnustats@gmail.com>

See Also

AIC, extractAIC, printCoefmat

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