a90logit: Maximum Likelihood Parameter Estimation of Logistic...

In ipeglim: Imprecise Inferential Framework on Statistical Reasoning

Description

Logistic regression model is used for estimating the unknown population size using multiple data sources. The model was introduced in the study of Alho (1990) and two data sources with binary indication of a case are used.

Usage

  a90logit(formula, data, nlists = 2, tol = 1e-06,
    max.iter = 100)

Arguments

formula	a symbolic description of the model to be fit,
data	a data frame containing the variables in the model,
nlists	a number of data sources,
tol	distance-based absolute convergence tolerance. Default to 1e-6.
max.iter	the number of maximum iterations. Default to 1e2 for newton-rasphon method.

Value

An object of class a90logit 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 cfs.

TODO

Newton-Raphson method used in this algorithm can be replaced by optim().

TODO

This program is the initial implementation (without any code optimization).

Author(s)

Chel Hee Lee <[e-mail]>

References

Juha M. Alho (1990), Logistic Regression in Capture-Recapture Models, Biometrics, 46(3), pp. 623-635

See Also

see also

Examples

## Please see the vignette.

ipeglim documentation built on May 2, 2019, 4:31 p.m.