compute_cons_MLE_ase: Constrained MLE and ASE
In cta: Contingency Table Analysis Based on ML Fitting of MPH Models

Description Usage Arguments Value Author(s) References See Also

Computes the constrained MLE of S_{0}(m) subject to equality constraints h_{0}(m) = 0 under the specified strata and fixed.strata configuration, and its associated asymptotic standard error. Here m is the vector of expected table counts, i.e. m = E(Y).

compute_cons_MLE_ase(y, strata, fixed.strata, h0.fct, h0.fct.deriv, S0.fct,
                     S0.fct.deriv, max.mph.iter, step, change.step.after,
                     y.eps, iter.orig, norm.diff.conv, norm.score.conv,
                     max.score.diff.iter)

`y`	Observed table counts in the contingency table(s), in vector form.
`strata`	Vector of the same length as `y` that gives the stratum membership identifier.
`fixed.strata`	The object that gives information on which stratum (strata) has (have) fixed sample sizes.
`h0.fct`	The constraint function h_{0}(\cdot) with respect to m, where m = E(Y), the vector of expected table counts.
`h0.fct.deriv`	The R function object that computes analytic derivative of the transpose of the constraint function h_{0}(\cdot) with respect to m. If `h0.fct.deriv` is not specified or `h0.fct.deriv = NULL`, numerical derivatives will be used.
`S0.fct`	The estimand function S_{0}(\cdot) with respect to m.
`S0.fct.deriv`	The R function object that computes analytic derivative of the estimand function S_{0}(\cdot) with respect to m. If `S0.fct.deriv` is not specified or `S0.fct.deriv = NULL`, numerical derivatives will be used.
`max.mph.iter, step, change.step.after, y.eps, iter.orig, norm.diff.conv, norm.score.conv, max.score.diff.iter`	The parameters used in `mph.fit`.

compute_cons_MLE_ase returns a vector of length two. The first element S0.fct.m_H0 is the constrained MLE of S_{0}(m) subject to equality constraints h_{0}(m) = 0, and the second element ase.S0.fct.m_H0 is the associated asymptotic standard error.

Qiansheng Zhu

Lang, J. B. (2004) Multinomial-Poisson homogeneous models for contingency tables, Annals of Statistics, 32, 340–383.

Zhu, Q. (2020) "On improved confidence intervals for parameters of discrete distributions." PhD dissertation, University of Iowa.

ci.table

cta documentation built on Aug. 24, 2021, 1:06 a.m.