modelfit: Fit a specified model to Multiple Systems Estimation data

In SparseMSE/sparsemse: Multiple systems estimation for Sparse capture data

Description

This routine fits a specified model to multiple systems estimation data, taking account of the possibility of empty overlaps between observed lists.

Usage

modelfit(zdat, mX = NULL, check = T)

Arguments

zdat	Data matrix with t+1 columns. The first t columns, each corresponding to a particular list, are 0s and 1s defining the capture histories observed. The last column is the count of cases with that particular capture history. List names A, B, ... are constructed if not supplied. Where a capture history is not explicitly listed, it is assumed that it has observed count zero.
mX	A 2 \times k matrix giving the k two-list interactions to be included in the model. Each column of mX contains the numbers of the corresponding pair of lists. If mX = 0, then all two-list interactions are included. If mX = NULL, no interactions are included and the main effects model is fitted. If only one interaction is to be fitted, it may be specified as a vector of length 2, e.g mX=c(1,3) for interactions of list 1 and 3.
check	If check = T check first of all if the maximum likelihood estimate exists and is identifiable, using the routine checkident. If either condition fails, print an appropriate error message and return the error code.

Value

A list of components as below

fit Details of the fit of the specified model as output by glm. The Akaike information criterion is adjusted to take account of the number of parameters corresponding to empty overlaps.

emptyoverlaps Matrix with two rows, giving the list pairs within the model for which no cases are observed in common. Each column gives the indices of a pair of lists, with the names of the lists in the column name.

poisspempty the Poisson p-values of the empty overlaps.

Examples

data(NewOrl)
modelfit(NewOrl,c(1,3))

SparseMSE/sparsemse documentation built on May 7, 2019, 7:13 p.m.