modelfit: Fit a specified model to multiple systems estimation data
In SparseMSE: 'Multiple Systems Estimation for Sparse Capture Data'

Description Usage Arguments Value Examples

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

1	modelfit(zdat, mX = NULL, check = TRUE)

`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 zero count.
`mX`	A 2 \times k matrix giving the k two-list parameters 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 parameters are included. If `mX = NULL`, no two-list parameters are included and the main effects model is fitted. If only one two-list parameter is to be fitted, it may be specified as a vector of length 2, e.g `mX=c(1,3)` for the parameter corresponding to lists 1 and 3.
`check`	If `check = TRUE` 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.

A list with 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 non-overlapping pairs.

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 parameters corresponding to non-overlapping pairs.