For each of a specified set of linear combinations of parameters from a
gnm
model, checks numerically whether the combination's
estimate is invariant to reparameterization of the model.
1 2  checkEstimable(model, combMatrix = diag(length(coef(model))),
tolerance = NULL)

model 
a model object of class 
combMatrix 
numeric: either a vector of length the same as

tolerance 
numeric: a threshold value for detection of
nonestimability. If 
A logical vector of length equal to the number of parameter combinations
tested; NA
where a parameter combination is identically zero.
David Firth and Heather Turner
Catchpole, E.A. and Morgan, B.J.T. (1997). Detecting parameter redundancy. Biometrika, 84, 187–196.
gnm
,
se
,
getContrasts
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  set.seed(1)
## Fit the "UNIDIFF" mobility model across education levels
unidiff < gnm(Freq ~ educ*orig + educ*dest +
Mult(Exp(educ), orig:dest), family = poisson,
data = yaish, subset = (dest != 7))
## Check whether multiplier contrast educ4  educ5 is estimable
ofInterest(unidiff) < pickCoef(unidiff, "[.]educ")
mycontrast < numeric(length(coef(unidiff)))
mycontrast[ofInterest(unidiff)[4:5]] < c(1, 1)
checkEstimable(unidiff, mycontrast)
## should be TRUE
## Check whether multiplier educ4 itself is estimable
mycontrast[ofInterest(unidiff)[5]] < 0
checkEstimable(unidiff, mycontrast)
## should be FALSE  only *differences* are identified here

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