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Find out why there is a .p at the end of the model names in the
printed value of anova with multiple arguments.
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Consider the steps in reimplementing AGQ. First you need to find the
conditional modes, then evaluate the conditional variances, then step
out according to the conditional variance, evaluate the integrand
relative to the step.
The paper by Sophia Rabe-Hesketh et al describes a spherical form
of the Gauss-Hermite quadrature formula. Look that up and use it.
Because the Gauss-Hermite quadrature is formed as a sum, it is
necessary to divide the contributions to the deviance according to
the levels of the random effects. This means that it is only
practical to use AGQ when the response vector can be split into
sections that are conditionally independent. As far as I can see
this will mean a single grouping factor only.
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Allow for a matrix of responses in lmer so multiple fits can be
performed without needing to regenerate the model matrices.
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Modify the one-argument form of the anova method for lmer objects (yet
again) to calculate the F ratios. It is the df, not the ratio that
is controversial.
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Determine what a "coef" function should do for multiple, possibly
non-nested, grouping factors.
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- add nicer (more realistic?) pedigree examples and tests
- document print(<mer>) including an example print(<lmer>, corr = FALSE)
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