added functionality to fit generalized bilinear mixed effects models, e.g., Poisson or logistic regression model (see the demo on Poisson regression).
Added an option so that
rho can either have an arc sine prior or a uniform prior.
rho is now restricted to lie between -.995 and +.995 in order to avoid the Markov chain from getting stuck at extreme values.
design_array so that it tries to figure out the number of nodes from the covariate information.
Fixed a bug so that
plot.ame works for both
Added a function
rrho_fc that updates the dyadic correlation
rho from its full conditional distribution, leading to faster-mixing Markov chains.
rZ_fc_bin to avoid numerical instabilities that lead to infinities.
ame_rep so that the GOF statistics are calculated for each rep.
Changed the default priors. For fitting non-normal models to sparse data, the priors on the covariance matrices
Suv have a smaller scale. Also changed the prior on
beta to be a g-prior on the non-intercept coefficients but a more diffuse prior on the intercept.
Changed the name of the covariance of
Moved the update of
Suv outside of the update for
Added an argument
offset to most Gibbs sampling functions. The function documentation indicates what things should be subtracted off (offset) for each update.
Fixed a bug in
plot.ame that created the wrong number of panels when
Added a secondary plotting parameter to
This update from 1.3 is a medium-sized step towards making
amen more modular, so that individuals can build their own custom AME models. There are some medium-sized changes detailed below. The vignette sill works fine, but please let me know if this screws up something you’ve been working on, or if you have strong objections to some of the changes.
rbeta_ab_fc: The function now takes as optional arguments a prior mean and a prior precision matrix for beta.
The default prior mean is zero and the default prior precision is much smaller than in 1.3.
rbeta_ab_fc: The function no longer takes as arguments all the items computed from the design array. Instead, these items are either attributes of the design array
X, or if they aren’t, they are calculated in this function. Since in most applications these items don’t change during the MCMC algorithm, you should precompute these items with the
design_array command or the
precomputeX: Precomputes various quantities from
X that will be repeatedly needed for the MCMC, and returns a new
X where the precomputed items are stored as attributes. If you construct your own design array, you should run
X<-precomputeX(X) (unless you are using the canned
ame_rep functions, as this do it automatically). The precomputation is also done automatically if you construct your design array with the
design_array: Derived quantities for the MCMC are precomputed using the
precomputeX function and stored as attributes in the constructed design array.
ame: There is now an additional parameter
prior, which is a list of hyperparameters (empty by default). Parameters for which priors may be set include
simZ, just changing some notation.
rs2_fc in two ways:
the diagonal of the error matrix is now part of the update; the function now takes optional prior parameter values.
Added a secondary triadic dependence gof statistic. Also modified
plot.ame to plot the additional statistic.
rZ functions to appropriately update the diagonal.
Changed all Wishart prior degrees of freedom to be two plus the number of parameters.
ame, use standard update for Sab whenever appropriate, and still use “specialty” updates in certain cases.
Created a new function
rSab_fc to update
Sab update functions, including
raSab_frn_fc all take optional parameters for the inverse-Wishart prior on
For the monk example in the vignette, the model is now fit without an intercept. There is not really any information about the intercept from these data.
The “model” parameter is now the “family” parameter and is now required
to prevent inadvertent fits of the normal model to binary data.
The order of the “model/family” and “R” parameter have been changed in the
rUV_fc function now takes arguments for the prior distribution over Psi, the covariance matrix of U and V.
zscores function now takes an optional argument for dealing with ties.
Incorporate a method to deal with missing values in predictors.
Methods for logistic regression, (overdispersed) Poisson and tobit models.
Allow for user-specified link functions.
Allow prior means for (
V) and a prior for
rho. The former will facilitate hierarchical or longitudinal modeling.
Incorporate prior specification into the
ame_rep wrapper function.
Incorporate more full prior specifications for the symmetric case.
rho using the marginal likelihood (integrating over the additive row and column effects).
Allow for user-defined GOF stats.
Should remove the plotting from the
ame wrapper, just call
rZ_cbin_fc don’t handle the dyadic correlation in missing data properly if data are missing asymmetrically.
Random reorderings can be avoided for FRN and RRL.
Simplify the update for
V. I don’t think the complicated method is saving much time.