mhglm is used to fit a moment hierarchical generalized linear model.
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mhglm(formula, family = gaussian, data, weights, subset, na.action, start = NULL, etastart, mustart, offset, control = list(), model = TRUE, method = "mhglm.fit", x = FALSE, z = FALSE, y = TRUE, group = TRUE, contrasts = NULL) mhglm.fit(x, z, y, group, weights = rep(1, nobs), start = NULL, etastart = NULL, mustart = NULL, offset = rep(0, nobs), family = gaussian(), control = list(), intercept = TRUE)
are analogous to the similarly-named arguments for the
a list of parameters for controlling the fitting
the method to be used in fitting the model. The default
These functions are analogues of
glm.fit, meant to be used for fitting hierarchical
generalized linear models. A typical predictor has the form
response ~ terms + (reterms | group) where
response is the (numeric) response vector,
terms is a
series of terms which specifies a linear predictor for
reterms is a series of terms with random
coefficients (effects), and
group is a grouping factor; observations
with the same grouping factor share the same random effects.
Currently, only one random effect term is allowed, along with a single
level of hierarchy; random effect terms of the form
reterms | g1/.../gQ are not supported.
mhglm returns an object of class inheriting from
summary can be used to obtain or print a summary
of the results.
The generic accessor functions
residuals can be used to extract various useful features of the
value returned by
If the moment-based random effect covariance is not positive-semidefinite, then a warning will be issued, and a projection of the estimate to the positive-semidefinite cone will be used instead.
Patrick O. Perry
Perry, P. O. (2015) "Fast Moment-Based Estimation for Hierarchical Models", Preprint.
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