These functions take a fitted mvgam or jsdgam object and
return various useful summaries
# S3 method for mvgam
summary(object, include_betas = TRUE, smooth_test = TRUE, digits = 2, ...)# S3 method for mvgam_prefit
summary(object, ...)
# S3 method for mvgam
coef(object, summarise = TRUE, ...)
For summary.mvgam and summary.mvgam_prefit, a list is printed
on-screen showing the summaries for the model
For coef.mvgam, either a matrix of posterior coefficient distributions
(if summarise == FALSE or data.frame of coefficient summaries)
list object returned from mvgam
Logical. Print a summary that includes posterior summaries
of all linear predictor beta coefficients (including spline coefficients)?
Defaults to TRUE but use FALSE for a more concise summary
Logical. Compute estimated degrees of freedom and approximate
p-values for smooth terms? Defaults to TRUE, but users may wish to set
to FALSE for complex models with many smooth or random effect terms
The number of significant digits for printing out the summary;
defaults to 2.
Ignored
logical. Summaries of coefficients will be returned
if TRUE. Otherwise the full posterior distribution will be returned
Nicholas J Clark
summary.mvgam and summary.mvgam_prefit return brief summaries of the model's call, along with posterior intervals for
some of the key parameters in the model. Note that some smooths have extra penalties on the null space,
so summaries for the rho parameters may include more penalty terms than the number of smooths in
the original model formula. Approximate p-values for smooth terms are also returned,
with methods used for their
calculation following those used for mgcv equivalents (see summary.gam for details).
The Estimated Degrees of Freedom (edf) for smooth terms is computed using
either edf.type = 1 for models with no trend component, or edf.type = 0 for models with
trend components. These are described in the documentation for jagam. Experiments suggest
these p-values tend to be more conservative than those that might be returned from an equivalent
model fit with summary.gam using method = 'REML'
coef.mvgam returns either summaries or full posterior estimates for GAM component
coefficients