bgeva object produced by bgeva() and produces some summaries from it.## S3 method for class 'bgeva':
summary(object,s.meth="svd",sig.lev=0.05,...)bgeva object as produced by bgeva().mvtnorm for further details.mgcv, based on the results of Wood (2013), `Bayesian p-values' are returned for the smooth terms. These have
better frequentist performance than their frequentist counterpart. Let $\hat{\bf f}$
and ${\bf V}_f$ denote the vector of values of a smooth term evaluated at the original covariate values and the
corresponding Bayesian covariance matrix, and let ${\bf V}_f^{r-}$ denote
the rank $r$ pseudoinverse of ${\bf V}_f$. The statistic used
is $T=\hat{\bf f}^\prime {\bf V}_f^{r-} \hat{\bf f}$. This is
compared to a chi-squared distribution with degrees of freedom given by $r$, which is obtained by
biased rounding of the estimated degrees of freedom. See Wood (2013) for further details.
Note that covariate selection can also be achieved using a single penalty shrinkage approach as shown in Marra and Wood (2011).bgevaObject, plot.bgeva