bgeva object produced by bgeva() and produces some summaries from it.# S3 method for bgeva
summary(object,s.meth="svd",sig.lev=0.05,...)bgeva object as produced by bgeva().mvtnorm for further details.As in the package 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).
Consider also using the version of the model implemented in the gamlss() function of the
SemiParBIVProbit package, where p-value calculations are more rigorous.
Marra G. and Wood S.N. (2011), Practical Variable Selection for Generalized Additive Models. Computational Statistics and Data Analysis, 55(7), 2372-2387.
Wood, S.N. (2013). On p-values for smooth components of an extended generalized additive model. Biometrika, 100(1), 221-228.
bgevaObject, plot.bgeva