It takes a fitted bgeva
object produced by bgeva()
and produces some summaries from it.
1 2 3 4 
object 
A fitted 
s.meth 
Matrix decomposition used to determine the matrix root of the covariance matrix. See the documentation of 
sig.lev 
Significance level used for intervals obtained via posterior simulation. 
... 
Other arguments. 
As in the package mgcv
, based on the results of Wood (2013), ‘Bayesian pvalues’ are returned for the smooth terms. These have
better frequentist performance than their frequentist counterpart. Let \hat{\bf f}
and 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 V_f^{r} denote
the rank r pseudoinverse of V_f. The statistic used
is T=\hat{\bf f}^\prime {\bf V}_f^{r} \hat{\bf f}. This is
compared to a chisquared 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).
tableP 
It returns a table containing parametric estimates, their standard errors, zvalues and pvalues. 
tableNP 
It returns a table of nonparametric summaries for each smooth component including estimated degrees of freedom, estimated rank, approximate Wald statistic for testing the null hypothesis that the smooth term is zero, and pvalue. 
n 
Sample size. 
tau 
Tail parameter of the link function. 
formula 
The original GAM formula used. 
l.sp 
Number of smooth components. 
t.edf 
Total degrees of freedom of the estimated model. 
Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk
Marra G. and Wood S.N. (2011), Practical Variable Selection for Generalized Additive Models. Computational Statistics and Data Analysis, 55(7), 23722387.
Wood, S.N. (2013). On pvalues for smooth components of an extended generalized additive model. Biometrika, 100(1), 221228.
1  ## see examples for bgeva

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