summary.bgeva {bgeva} | R Documentation |
It takes a fitted 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,...)
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 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.
tableP |
It returns a table containing parametric estimates, their standard errors, z-values and p-values. |
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 p-value. |
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), 2372-2387.
Wood, S.N. (2013). On p-values for smooth components of an extended generalized additive model. Biometrika, 100(1), 221-228.
## see examples for bgeva