gamlps.object: Object resulting from the fit of a generalized additive...

In blapsr: Bayesian Inference with Laplace Approximations and P-Splines

Object resulting from the fit of a generalized additive model.

Description

An object returned by the gamlps function consists in a list with various components related to the fit of a generalized additive model with the Laplace-P-spline approach.

Value

A gamlps object has the following elements:

formula	The formula of the generalized additive model.
family	The chosen exponential family.
link	The link function used for the fit.
n	Sample size.
q	Total number of smooth terms.
K	Number of B-spline basis functions used for the fit.
penalty.order	Chosen penalty order.
latfield.dim	The dimension of the latent field. This is equal to the sum of the number of B-spline coefficients and the number of regression parameters related to the covariates in the linear part.
linear.coeff	Estimated linear regression coefficients. This is a matrix containing the posterior point estimate, standard deviation, z-score and lower/upper bounds of the credible interval.
spline.estim	The estimated B-spline coefficients. This is a list with q vectors of size K-1 representing the estimated B-spline amplitudes for each smooth term.
edf	Estimated effective degrees of freedom for each latent field variable.
Approx.signif	A matrix returning the observed test statistic and p-value for the approximate significance of smooth terms.
EDf	The estimated effective degrees of freedom of the smooth terms.
EDfHPD.95	95% HPD interval for the degrees of freedom of the smooth terms.
ED	The estimated degrees of freedom of the GAM model.
vmap	The maximum a posteriori of the (log) posterior penalty vector.
Cov.vmap	Covariance matrix of the (log) posterior penalty vector evaluated at vmap.
pen.family	The family of the posterior distribution for v. It is either "skew-normal" or "gaussian".
pendist.params	The parameterization for the posterior distribution of v. If the posterior of v belongs to the skew-normal family, then pendist.params is a matrix with as many rows as the number of smooth terms q. Each row contains the location, scale and shape parameter of the skew-normal distribution. If the posterior of v belongs to the Gaussian family, then pendist.params is a vector of length q, corresponding to vmap.
Covmaximum	The covariance matrix of the latent field evaluated at the posterior maximum value of the penalty vector.
latmaximum	The latent field value evaluated at the posterior maximum value of the penalty vector.
fitted.values	The fitted response values.
residuals	The response residuals.
r2.adj	The adjusted r-squared of the model indicating the proportion of the data variance explained by the model fit.
data	The data frame of the model.

Author(s)

Oswaldo Gressani [e-mail].

See Also

gamlps, print.gamlps, plot.gamlps

blapsr documentation built on Sept. 1, 2025, 5:10 p.m.