Description Usage Arguments Details Value Author(s) References See Also Examples
Provide a way to calculate approximate posterior standard deviations and fitted
values at any specified values for any combinations of elements of the spline
estimate of nonparametric functions from a slm
object, based on which
approximate Bayesian confidence intervals may be constructed.
1 2 |
object |
an object inheriting from class "slm", representing a semi-parametric nonlinear regression model fit. |
level |
set as 0.95, unused currently |
newdata |
an optional data frame on which the fitted spline estimate is to be evaluated. |
terms |
an optional vector of 0's and 1's collecting a combination of components, or a matrix of 0's and 1's collecting several combinations of components, in a fitted ssr object. All components include bases on the right side of ~ in the formula and reproducing kernels in the rk list. Note that the first component is usually a constant function if it is not specifically excluded in the formula. A value "1" at a particular position means that the component at that position is collected. Default is a vector of 1's, representing the overall fit. |
pstd |
an optional logic value. If TRUE (the default), the posterior standard deviations are calculated. Orelse, only the predictions are calculated. Computation required for posterior standard deviations could be intensive. |
... |
other arguments, currently unused. |
The standard deviation returned is based on approximate Bayesian confidence intervals as formulated in Wang (1998).
an object of class bCI
is returned, which is a list of length 2. Its first element is a matrix which contains predictions for
combinations specified by terms
, and second element is a matrix which contains
corresponding posterior standard deviations.
Chunlei Ke chunlei\_ke@pstat.ucsb.edu and Yuedong Wang yuedong@pstat.ucsb.edu
Wang, Y. (1998). Mixed-effects smoothing spline ANOVA. Journal of the Royal Statistical Society, Series B 60, 159-174.
1 2 3 4 5 6 7 8 9 10 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.