intervals.slm {assist} | R Documentation |
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.
intervals.slm(object, level=0.95, newdata=NULL, terms=<see below>, pstd=TRUE, level=0.95, ...)
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. |
level |
a numeric value set as 0.95. |
... |
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.
data(dog) # fit a SLM model with random effects for dogs dog.fit<-slm(y~group*time, rk=list(cubic(time), shrink1(group), rk.prod(kron(time-0.5),shrink1(group)),rk.prod(cubic(time), shrink1(group))), random=list(dog=~1), data=dog) intervals(dog.fit)