intervals.slm {assist}R Documentation

Calculate Predictions and Posterior Standard Deviations of Spline Estimates From a slm Object


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 and Yuedong Wang


Wang, Y. (1998). Mixed-effects smoothing spline ANOVA. Journal of the Royal Statistical Society, Series B 60, 159-174.

See Also

slm, plot.bCI, predict.ssr


# fit a SLM model with random effects for dogs<-slm(y~group*time, rk=list(cubic(time), shrink1(group),,shrink1(group)),, 
    shrink1(group))), random=list(dog=~1), data=dog)


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