intervals.slm {assist} R Documentation

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

### Description

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.

### Usage

```## S3 method for class 'slm'
intervals(object, level=0.95, newdata=NULL, terms, pstd=TRUE, ...)
```

### Arguments

 `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.

### Details

The standard deviation returned is based on approximate Bayesian confidence intervals as formulated in Wang (1998).

### Value

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.

### Author(s)

Chunlei Ke chunlei\_ke@pstat.ucsb.edu and Yuedong Wang yuedong@pstat.ucsb.edu

### References

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

`slm`, `plot.bCI`, `predict.ssr`

### Examples

```## Not run:
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)

## End(Not run)
```

[Package assist version 3.1.7 Index]