intervals.nnr {assist} | R Documentation |

## Calculate Predictions and Approximate Posterior Standard Deviations for Spline Estimates From a nnr Object

### Description

Approximate posterior standard deviations are calculated for the spline estimate of
nonparametric functions from a `nnr`

object, based on which approximate Bayesian
confidence intervals may be constructed.

### Usage

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

### Arguments

`object` |
an object inheriting from class |

`newdata` |
a data frame on which the fitted spline estimates are to be evaluated.
Only those predictors, referred in |

`terms` |
an optional named list of vectors or matrices containing 0's and 1's collecting one or several combinations of the components of spline estimates in the fitted snr object. The length and names of the list shall match those of the unknown functions appearing in the 'snr' fit object. For the case of a single function, a vector of 0's and 1's can also be accepted. 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 fits of all unknown functions. |

`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. |

### Details

The standard deviation returned is based on approximate Bayesian confidence intervals as formulated in Ke and Wang (2002).

### 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@yahoo.com and Yuedong Wang yuedong@pstat.ucsb.edu

### References

Ke, C. and Wang, Y. (2002). Nonlinear Nonparametric Regression Models. Submitted.

### See Also

### Examples

```
## Not run:
## fit a generalized varying coefficient models
data(Arosa)
Arosa$csmonth <- (Arosa$month-0.5)/12
Arosa$csyear <- (Arosa$year-1)/45
ozone.fit <- nnr(thick~f1(csyear)+exp(f2(csyear))*f3(csmonth),
func=list(f1(x)~list(~I(x-.5),cubic(x)), f2(x)~list(~I(x-.5)-1,cubic(x)),
f3(x)~list(~sin(2*pi*x)+cos(2*pi*x)-1,lspline(x,type="sine0"))),
data=Arosa[Arosa$year%%2==1,], spar="m", start=list(f1=mean(thick),f2=0,f3=sin(csmonth)),
control=list(backfit=1))
x <- seq(0,1,len=50)
u <- seq(0,1,len=50)
## calculate Bayesian confidence limits for all components of all functions
p.ozone.fit <- intervals(ozone.fit, newdata=list(csyear=x,csmonth=u),
terms=list(f1=matrix(c(1,1,1,1,1,0,0,0,1),nrow=3,byrow=TRUE),
f2=matrix(c(1,1,1,0,0,1),nrow=3,byrow=TRUE),
f3=matrix(c(1,1,1,1,1,0,0,0,1),nrow=3,byrow=TRUE)))
plot(p.ozone.fit, x.val=x)
## End(Not run)
```

*assist*version 3.1.9 Index]