assist (version 2.0)

intervals.snr: Calculate Predictions and Approximate Posterior Standard Deviations for Spline Estimates From a snr Object

Description

Approximate posterior standard deviations are calculated for the spline estimate of nonparametric functions from a snr object, based on which approximate Bayesian confidence intervals may be constructed.

Usage

intervals.snr(object, level=0.95,newdata=NULL, terms=list(), pstd=TRUE,  ...)

Arguments

object

an object inheriting from class 'snr', representing a semi-parametric nonlinear regression model fit.

level

set as 0.95, unused currently

newdata

a data frame on which the fitted spline estimates are to be evaluated. Only those predictors, referred in 'func' of 'snr' fitting, have to be present. The variable names of the data frame should correspond to the function(s)' arguments appearing in t

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 ap

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.

Value

• a named list of objects of class "bCI" is returned, each component of which is a list of length 2. Within each component, the first element is a matrix which contains predictions for combinations specified by "terms", and the second element is a matrix which contains corresponding posterior standard deviations.

Details

The standard deviation returned is based on approximate Bayesian confidence intervals as formulated in Ke (2000).

References

Ke, C. (2000). Semi-parametric Nonlinear Regression and Mixed Effects Models. PhD thesis, University of California, Santa Barbara.

See Also

snr, plot.bCI, predict.ssr

Examples

data(CO2)
options(contrasts=rep("contr.treatment", 2))  

## get start values  
co2.fit1 <- nlme(uptake~exp(a1)*(1-exp(-exp(a2)*(conc-a3))), 
                 fixed=list(a1+a2~Type*Treatment,a3~1), 
                 random=a1~1, groups=~Plant, 
                 start=c(log(30),0,0,0,log(0.01),0,0,0,50),
                 data=CO2)

M <- model.matrix(~Type*Treatment, data=CO2)[,-1]

## fit a SNR model
co2.fit2 <- snr(uptake~exp(a1)*f(exp(a2)*(conc-a3)),
                func=f(u)~list(~I(1-exp(-u))-1,lspline(u, type="exp")),
                params=list(a1~M-1, a3~1, a2~Type*Treatment),
                start=list(params=co2.fit1$coe$fixed[c(2:4,9,5:8)]), data=CO2)

p.co2.fit2<- intervals(co2.fit2, newdata=data.frame(u=seq(0,10,len=50)))