intervals.snr {assist}R Documentation

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


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.


intervals.snr(object, level=0.95, newdata=NULL, terms=<see below>, pstd=TRUE, level=0.95, ...)


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 the opion func= of snr. Default is NULL, where predictions are made at the same values used to fit the object.
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.


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


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.


Chunlei Ke and Yuedong Wang


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


Arosa$csmonth <- (Arosa$month-0.5)/12
Arosa$csyear <- (Arosa$year-1)/45
# fit a simple sin-cos model
ozone.fit1 <- lm(thick~sin(2*pi*csmonth)+cos(2*pi*csmonth), data=Arosa)

# get start values
tmp <- atan(-ozone.fit1$coef[3]/ozone.fit1$coef[2])/(2*pi)
tmp <- log(tmp/(1-tmp))

# fit a SNR model
ozone.fit2 <- snr(thick~f1(csyear)+f2(csyear)*cos(2*pi*(csmonth+alogit(a))),
                   func=list(f1(x)+f2(x)~list(~x, cubic(x))), params=list(a~1),
                   data=Arosa, start=list(params=c(tmp)), spar="m")
p.ozone.fit2 <- intervals(ozone.fit2,data.frame(x=seq(0,1,len=100)),

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