intervals.snm {assist}R Documentation

Calculate Predictions and Approximate Posterior Standard Deviations for Spline Estimate From a snm 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 snm object, based on which approximate Bayesian confidence intervals may be constructed.


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



an object inheriting from class snm, representing a semi-parametric nonlinear mixed effects model fit.


a data frame on which the fitted spline estimates are to be evaluated. Only those predictors, referred in 'func' of 'snm' 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 snm. Default is NULL, where predictions are made at the same values used to fit the object.


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 of spline estimates in a fitted snm object. Note that in the cases of multiple functions, the order of all componets is collection of base functions for all functions followed by RK's. 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.


an optional logic value. If TRUE (the default), approximate posterior standard deviations are calculated. Orelse, only the predictions are calculated. Computation required for posterior standard deviations could be intensive.


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 and Wang (2001).


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


Ke, C. and Wang, Y. (2001). Semi-parametric Nonlinear Mixed Effects Models and Their Applications. JASA 96:1272-1298.

See Also

snm, plot.bCI, predict.ssr


## Not run: 

## extract normal dubjects
cort.nor<- horm.cort[horm.cort$type=="normal",]

## fit a self-modelling model with random effects<- snm(conc~b1+exp(b2)*f(time-alogit(b3)), 
  func=f(u)~list(periodic(u)), fixed=list(b1~1), 
  random=pdDiag(b1+b2+b3~1), data=cort.nor, 
  groups= ~ID,start=mean(cort.nor$conc))

## note the variable name of newdata
intervals(, newdata=data.frame(u=seq(0,1,len=50)))

## End(Not run)

[Package assist version 3.1.9 Index]