approx.Z: Interpolating in smoothing spline Z-matrix columns

In lmeSplines: Add smoothing spline modelling capability to nlme.

Description

Function to interpolate the Z-matrix for LME smoothing spline fits from one set of values of the time covariate to another.

Usage

approx.Z(Z, oldtimes, newtimes)

Arguments

Z	Z-matrix with rows corresponding to the sorted unique values of the time covariate
oldtimes	original (sorted) values for time covariate, corresponding to the rows of Z
newtimes	new (sorted) values for time covariate

Details

Uses linear interpolation of each column of the Z-matrix, regarded as a function of time, with the times given by oldtimes.

Value

A matrix corresponding to an interpolated spline matrix. Can be used with smspline() for fitting LME splines with random effects corresponding to a different set of values of the time covariate than those represented in the data, or as part of the ‘newdata’ argument prediction from an existing model, to obtain predictions at points not represented in the data using predict.lme().

Note

Linear interpolation works well here because the spline basis functions are approximately piecewise linear.

Author(s)

Rod Ball [e-mail] www.scionresearch.com

See Also

smspline nlme

Examples

times1 <- 1:10
Zt1 <- smspline(~ times1)
times2 <- seq(1,10,by=0.1)
Zt2 <- approx.Z(Zt1,oldtimes=times1,newtimes=times2)

Example output

Loading required package: nlme

lmeSplines documentation built on May 2, 2019, 6:48 p.m.