# new.growth: recovery of growth velocity for a new subject In growthrate: Bayesian reconstruction of growth velocity

## Description

Computes the posterior mean and covariance kernel for a new subject having data at observation times newtobs different from tobs (apart from the first and the last). growth needs to be run first.

## Usage

 1 new.growth(newdata, newtobs, sigma, d, muhatcurve, Khat, tgrid)

## Arguments

 newdata Row vector of p heights for the new subject. newtobs Row vector of p observation times for the new subject (in increasing order; must include the first and last time points in tobs). sigma Infinitessimal standard deviation of the Brownian motion prior (same as in growth). d Number of time points on the fine grid. muhatcurve Output from growth. Khat Output from growth. tgrid The fine grid (output from growth).

## Value

 muhatcurvenew Posterior mean (on tgrid) for the new subject. Khatnew Posterior covariance kernel (on tgrid) for the new subject.

## Author(s)

Sara Lopez-Pintado and Ian W. McKeague

Maintainer: Ian W. McKeague <im2131@columbia.edu>

## References

Lopez-Pintado, S. and McKeague, I. W. (2013). Recovering gradients from sparsely observed functional data. Biometrics 69, 396-404 (2013). http://www.columbia.edu/~im2131/ps/growthrate-package-reference.pdf

## Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ## Not run: ## example using the height data provided in the package ## (after first running growth to obtain the output g): ## suppose a new subject has 5 observation times (including 0 and 7) data(height_data); tobs=c(0,1/3,2/3,1,3,4,7); d=200; sigma=1; g=growth(height_data,tobs,sigma,d); newtobs=c(0, 2, 3, 5, 7); newdata=t(as.vector(c(50,70,87,100,115))); ng=new.growth(newdata,newtobs,sigma,d,g\$muhatcurve,g\$Khat,g\$tgrid); ## plot of the posterior mean growth velocity for the new subject: plot(g\$tgrid,ng\$muhatcurvenew,xlab="Age (years)",ylab="Growth velocity (cms/year)"); ## End(Not run)

growthrate documentation built on May 2, 2019, 3:43 p.m.