Description Usage Arguments Details Value Author(s) References Examples

Computes the mean function and covariance kernel (over a fine grid of equispaced time points) of the posterior growth velocity for each subject, based on growth data (e.g., heights) at fixed observation times.

1 |

`data` |
Input matrix of size N (subjects) times n (observation times). Each column contains the heights (of all subjects) at a given observation time, each row contains the heights (at the observation times) for a given subject. |

`tobs` |
Row vector of n observation times (in increasing order, same for each subject). |

`sigma` |
A positive scalar representing the infinitessimal standard deviation of the tied-down Brownian motion in the prior. Can be selected by cross-validation. |

`d` |
Number of time points on the fine grid. |

The Bayesian reconstruction implemented here uses a prior growth velocity model that is specified by a general multivariate normal distribution at the n fixed observation times, and a tied-down Brownian motion (having infinitessimal standard deviation specified by `sigma`

) between the observation times.

The prior mean and prior precision matrix at the observation times are estimated using the data on N subjects. Clime (constrained L1 minimization) provides the estimate of the prior precision matrix, with the clime constraint parameter lambda selected by 5-fold cross validation using the likelihood loss function.

`muhatcurve` |
Posterior means of the growth velocities (for each subject) on the fine grid |

`Khat` |
Posterior covariance kernel of the growth velocities on the fine grid |

`tgrid` |
The fine grid of |

Sara Lopez-Pintado and Ian W. McKeague

Maintainer: Ian W. McKeague <[email protected]>

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
## Not run:
## example using the height data provided in the package
## there are 7 observation times (age in years):
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);
## Plot of the posterior mean and credible interval for a specific individual
indiv=1;
## posterior standard deviation (same for all subjects):
postsd=sqrt(diag(g$Khat));
plot(g$tgrid,g$muhatcurve[indiv,],type='l',
xlab="Age (years)",ylab="Growth velocity (cms/year)");
lines(g$tgrid,g$muhatcurve[indiv,]);
lines(g$tgrid,g$muhatcurve[indiv,]+2*postsd,lty=2);
lines(g$tgrid,g$muhatcurve[indiv,]-2*postsd,lty=2);
## Plot of a draw from the posterior growth velocity for a specific individual:
draw=rmvnorm(n=1, mean=g$muhatcurve[indiv,], sigma=g$Khat, method="chol");
plot(g$tgrid,draw,type='l',xlab="Age (years)",ylab="Growth
velocity (cms/year)");
## End(Not run)
``` |

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