dpffr_pffr: DPFFR using pffr

In MatthewGrigsby/growthmetrics: Tools for applying statistic modelling and comparison techniques developed by researchers at HBGDKi and the Johns Hopkins University Center for Global NCD Research and Training

Description

DPFFR considers a functional response Y[i,(t*+1):length(month)] for each subject i. The predictor, Y[i,1:t*], is also functional, which makes the approach a dynamic function-on-function regression. We provide the implementation of DPFFR with the pffr function from the refund R package and the gam function from the mgcv R package.

Usage

dpffr_pffr(data = HAZ, time = 0:15, hist.lngth = 7)

Arguments

data	A matrix of values with each row representing an individual and each column representing measurements at each time point (e.g. column 1 is time point 1, etc.). Measurements are assumed to be taken at the same time intervals for every participant.
time	A vector of times. This equal the number of columts in the matrix provided (e.g. 0 through 15 months for the example dataset).
hist.lngth	The length of known history for the observed process. We use leave one-curve out cross validation for prediction (hist.lgth is 7 for the example dataset).

Details

When using pffr from the refund R package, the user directly refers to the functional response Y[i,(t*+1):length(month)] and functional predictor Y[i,1:t*]. The response and predictor data are stored as matrices.

Value

A matrix of predictions with rows representing each individual and columts representing predictions for each time point.

References

Ivanescu AE, Crainiceanu CM, Checkley W. Dynamic child growth prediction: A comparative methods approach. Statistical Modelling. 2017 Dec;17(6):468-93.

MatthewGrigsby/growthmetrics documentation built on May 25, 2019, 8:29 p.m.