bendy: BENDY

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

Dynamic predictions with BENDY are obtained for each subject at each time point t* + j. For all subjects except subject i we consider data Y[-i,1] and Y[-i,t*], corresponding to first and last HAZ data from the known HAZ history, as predictive data for BENDY. We use all except the i-th subject to obtain the BENDY model fit, because we perform the leave one-curve out cross validation for prediction. The BENDY model fit is done n times to account for dynamic prediction for all n subjects. The lm function in R is used to fit the BENDY model. For each dynamic prediction, a BENDY model fit is used. Model fit and prediction are needed for each j for dynamic prediction at times t* + j .

Usage

bendy(data = HAZ, time = 0:15, hist.lgth = 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 example dataset).

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.