Description Usage Arguments Value References
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 .
1 |
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). |
A matrix of predictions with rows representing each individual and columts representing predictions for each time point.
Ivanescu AE, Crainiceanu CM, Checkley W. Dynamic child growth prediction: A comparative methods approach. Statistical Modelling. 2017 Dec;17(6):468-93.
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