Description Usage Arguments Details Value References
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.
1 | dpffr_pffr(data = HAZ, time = 0:15, hist.lngth = 7)
|
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). |
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.
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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