Description Usage Arguments Details Value References
DPFR uses penalized functional regression to incorporate all the history of Y up to time t* for subject i in a scalar-on-function regression. At each t* + j the response for subject i is the scalar Y[i,t*+j] and the functional predictor data consists of Y[i,1:t*]. We show how to obtain DPFR dynamic prediction using the function pfr from the refund R package and the gam function from the mgcv R package.
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 the example dataset). |
DPFR can use pfr for model fitting. The function pfr from the refund R package directly takes on a scalar response Y[i,t*+j] and a functional predictor Y[i,1:t*].
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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