dlm: DLM

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

DLM uses more information than BENDY. While BENDY uses Y[i,1] and Y[i,t*] for dynamic prediction, DLM includes data Y[i,1:t*] which contains all the known history before time t* for a subject i. The DLM model fit is done at each point t* + j.

Usage

dlm(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 (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).

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.