| KE_fit {longke} | R Documentation |
KE_fit
Description
Function used to predict response trajectory by nonparametric kernel estimator
Usage
KE_fit(train,test,T1,T2,bw_time,bw_subj,alpha=0.05,seed=1,coefCI=FALSE)
Arguments
train |
A long format data matrix containing columns ordered by time, subject ID, response, predictor1, predictor2, ... where the measurement time of the longitudinal data should be discretized within T1. |
test |
A long format data matrix containing columns ordered by time, subject ID, response, predictor1, predictor2, ... where the measurement time of the longitudinal data should be discretized within T2. |
T1 |
A measurement time domain where the functional predictors are measured within |
T2 |
A measurement time domain where the functional response is of interest to predict |
bw_time |
(optimal) time bandwidth |
bw_subj |
(optimal) trajectory/subject bandwidth |
alpha |
confidence level for bootstrap CI of alpha_0, alpha_1, ... |
seed |
A random seed fo producing replicable bootstrap CI of alpha_0, alpha_1, ... |
coefCI |
Logical statement: TRUE to derive bootstrap CI of alpha0, alpha1, ... default is FALSE |
Value
A list containing 6 elements
testTraj |
A num.test x num.T2 matrix containing num.test subjects' trajectories where num.T2 is the total number of the discrete measurement time over T2 |
proxycoeff |
Coefficient estimation for the non-negative least square regression. From left to right they are alpha_0, alpha_1, ... |
fpca.fit |
A list containing FPCA fit for the functional predictors and the functional response |
w.hat |
A list containing num.test elements where ith element contains the proxy distance/similarity between ith testing subject and other training subjects |
bootCI.mean |
Bootstrap confidence interval of alpha_0, alpha_1, ... |
input.list |
A list containing the input arguments |
References
Wang S, Kim S, Cho H, Chang W. Nonparametric predictive model for sparse and irregular longitudinal data. (2023+)
Examples
t_all = 1:50
T1=c(1,25);T2=c(26,50)
data = datagen(ntotal=350,ntest=50,t_all=t_all,t_split=25,seed=1)
train = data$train
test = data$test
ke.fit = KE_fit(train=train,test=test,T1=T1,T2=T2,bw_time=1,bw_subj = 0.2)