longke (version 0.1.0)

FPCA_trajectory: FPCA_trajectory

Description

Function used to perform functional principal component analysis (FPCA) for a single functional variable

Usage

FPCA_trajectory(data,...)

Value

A list containing two elements

fpca_target

A FPCA object

target_fit

A num.t x num.sub matrix containing the imputated longitudinal trajectories where num.t is the total number of the discrete measurement time and num.sub is the total number of subjects

Arguments

data

A long format data matrix containing 3 columns ordered by time, subject ID, variable where the measurement time of the longitudinal data should be discretized

...

Arguments to be passed to fdapace::FPCA

References

Carroll, C., Gajardo, A., Chen, Y., Dai, X., Fan, J., Hadjipantelis, P. Z., ... & Wang, J. L. (2020). fdapace: Functional data analysis and empirical dynamics. R package version 0.5, 4.

Yao, F., Müller, H. G., & Wang, J. L. (2005). Functional data analysis for sparse longitudinal data. Journal of the American statistical association, 100(470), 577-590.

Examples

t_all = 1:50
data = datagen(ntotal=350,ntest=50,t_all=t_all,t_split=25,seed=1)
data.sample = data$test[,c(1,2,3)]
# In this case, num.t=50 and num.sub=50 since we only used 50 subjects in the testing data
data.FPCA = FPCA_trajectory(data.sample,list(dataType='Sparse',
                error=FALSE, kernel='gauss', verbose=FALSE, nRegGrid=length(t_all)))
data.FPCA$target_fit