scoresPACE: Estimates of functional Principal Component scores through...

In fda: Functional Data Analysis

Estimates of functional Principal Component scores through PACE

Description

Function scoresPACE estimates the functional Principal Component scores through Conditional Expectation (PACE)

Usage

  scoresPACE(data, time, covestimate, PC)

Arguments

data	a matrix object or list – If the set is supplied as a matrix object, the rows must correspond to argument values and columns to replications, and it will be assumed that there is only one variable per observation. If y is a three-dimensional array, the first dimension corresponds to argument values, the second to replications, and the third to variables within replications. – If it is a list, each element must be a matrix object, the rows correspond to argument values per individual. First column corresponds to time points and following columns to argument values per variable.
time	Array with time points where data was taken. length(time) == dim(data)[1]
covestimate	a list with the two named entries "cov.estimate" and "meanfd"
PC	an object of class "pca.fd"

Value

a matrix of scores with dimension nrow = nharm and ncol = ncol(data)

References

Ramsay, James O., Hooker, Giles, and Graves, Spencer (2009), Functional data analysis with R and Matlab, Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2005), Functional Data Analysis, 2nd ed., Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York.

Yao, F., Mueller, H.G., Wang, J.L. (2005), Functional data analysis for sparse longitudinal data, J. American Statistical Association, 100, 577-590.

fda documentation built on Sept. 30, 2024, 9:19 a.m.