cp2beta: Transformation between Parameters and B-spline Coefficients

In cSFM: Covariate-adjusted Skewed Functional Model (cSFM)

Description

Given a B-spline basis, transfer parameters (mean, variance, skewness) to the corresponding coefficients, and vice versa.

Usage

cp2beta(cp.list, Basis.list)
beta2cp(vec.beta, Basis.list)

Arguments

cp.list	list of parameters with names to be (mean, var, skew) corresponding to (mean, variance, skew)
Basis.list	list of basis matrices for the mean, variance and skewness
vec.beta	vector of coefficients

Value

cp2beta returns a vector of coefficients with the same form of vec.beta; beta2cp returns a list of parameters with the same form of cp.list

References

[1]. Meng Li, Ana-Maria Staicu and Howard D. Bondell (2013), Incorporating Covariates in Skewed Functional Data Models. http://www.stat.ncsu.edu/information/library/papers/mimeo2654_Li.pdf.

Examples

data(data.simulation)
# bivariate for mean and variance; univariate for shape parameter
cases = c(2,2,1)   
# 2 knots at time direction for each parameter
nknots.tp = c(2,2,2) 
# 2 knots at covariate direction for mean and variance
nknots.cp = c(2,2)   
basis.list <- lapply(1:3, function(k) 
  kpbb(DST$tp, DST$cp, nknots.tp = nknots.tp[k],
       nknots.cp= nknots.cp[k], sub.case=cases[k]))
cp.hat <- DST$pars # true parameters 
cp.hat$var <- exp(cp.hat$logvar) # follow the fomart stricely: (mean, var, skew)
beta <- cp2beta(cp.hat, basis.list)
cp.recover <- beta2cp(beta, basis.list)
norm(cp.hat$mean - cp.recover$mean) 

cSFM documentation built on May 29, 2017, 6:10 p.m.