scc.SVCMimage: Simultaneous confidence corridors for multivariate varying...

In funstatpackages/SVCMimage: Multivariate Spline Estimation and Inference for Varying Coefficient Models with Imaging Data

Description

This function is used to construct the simultaneous confidence corridors (SCC) for the coefficient functions in multivariate varying coefficient models.

Usage

scc.SVCMimage(Y, X, Z, V, Tr, d = 5, r = 1, lambda = 10^seq(-6, 6, by
  = 0.5), alpha0 = 0.05, nboot = 100, ksi = 0.01)

Arguments

Y	The response images of dimension n by npix, where n is the number of observed images and npix is the number of pixels/voxels in each image. Each row is an observation of the images.
X	The design matrix of dimension n by p, with an intercept. Each row is an observation vector.
Z	The cooridinates of dimension npix by two. Each row is the coordinates of a pixel/voxel.
V	The N by two matrix of vertices of a triangulation, where N is the number of vertices. Each row is the coordinates for a vertex.
Tr	The triangulation matrix of dimention nT by three, where nT is the number of triangles in the triangulation. Each row is the indices of vertices in V.
d	The degree of piecewise polynomials – default is 5, and -1 represents piecewise constant.
r	The smoothness parameter – default is 1, and 0 ≤ r < d.
lambda	The vector of the candidates of penalty parameter – default is grid points of 10 to the power of a sequence from -6 to 6 by 0.5.
alpha0	The nominal level of the SCC – default is 0.05.
nboot	The number of bootstraps – default is 100.

Details

This R package is the implementation program for manuscript entitled “Multivariate Spline Estimation and Inference for Varying Coefficient Models with Imaging Data" by Shan Yu, Guannan Wang, Li Wang and Lijian Yang.

Value

A list of the lower SCC and upper SCC for the coefficient functions.

funstatpackages/SVCMimage documentation built on May 17, 2019, 4:21 a.m.