scc.FDAimage: Simultaneous confidence corridors for Image-on-Scalar...

In funstatpackages/FDAimage: Multivariate Spline Estimation and Inference for Image-on-Scalar Regression

Description

This function is used to construct the simultaneous confidence corridors (SCC) for the coefficient functions in image-on-scalar regression.

Usage

scc.FDAimage(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 Image-on-Scalar Regression" 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/FDAimage documentation built on Oct. 11, 2019, 12:50 p.m.