test.gsvcm: Generalized Quasi-Likelihood Ratio (GQLR) test for...
In funstatpackages/gsvcm: Generalized Spatially Varying Coefficient Models

Description Usage Arguments Details Value Examples

test.gsvcm implements Generalized Quasi-Likelihood Ratio (GQLR) test using wild bootstrap.

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test.gsvcm(y, X, S, V, Tr, d, r, lambda = 10^seq(-6, 6, by = 0.5),
  test_iter = 1, family, off = 0, r.theta = c(2, 8), nB = 100,
  initial = 123, eps.sigma = 0.01)

`y`	The response of dimension `n` by one, where `n` is the number of observations.
`X`	The design matrix of dimension `n` by `p`, with an intercept. Each row is an observation vector.
`S`	The cooridinates of dimension `n` by two. Each row is the coordinates of an observation.
`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 2.
`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.
`test_iter`	The function to be tested – default is the first function, which corresponds to the first covariate.
`family`	The family object, specifying the distribution and link to use.
`off`	offset – default is 0.
`r.theta`	smoothness penalty parameter candidates – default is c(2,8).
`nB`	The number of boostrap replication – default is 100.
`initial`	The seed used for bootstrap sample in the GQLR test – default is 123.
`eps.sigma`	Error tolerance for the Pearson estimate of the scale parameter, which is as close as possible to 1, when estimating an additional parameter `theta` for negative binomial scenario – default is 0.01.

This R package is the implementation program for manuscript entitled "Generalized Spatially Varying Coefficinet Models" by Myungjin Kim and Li Wang.

The function returns a list with the following items:

`b.GQLR`	The GQLR test statistics based on sample.
`obs.GQLR`	The GQLR test statistics based on each boostrap iteration.
`pvalue`	The p-value for the GQLR test.