AD.regression.pvalue: P-value of Anderson-Darling statistic for regression

In LiYao-sfu/EDFtest: Goodness of Fit Based on Empirical Distribution Function

Usage

AD.normal.regression.pvalue(a, x, neig = max(n, 400), verbose = FALSE)

AD.gamma.regression.pvalue(
  a,
  x,
  theta,
  link = "log",
  neig = max(n, 400),
  verbose = FALSE
)

AD.logistic.regression.pvalue(a, x, neig = max(n, 400), verbose = FALSE)

AD.laplace.regression.pvalue(a, x, neig = max(400, n), verbose = FALSE)

AD.weibull.regression.pvalue(a, x, neig = max(n, 400), verbose = FALSE)

AD.extremevalue.regression.pvalue(a, x, neig = max(n, 400), verbose = FALSE)

AD.exp.regression.pvalue(
  a,
  x,
  theta,
  link = "log",
  neig = max(n, 400),
  verbose = FALSE
)

Arguments

x	explanatory variables
verbose
w	Anderson-Darling statistic A^2 with a given distribution.

LiYao-sfu/EDFtest documentation built on Dec. 18, 2021, 4:35 a.m.