CvM.regression.pvalue: P-value of Cramer-von Mises statistic for regression

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

Usage

CvM.normal.regression.pvalue(w, x, neig = max(n, 400), verbose = FALSE)

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

CvM.logistic.regression.pvalue(w, x, neig = max(n, 400), verbose = FALSE)

CvM.laplace.regression.pvalue(w, x, neig = max(400, n), verbose = FALSE)

CvM.weibull.regression.pvalue(w, x, neig = max(n, 400), verbose = FALSE)

CvM.extremevalue.regression.pvalue(w, x, neig = max(n, 400), verbose = FALSE)

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

Arguments

w	Cramér-von Mises statistic W^2 with a given distribution.
x	explanatory variables
verbose

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