CvM.regression: Cramer-von Mises statistic for regression

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

Usage

CvM.normal.regression(y, x, fit.intercept = TRUE)

CvM.gamma.regression(y, x, fit, fit.intercept = TRUE, link = "log")

CvM.logistic.regression(y, x, fit.intercept = TRUE)

CvM.laplace.regression(y, x, fit.intercept = TRUE)

CvM.weibull.regression(y, x, fit.intercept = TRUE)

CvM.extremevalue.regression(y, x, fit.intercept = TRUE)

CvM.exp.regression(y, x, fit, fit.intercept = TRUE, link = "log")

Arguments

y	response variable
x	explanatory variables
parameter	estimates of regression parameters

Examples

n = 500
p = 3
beta = c(1,2,3)
x = rnorm(n*(p))
#x = c(rep(1,n),x)
x = matrix(x,n,p)
# OLS regression
mean = x%*%(beta)
#apply the link to get the mean
#generate data with that mean,

sd = 2
y =rnorm(n,mean=mean,sd=sd)
estimate.normal.regression(x=x,y=y,fit.intercept=TRUE)
CvM.normal.regression(x=x,y=y,fit.intercept=TRUE) # mean(cvm)=.06

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