gof.bootstrap {EDFtest} | R Documentation |
This function takes in an i.i.d. random sample, use MLE to estimate parameters of the assumed distribution, compute probability integral transforms, and computes Cramér-von Mises, Anderson-Darling and Watson statistics and their P-values using bootstrap method.
gof.uniform.bootstrap(x, M = 10000)
gof.normal.bootstrap(x, M = 10000)
gof.gamma.bootstrap(x, M = 10000)
gof.logistic.bootstrap(x, M = 10000)
gof.laplace.bootstrap(x, M = 10000)
gof.weibull.bootstrap(x, M = 10000)
gof.extremevalue.bootstrap(x, M = 10000)
gof.exp.bootstrap(x, M = 10000)
x |
A random sample. |
M |
Number of bootstrap, 10000 by default. |
Cramér-von Mises, Anderson-Darling and Watson statistics and their P-values.
gof.sandwich
for general distributions using Sandwich estimation
of covariance function;
gof
for generic functions using imhof
function.
x0=runif(n=100,min=-1,max=1)
gof.uniform.bootstrap(x0,M=100)
x1=rnorm(n=100,mean=0,sd=1)
gof.normal.bootstrap(x1,M=100)
x2=rgamma(n=100,shape=1,scale=1)
gof.gamma.bootstrap(x2,M=100)
x3=rlogis(n=100,location=0,scale=1)
gof.logistic.bootstrap(x3,M=100)
x4= rmutil::rlaplace(n=100,m=0,s=1)
gof.laplace.bootstrap(x4,M=100)
x5=rweibull(n=100,shape=1,scale=1)
gof.weibull.bootstrap(x5,M=100)
x5_log=log(x5)
gof.extremevalue.bootstrap(x5_log,M=100)
x6=rexp(n=100,rate=1/2)
gof.exp.bootstrap(x6,M=100)