Description Usage Arguments Value See Also Examples
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | 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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.