Distribution check
dist-distCheck.RdTests properties of an R implementation of a distribution, i.e., of all four of its “dpqr” functions.
Arguments
- fun
a character string, the name of the distribution.
- n
an integer specifying the number of random variates to be generated.
- robust
logical flag, should robust estimates be used? By default
TRUE.- subdivisions
integer specifying the numbers of subdivisions in integration.
- ...
the distributional parameters.
Examples
distCheck("norm", mean = 1, sd = 1)
#>
#> Distribution Check for: norm
#> Call: distCheck(fun = "norm", mean = 1, sd = 1)
#>
#> 1. Normalization Check:
#> NORM 1 with absolute error < 1.6e-05
#>
#> 2. [p-pfun(qfun(p))]^2 Check:
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> p 0.001 0.01 0.1 0.5 0.9 0.99 0.999
#> P 0.001 0.01 0.1 0.5 0.9 0.99 0.999
#> RMSE
#> 2.205081e-17
#>
#> 3. r(1000) Check:
#> MEAN VAR
#> SAMPLE 0.991 0.896
#> X 1 with absolute error < 4.4e-07
#> X^2 2 with absolute error < 7.9e-07
#> MEAN VAR
#> EXACT 1 1
#>
#> normCheck rmseCheck meanvarCheck
#> TRUE TRUE FALSE
distCheck("lnorm", meanlog = 0.5, sdlog = 2, robust=FALSE)
#>
#> Distribution Check for: lnorm
#> Call: distCheck(fun = "lnorm", robust = FALSE, meanlog = 0.5, sdlog = 2)
#>
#> 1. Normalization Check:
#> NORM 0.9999976 with absolute error < 7.6e-05
#>
#> 2. [p-pfun(qfun(p))]^2 Check:
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> p 0.001 0.01 0.1 0.5 0.9 0.99 0.999
#> P 0.001 0.01 0.1 0.5 0.9 0.99 0.999
#> RMSE
#> 2.205081e-17
#>
#> 3. r(1000) Check:
#> MEAN VAR
#> SAMPLE 9.1 2170
#> X 12.18247 with absolute error < 0.0012
#> X^2 8103.065 with absolute error < 0.64
#> MEAN VAR
#> EXACT 12.2 7950
#>
#> normCheck rmseCheck meanvarCheck
#> TRUE TRUE FALSE
## here, true E(X) = exp(mu + 1/2 sigma^2) = exp(.5 + 2) = exp(2.5) = 12.182
## and Var(X) = exp(2*mu + sigma^2)*(exp(sigma^2) - 1) = 7954.67