Tests properties of an R implementation of a distribution, i.e., of all four of its “dpqr” functions.

## Usage

distCheck(fun = "norm", n = 1000, robust = TRUE, subdivisions = 100, ...)

## 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