pdfsqcdfstar: Calculate f^2(x)(1-F(x)).
In asymmetry.measures: Asymmetry Measures for Probability Density Functions

Description Usage Arguments Details Value Author(s) References See Also Examples

Return the product f^2(x)(1-F(x)).

1	pdfsqcdfstar(s,dist, p1,p2)

`s`	A scalar or vector: the x-axis grid points where the probability density function will be evaluated.
`dist`	Character string, used as a switch to the user selected distribution function (see details below).
`p1`	A scalar. Parameter 1 (vector or object) of the selected density.
`p2`	A scalar. Parameter 2 (vector or object) of the selected density.

Based on user-specified argument dist, the function returns the value of f^2(x)(1-F(x))dx, used in the definitions of ρ_p^*, ρ_p and their exact versions.

Supported distributions (along with the corresponding dist values) are:

weib: The weibull distribution is implemented as

f(s;p_1,p_2)= \frac{p_1}{p_2} ≤ft (\frac{s}{p_2}\right )^{p_1-1} \exp ≤ft \{- ≤ft (\frac{s}{p_2}\right )^{p_1} \right \}

with s ≥ 0 where p_1 is the shape parameter and p_2 the scale parameter.
lognorm: The lognormal distribution is implemented as

f(s) = \frac{1}{p_2s√{2π}}e^{-\frac{(log s -p_1)^2}{2p_2^2}}

where p_1 is the mean and p_2 is the standard deviation of the distirbution.
norm: The normal distribution is implemented as

f(s) = \frac{1}{p_2√{2 π}}e^{-\frac{ (s - p_1)^2 }{ 2p_2^2 }}

where p_1 is the mean and the p_2 is the standard deviation of the distirbution.
uni: The uniform distribution is implemented as

f(s) = \frac{1}{p_2-p_1}

for p_1 ≤ s ≤ p_2.
cauchy: The cauchy distribution is implemented as

f(s)=\frac{1}{π p_2 ≤ft \{1+( \frac{s-p_1}{p_2})^2\right \} }

where p_1 is the location parameter and p_2 the scale parameter.
fnorm: The half normal distribution is implemented as

2 f(s)-1

where

f(s) = \frac{1}{sd√{2 π} }e^{-\frac{s^2}{2 sd^2 }},

and sd=√{π/2}/p_1.
normmixt:The normal mixture distribution is implemented as

f(s)=p_1\frac{1}{p_2[2] √{2π} } e^{- \frac{ (s - p_2[1])^2}{2p_2[2]^2}} +(1-p_1)\frac{1}{p_2[4]√{2π}} e^{-\frac{(s - p_2[3])^2}{2p_2[4]^2 }}

where p1 is a mixture component(scalar) and p_2 a vector of parameters for the mean and variance of the two mixture components p_2= c(mean1, sd1, mean2, sd2).
skewnorm: The skew normal distribution with parameter p_1 is implemented as

f(s)=2φ(s)Φ(p_1s)

.
fas: The Fernandez and Steel distribution is implemented as

f(s; p_1, p_2) = \frac{2}{p_1+\frac{1}{p_1}} ≤ft \{ f_t(s/p_1; p_2) I_{\{s ≥ 0\}} + f_t(p_1s; p_2)I_{\{s<0 \}}\right \}

where f_t(x;ν) is the p.d.f. of the t distribution with ν = 5 degrees of freedom. p_1 controls the skewness of the distribution with values between (0, +∞) and p_2 denotes the degrees of freedom.
shash: The Sinh-Arcsinh distribution is implemented as

f(s;μ, p_1, p_2, τ) = \frac{ce^{-r^2/2}}{√{2π }} \frac{1}{p_2} \frac{1}{2} √{1+z^2}

where r=\sinh(\sinh(z)-(-p_1)), c=\cosh(\sinh(z)-(-p_1)) and z=((s-μ)/p2). p_1 is the vector of skewness, p_2 is the scale parameter, μ=0 is the location parameter and τ=1 the kurtosis parameter.

A vector containing the user selected density values at the user specified points s.

Dimitrios Bagkavos and Lucia Gamez Gallardo

R implementation and documentation: Dimitrios Bagkavos <dimitrios.bagkavos@gmail.com>, Lucia Gamez Gallardo <gamezgallardolucia@gmail.com>

Bagkavos D., Patil P.N., Wood A.T.A. (2016), A Numerical Study of the Power Function of a New Symmetry Test. In: Cao R., Gonzalez Manteiga W., Romo J. (eds) Nonparametric Statistics. Springer Proceedings in Mathematics and Statistics, vol 175, Springer.

r.sample, q.sample, p.sample

  selected.dens <- "weib" #select Weibull
  shape <- 2  # specify shape parameter
  scale <- 1 # specify scale parameter
  xout <- seq(0.1,5,length=50)  #design point
  pdfsqcdfstar(xout,selected.dens,shape,scale)  #return f^2(xout)F(xout)