rightparetolognormal_plt function

Right-Pareto Lognormal coefficients of power-law transformed Right-Pareto Lognormal

Coefficients of a power-law transformed Right-Pareto Lognormal distribution

rightparetolognormal_plt(
  shape2 = 1.5,
  meanlog = -0.5,
  sdlog = 0.5,
  a = 1,
  b = 1,
  inv = FALSE
)

Arguments

• shape2, meanlog, sdlog: Shapes, mean and variance of the Right-Pareto Lognormal distribution respectively.
• a, b: constant and power of power-law transformation, defaults to 1 and 1 respectively.
• inv: logical indicating whether coefficients of the outcome variable of the power-law transformation should be returned (FALSE) or whether coefficients of the input variable being power-law transformed should be returned (TRUE). Defaults to FALSE.

Returns

Returns a named list containing

• coefficients: Named vector of coefficients

Comparing probabilites of power-law transformed transformed variables prightparetolognormal(3, shape2 = 3, meanlog = -0.5, sdlog = 0.5) coeff = rightparetolognormal_plt( shape2 = 3, meanlog = -0.5, sdlog = 0.5,a=5,b=7)$coefficients prightparetolognormal(5*3^7,shape2=coeff[["shape2"]],meanlog=coeff[["meanlog"]],sdlog=coeff[["sdlog"]])

prightparetolognormal(5*0.9^7,shape2 = 3, meanlog = -0.5, sdlog = 0.5) coeff = rightparetolognormal_plt(shape2 = 3, meanlog = -0.5, sdlog = 0.5,a=5,b=7, inv=TRUE)$coefficients prightparetolognormal(0.9,shape2=coeff[["shape2"]],meanlog=coeff[["meanlog"]],sdlog=coeff[["sdlog"]])

Details

If the random variable y is Right-Pareto Lognormal distributed with mean meanlog and standard deviation sdlog, then the power-law transformed variable

is Right-Pareto Lognormal distributed with $\frac{meanlog-log(a)}{b}, \frac{sdlog}{b}, shape2*b$.