normal.params: Calculate the Normal Mean and Standard Deviation Using the...

In bayescount: Power Calculations and Bayesian Analysis of Count Distributions and FECRT Data using MCMC

Calculate the Normal Mean and Standard Deviation Using the Log-Normal Mean and Standard Deviation

Description

Function to calculate the equivalent values for the mean and standard deviation of a normal distribution from the mean and standard deviation of the log-normal distribution. Outputs from this function can be used with the dnorm() function, and with the normal distribution in JAGS.

Usage

normal.params(log.mean, log.sd, coeff.variation=sqrt(exp(log.sd^2)-1))

Arguments

log.mean	either a single value or vector of values for the mean of the lognormal distribution.
log.sd	either a single value or vector of values for the standard deviation of the lognormal distribution. Ignored if values are supplied for coeff.variation.
coeff.variation	either a single value or vector of values for the coefficient of dispersion.

Value

A list with elements representing the mean of the normal distribution, the standard deviation of the normal distribution, and the coefficient of variation.

See Also

lnormal.params

Examples

lmean <- 2.5
lsd <- 0.2
mean <- normal.params(lmean,lsd)[[1]]
sd <- normal.params(lmean,lsd)[[2]]

curve(dlnorm(x, lmean, lsd), from=0, to=25, col="blue")
curve(dnorm(x, mean, sd), from=0, to=25, add=TRUE, col="red")

bayescount documentation built on June 22, 2024, 12:24 p.m.