twCoefLogitnorm
Estimating coefficients of logitnormal distribution from median and upper quantile
twCoefLogitnorm(median, quant, perc = 0.975,
...)median |
numeric vector: the median of the density function |
quant |
numeric vector: the upper quantile value |
perc |
numeric vector: the probability for which the quantile was specified |
... |
numeric matrix with columns c("mu","sigma")
rows correspond to rows in median, quant, and perc
Thomas Wutzler
# estimate the parameters, with median at 0.7 and upper quantile at 0.9
med = 0.7; upper = 0.9
med = 0.2; upper = 0.4
(theta <- twCoefLogitnorm(med,upper))
x <- seq(0,1,length.out = 41)[-c(1,41)] # plotting grid
px <- plogitnorm(x,mu = theta[1],sigma = theta[2]) #percentiles function
plot(px~x); abline(v = c(med,upper),col = "gray"); abline(h = c(0.5,0.975),col = "gray")
dx <- dlogitnorm(x,mu = theta[1],sigma = theta[2]) #density function
plot(dx~x); abline(v = c(med,upper),col = "gray")
# vectorized
(theta <- twCoefLogitnorm(seq(0.4,0.8,by = 0.1),0.9))
.tmp.f <- function(){
# xr = rlogitnorm(1e5, mu = theta["mu"], sigma = theta["sigma"])
# median(xr)
invlogit(theta["mu"])
qlogitnorm(0.975, mu = theta["mu"], sigma = theta["sigma"])
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.