https://github.com/cran/GsymPoint
Tip revision: 158c572b6991bd851086fb65dec3157d2ba15647 authored by Mónica López-Ratón on 31 October 2023, 18:30:02 UTC
version 1.1.2
version 1.1.2
Tip revision: 158c572
BoxCox_binormal_MLestimate.R
BoxCox_binormal_MLestimate <-
function (X0,X1,n0,n1)
{
# Arguments:
# X0: diagnostic marker in the healthy population
# X1: diagnostic marker in the diseased population
# n0: sample size of the healthy population
# n1: sample size of the diseased population
# Initial estimation for lambda:
parameters0 = c(n0,X0)
parameters1 = c(n1,X1)
parameters = c(parameters0,parameters1)
lambda_grid = seq(-1,1,length.out = 24)
yfun = minus_loglik(lambda_grid,parameters)
ind = which(yfun == min(yfun))
lambda_ini = mean(lambda_grid[ind])
output = solnp(pars = lambda_ini, fun = minus_loglik, parameters = parameters, control=list(trace = 0))
lambda_sol = output$pars
# exit = output$convergence
res <- lambda_sol
return(res)
}