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#' Get Parameters for weibull distribution
#'
#' @param int_a : grid of first parameter of the distribution
#' @param med : median of real dataset
#' @param mu : mean of real dataset
#'
#' @return : list with parameter values of the distribution
#' @import stats
#' @export
#'
#' @examples
#' library(survMS)
get_param_weib = function(int_a = c(0.1,11), med, mu){
med_opt = function(x, med){
a <- x
abs(med - med_fct_avec_mu(a, mu))
}
res = optimise(med_opt, interval = int_a, med = med)
a = res$minimum
lambda = ((1/mu)*gamma(1+1/a))^(a)
return(list(a = a, lambda = lambda))
}
med_fct_avec_mu = function(a, mu=2325){
(log(2))^(1/a)*(mu/gamma(1+1/a))
}
#' Getting parameters of log-normal distribution
#'
#' @param int_a : grid of first parameter of the distribution
#' @param med : median of real dataset
#' @param mu : mean of real dataset
#'
#' @return : list with parameter values of the distribution
#' @import stats
#' @export
#'
#' @examples
#' library(survMS)
get_param_ln2 = function(int_a = c(0.1,11), med, mu){
mu_opt = function(x, med, mu){
a <- x
abs(mu - exp(log(med) + a^2/2))
}
res = optimise(mu_opt, interval = int_a, med = med, mu = mu)
a = res$minimum
lambda = log(mu) - a^2/2
return(list(a = a, lambda = lambda))
}
#' Getting parameters of log-normal distribution
#'
#' @param var : variance of real dataset
#' @param mu : mean of real dataset
#'
#' @return list with parameter values of the distribution
#' @export
#'
#' @examples
#' library(survMS)
get_param_ln = function(var = 170000, mu = 2325){
a2 = log(1 + var/mu^2)
lambda = log(mu) - (1/2)*a2
a = sqrt(a2)
return(list(a = a, lambda = lambda))
}
#' Getting parameters of exponential distribution
#'
#' @param int_a : grid of first parameter of the distribution
#' @param med : median of real dataset
#' @param mu : mean of real dataset
#'
#' @return : list with parameter values of the distribution
#' @import stats
#' @export
#'
#' @examples
#' library(survMS)
get_param_exp = function(int_a = c(0.000001,110), med, mu){
mu_med_opt = function(x, med, mu){
lambda <- x
abs(mu - 1/lambda) + abs(med - log(2)/lambda)
}
res = optimise(mu_med_opt, interval = int_a, med = med, mu = mu)
lambda = res$minimum
return(list(lambda = lambda))
}
# SurvTimesAHWeib = function(){
#
# }
#
# SurvTimesAHLN = function(){
#
# }
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