#' Fit the model for binomial distribution
#'
#' @param data A data frame
#' @param response A character string. Response (e.g. "worm_count")
#' @param hybridIndex A vector of points representing the index used as x axis
#' @param paramBounds A vector of parameters (upper, lower, start) for the optimisation
#' @param config A list containing an optimizer (default: "optimx"), a method (default "bobyqa", "L-BFGS-B") and a control (default list(follow.on = TRUE))
#' @return A fit for binomial distributed data
#' @export
FitBasicNoAlphaBinomial <- function(data, response, hybridIndex, paramBounds, config){
print("Fitting model basic without alpha")
data$response <- data[[response]] # little trick
HI <- data[[hybridIndex]]
start <- list(L1 = paramBounds[["L1start"]])
fit <- bbmle::mle2(
response ~ dbinom(prob = MeanLoad(L1, L1, 0, HI),
size = 1),
data = data,
start = start,
lower = c(L1 = paramBounds[["L1LB"]]),
upper = c(L1 = paramBounds[["L1UB"]]),
optimizer = config$optimizer,
method = config$method,
control = config$control)
printConvergence(fit)
return(fit)
}
FitBasicAlphaBinomial <- function(data, response, hybridIndex, paramBounds, config){
print("Fitting model basic with alpha")
data$response <- data[[response]] # little trick
HI <- data[[hybridIndex]]
start <- list(L1 = paramBounds[["L1start"]],
alpha = paramBounds[["alphaStart"]])
fit <- bbmle::mle2(
response ~ dbinom(prob = MeanLoad(L1, L1, alpha, HI),
size = 1),
data = data,
start = start,
lower = c(L1 = paramBounds[["L1LB"]],
alpha = paramBounds[["alphaLB"]]),
upper = c(L1 = paramBounds[["L1UB"]],
alpha = paramBounds[["alphaUB"]]),
optimizer = config$optimizer,
method = config$method,
control = config$control)
printConvergence(fit)
return(fit)
}
FitAdvancedNoAlphaBinomial <- function(data, response, hybridIndex, paramBounds, config){
print("Fitting model advanced without alpha")
data$response <- data[[response]]
HI <- data[[hybridIndex]]
start <- list(L1 = paramBounds[["L1start"]],
L2 = paramBounds[["L2start"]])
fit <- bbmle::mle2(
response ~ dbinom(prob = MeanLoad(L1, L2, 0, HI),
size = 1),
data = data,
start = start,
lower = c(L1 = paramBounds[["L1LB"]],
L2 = paramBounds[["L2LB"]]),
upper = c(L1 = paramBounds[["L1UB"]],
L2 = paramBounds[["L2UB"]]),
optimizer = config$optimizer,
method = config$method,
control = config$control)
printConvergence(fit)
return(fit)
}
FitAdvancedAlphaBinomial <- function(data, response, hybridIndex, paramBounds, config){
print("Fitting model advanced with alpha")
data$response <- data[[response]]
HI <- data[[hybridIndex]]
start <- list(L1 = paramBounds[["L1start"]],
L2 = paramBounds[["L2start"]],
alpha = paramBounds[["alphaStart"]])
fit <- bbmle::mle2(
response ~ dbinom(prob = MeanLoad(L1, L2, alpha, HI),
size = 1),
data = data,
start = start,
lower = c(L1 = paramBounds[["L1LB"]],
L2 = paramBounds[["L2LB"]],
alpha = paramBounds[["alphaLB"]]),
upper = c(L1 = paramBounds[["L1UB"]],
L2 = paramBounds[["L2UB"]],
alpha = paramBounds[["alphaUB"]]),
optimizer = config$optimizer,
method = config$method,
control = config$control)
printConvergence(fit)
return(fit)
}
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