Calculates the maximum-likelihood estimate for a given likelihood function

This function calculates the maximum-likelihood estimate for a one or two parameter model

Usage

estimate_mle(log_likelihood, data_in, n_param, a_initial, b_initial = NULL)

Arguments

log_likelihood: Log-likelihood function in form function(x, a) (one parameter model) or function(x, a, b) (two parameter model)
data_in: Vector of observations to be evaluated in log_likelihood, with overall likelihood given by sum(log_likelihood)
n_param: Number of parameters in log_likelihood model
a_initial: Initial guess for parameter a
b_initial: Initial guess for parameter b (if a two parameter model, otherwise default is NULL)

Author

Adam Kucharski, Joshua W. Lambert

Examples

# example for a one parameter model
sim_data <- rnorm(100, 5, 2)
log_likelihood <- function(x, a) dnorm(x, a, 2, log = TRUE)
a_initial <- 4

estimate_mle(
  log_likelihood = log_likelihood,
  data_in = sim_data,
  n_param = 1,
  a_initial = 1
)
#> $estimate
#> [1] 4.939905
#> 
#> $log_likelihood
#> [1] -215.8972
#> 

# example for a two parameter model
sim_data <- rnorm(100, 5, 2)
log_likelihood <- function(x, a, b) dnorm(x, a, b, log = TRUE)
a_initial <- 4
b_initial <- 1

estimate_mle(
  log_likelihood = log_likelihood,
  data_in = sim_data,
  n_param = 2,
  a_initial = 1,
  b_initial = 1
)
#> $estimate
#>        a        b 
#> 5.377671 1.974567 
#> 
#> $log_likelihood
#> [1] -209.9133
#>