Obtain MLE estimates for model, given data.
Arguments
- data
A numeric vector containing data to fit model against.
- model_function
A function that runs the model and returns data.frame of value, organized as in
data.- optimizer_options
A list with options for the optimizer, as returned by get_optimizer_opts.
- file
A path to save the model fit. If the arguments to the fit call are found to be identical to those in the file, the model just gets loaded.
- ...
Extra parameters passed to the optimizer call.
Value
A CalmrFit object
Examples
# Make some fake data
df <- data.frame(g = "g", p1 = "3A>(US)", r1 = TRUE)
pars <- get_parameters(df, model = "RW1972")
pars$alphas["US"] <- 0.9
exper <- make_experiment(df, parameters = pars, model = "RW1972")
res <- run_experiment(exper, outputs = "responses")
responses <- results(res)$responses$value
# define model function
model_fun <- function(p, ex) {
np <- parameters(ex)
np[[1]]$alphas[] <- p
parameters(ex) <- np
results(run_experiment(ex))$responses$value
}
# Get optimizer options
optim_opts <- get_optimizer_opts(
model_pars = names(pars$alphas),
ll = rep(.05, 2), ul = rep(.95, 2),
optimizer = "optim", family = "identity"
)
optim_opts$initial_pars[] <- rep(.6, 2)
fit_model(responses, model_fun, optim_opts,
ex = exper, method = "L-BFGS-B",
control = list(maxit = 1)
)
#> Calmr model fit
#> --------------
#> Parameters:
#> A US
#> 0.4386228 0.8962406
#> --------------
#>
#> nLogLik: 11.029