Akaike information criterion / Bayesian information criterion

Computes AIC or BIC for one or more fitted Emax models. Lower values indicate a better-fitting model; values are only meaningful in comparison to other models fitted to the same response variable and dataset.

Usage

# S3 method for class 'emaxlogistic'
AIC(object, ..., k = 2)

# S3 method for class 'emaxlogistic'
BIC(object, ...)

# S3 method for class 'emaxnls'
AIC(object, ..., k = 2)

# S3 method for class 'emaxnls'
BIC(object, ...)

Arguments

object: An emaxnls or emaxlogistic object
...: Optionally, more fitted model objects
k: Penalty per parameter in the AIC

Value

If just one object is provided, a numeric value with the corresponding AIC (or BIC). If multiple objects are provided, a data.frame with rows corresponding to the objects and columns representing the number of parameters in the model (df) and the AIC or BIC.

Details

AIC applies a penalty of 2 * k to minus twice the log-likelihood, where k is the number of estimated parameters. BIC applies log(n) * k, making it more conservative than AIC in large samples. When multiple models are passed, any non-converging models are dropped with a warning.

Examples

mod_0 <- emax_nls(
  structural_model = rsp_1 ~ exp_1,
  covariate_model = list(E0 ~ 1, Emax ~ 1, logEC50 ~ 1),
  data = emax_df,
  opts = emax_nls_options(max_time = 10)
)
mod_1 <- emax_nls(
  structural_model = rsp_1 ~ exp_1,
  covariate_model = list(E0 ~ cnt_a, Emax ~ 1, logEC50 ~ 1),
  data = emax_df,
  opts = emax_nls_options(max_time = 10)
)

# calculate AIC for individual models
AIC(mod_0)
#> [1] 1281.131
AIC(mod_1)
#> [1] 603.6431

# calculate AIC for a sequence of models
AIC(mod_0, mod_1)
#>       df       AIC
#> mod_0  4 1281.1314
#> mod_1  5  603.6431

# calculate BIC for individual models
BIC(mod_0)
#> [1] 1297.097
BIC(mod_1)
#> [1] 623.6004

# calculate BIC for a sequence of models
BIC(mod_0, mod_1)
#>       df       BIC
#> mod_0  4 1297.0973
#> mod_1  5  623.6004

# emaxlogistic models
mod_b0 <- emax_logistic(
  structural_model = rsp_2 ~ exp_1,
  covariate_model = list(E0 ~ 1, Emax ~ 1, logEC50 ~ 1),
  data = emax_df,
  opts = emax_logistic_options(max_time = 10)
)
mod_b1 <- emax_logistic(
  structural_model = rsp_2 ~ exp_1,
  covariate_model = list(E0 ~ cnt_a, Emax ~ 1, logEC50 ~ 1),
  data = emax_df,
  opts = emax_logistic_options(max_time = 10)
)
AIC(mod_b0, mod_b1)
#>        df      AIC
#> mod_b0  3 444.4336
#> mod_b1  4 339.4698
BIC(mod_b0, mod_b1)
#>        df      BIC
#> mod_b0  3 456.4080
#> mod_b1  4 355.4356