model.select: model.select
In Michorlab/ACESO: A Cancer Evolution Simulation Optimizer

Description Usage Arguments Value

Comparison of different models using the following criteria: the log likelihood value, Akaike's or Bayes information criterion (AIC) or the estimated residual standard error.

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model.select(formula, data, fctList = NULL, type = "continuous",
  nested = FALSE, sorted = c("IC", "Res var", "Lack of fit", "no"),
  icfct = AIC, ...)

`formula`	a symbolic description of the model to be fit. Either of the form 'response ~ dose' or as a data frame with response values in first column and dose values in second column.
`data`	Concentration-response dataframe.
`fctList`	a list of dose-response functions to be compared.
`type`	a character string specifying the data type (parameter estimation will depend on the data type as different log likelihood function will be used).
`nested`	logical. TRUE results in F tests between adjacent models (in 'fctList'). Only sensible for nested models.
`sorted`	character string determining according to which criterion the model fits are ranked.
`icfct`	function supplying the information criterion to be used. "AIC" and "BIC" are the two options. "AIC" by default.
`...`	Additional arguments for the model fitting function. See `drm` for more info.

This function compares different models using the following criteria: the log likelihood value, Akaike's or Bayes information criterion (AIC) or the estimated residual standard error.

Michorlab/ACESO documentation built on June 4, 2021, 4:57 p.m.