mchoice {qpcR}R Documentation

Selection of the best model by nested F-tests/likelihood ratios/Akaike weights

Description

Model selection by comparison of different models using

1) the maximum log likelihood value,
2) Akaike's Information Criterion (AIC),
3) bias-corrected Akaike's Information Criterion (AICc),
4) the estimated residual variance,
5) the p-value from a nested F-test on the residual variance,
6) the p-value from the likelihood ratio (chi-square),
7) the Akaike weights based on AIC and
8) the Akaike weights based on AICc.

The best model is chosen by 5), 6) or 7) and returned as a new model.

Usage

  mchoice(object, fctList = NULL, sig.level = 0.05, verbose = TRUE, 
          crit = c("ftest", "ratio", "weights"))

Arguments

object an object of class 'drc'.
fctList a list of functions to be analyzed, i.e. for a non-nested regime. Should also contain the original model.
sig.level the significance level for the nested F-test.
verbose logical. If TRUE, the result matrix is displayed in the console.
crit the criterium for model selection. Either 'ftest'/'ratio' for nested models or 'weights' for nested and non-nested models.

Details

Criteria 5) and 6) cannot be used for comparison unless the models are nested. Criterion 7), Akaike weights, can be used for nested and non-nested regimes. For criterion 1) the larger the better. For criteria 2), 3) and 4): the smaller the better. The best model is chosen either from the nested F-test, likelihood ratio or Akaike weights and returned as a new model. When using 'ftest'/'ratio' the corresponding nested function are analyzed automatically, i.e. b3/b4/b5; l3/l4/l5; w3/w4.

Value

A model of the best fit selected by the nested F-tests, likelihood ratios or Akaike weights. The new model has an additional list item 'retMat' with the result matrix from the criterion tests.

Author(s)

Andrej-Nikolai Spiess

See Also

LR, akaike.weights

Examples

m1 <- pcrfit(reps, 1, 2, l3())
### choose best model based on F-tests 
### on the corresponding nested models
m2 <- mchoice(m1)
summary(m2)  ### Converted to l5() model !
plot(m2, log = "")

### use Akaike weights on non-nested models
### compare to original model
m2 <- mchoice(m1, fctList = list(l3(), l5(), b3(), w4(), baro5()), crit = "weights")

[Package qpcR version 1.1-8 Index]