mchoice {qpcR} | R Documentation |
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.
mchoice(object, fctList = NULL, sig.level = 0.05, verbose = TRUE, crit = c("ftest", "ratio", "weights"))
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. |
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.
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.
Andrej-Nikolai Spiess
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")