Description Usage Arguments Details Value Author(s) See Also Examples
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,
7) the Akaike weights based on AIC,
8) the Akaike weights based on AICc, and
9) the reduced chi-square, χ_ν^2, if replicates exist.
The best model is chosen by 5), 6), 8) or 9) and returned as a new model.
1 2 |
object |
an object of class 'pcrfit' or 'replist'. |
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 |
crit |
the criterium for model selection. Either |
do.all |
if |
... |
other parameters to be passed to |
Criteria 5) and 6) cannot be used for comparison unless the models are nested. Criterion 8), Akaike weights, can be used for nested and non-nested regimes, which also accounts for the reduced χ_ν^2. 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 (anova
), likelihood ratio (llratio
), corrected Akaike weights (akaike.weights
) or reduced χ_ν^2 (fitchisq
) and returned as a new model. When using "ftest"/"ratio"
the corresponding nested functions are analyzed automatically, i.e. b3/b4/b5/b6/b7; l3/l4/l5/l6/l7. If supplying nested models, please do this with ascending number of parameters.
A model of the best fit selected by one of the criteria above. The new model has an additional list item 'retMat' with a result matrix of the criterion tests.
Andrej-Nikolai Spiess
llratio
, akaike.weights
and fitchisq
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ## Choose best model based on F-tests
## on the corresponding nested models.
m1 <- pcrfit(reps, 1, 2, l4)
m2 <- mselect(m1)
summary(m2) ## Converted to l7 model!
## Use Akaike weights on non-nested models
## compare to original model.
m2 <- mselect(m1, fctList = list(l4, b5, cm3), crit = "weights")
summary(m2) ## Converted to b5 model!
## Try all sigmoidal models.
m3 <- pcrfit(reps, 1, 20, l4)
mselect(m3, do.all = TRUE) ## l7 wins by far!
## On replicated data using reduced chi-square.
ml1 <- modlist(reps, fluo = 2:5, model = l4)
rl1 <- replist(ml1, group = c(1, 1, 1, 1))
mselect(rl1, crit = "chisq") ## converted to l6!
|
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