Description Usage Arguments Details Value Examples
6 linear models linear are compared using Cross Validation on the basis of the least error of prediction (cf Details)
1 | msep_sigma(PAC.df)
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PAC.df |
A dataframe containing the weighted parameters in log10 scale (wMuA_lg, wMuC_lg, wSigmaA_lg, wSigmaC_lg) |
The 6 linear models are :
mod1 : wSigmaC_lg ~ 1
mod2 : wSigmaC_lg ~ wSigmaA_lg
mod3 : wMuC_lg ~ wMuA_lg + wSigmaA_lg
mod4 : wSigmaC_lg ~ wSigmaA_lg + I(wSigmaA_lg^2)
mod5 : wSigmaC_lg ~ wSigmaA_lg + I(wSigmaA_lg^2) + wMuA_lg
mod6 : wSigmaC_lg ~ wSigmaA_lg + I(wSigmaA_lg^2) + wMuA_lg + I(wMuA_lg^2)
The function return the formula of the best selected model
1 2 3 4 | library(ACTR)
ciprKP <- subset_data(cipr, Class,6)
PAC.bt <- lapply(ciprKP.bt,est_PAC,Class)
errMod_sigma <- lapply(PAC.bt,msep_sigma)
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