msep_sigma: Estimating the Mean Squared Error of Prediction for Sigma...
In cdv04/ACTR:
Description
Usage
Arguments
Details
Value
Examples
Description
6 linear models linear are compared using Cross Validation on the basis of
the least error of prediction (cf Details)
Usage
1
msep_sigma(PAC.df)
Arguments
PAC.df
A dataframe containing the weighted parameters in log10 scale (wMuA_lg, wMuC_lg, wSigmaA_lg, wSigmaC_lg)
Details
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)
Value
The function return the formula of the best selected model
Examples
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)
cdv04/ACTR documentation built on May 12, 2017, 1:15 a.m.
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Description Usage Arguments Details Value Examples
Description
6 linear models linear are compared using Cross Validation on the basis of the least error of prediction (cf Details)
Usage
1 | msep_sigma(PAC.df)
|
Arguments
PAC.df |
A dataframe containing the weighted parameters in log10 scale (wMuA_lg, wMuC_lg, wSigmaA_lg, wSigmaC_lg) |
Details
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)
Value
The function return the formula of the best selected model
Examples
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)
|
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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