Generate plot showing residuals for Repeated Double Cross Validation for Partial Robust M-regression

1 | ```
plotresprm(prmdcvobj, optcomp, y, X, ...)
``` |

`prmdcvobj` |
object from repeated double-CV of PRM, see |

`optcomp` |
optimal number of components |

`y` |
data from response variable |

`X` |
data with explanatory variables |

`...` |
additional plot arguments |

After running repeated double-CV for PRM, this plot visualizes the residuals. The result is compared with predicted values obtained via usual CV of PRM.

A plot is generated.

Peter Filzmoser <P.Filzmoser@tuwien.ac.at>

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

`prm`

1 2 3 4 5 6 | ```
data(NIR)
X <- NIR$xNIR[1:30,] # first 30 observations - for illustration
y <- NIR$yGlcEtOH[1:30,1] # only variable Glucose
NIR.Glc <- data.frame(X=X, y=y)
res <- prm_dcv(X,y,a=4,repl=2)
plot4 <- plotresprm(res,opt=res$afinal,y,X)
``` |

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