Can We Trust Score Plots?

Research output: Contribution to journal › Journal article › peer-review

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Can We Trust Score Plots? / Bevilacqua, Marta; Bro, Rasmus.

In: Metabolites, Vol. 10, No. 7, 278, 2020.

Research output: Contribution to journal › Journal article › peer-review

Harvard

Bevilacqua, M & Bro, R 2020, 'Can We Trust Score Plots?', Metabolites, vol. 10, no. 7, 278. https://doi.org/10.3390/metabo10070278

APA

Bevilacqua, M., & Bro, R. (2020). Can We Trust Score Plots? Metabolites, 10(7), [278]. https://doi.org/10.3390/metabo10070278

Vancouver

Bevilacqua M, Bro R. Can We Trust Score Plots? Metabolites. 2020;10(7). 278. https://doi.org/10.3390/metabo10070278

Author

Bevilacqua, Marta ; Bro, Rasmus. / Can We Trust Score Plots?. In: Metabolites. 2020 ; Vol. 10, No. 7.

Bibtex

@article{6b06ef7a5a4a4a2d8641890efa3fd245,
title = "Can We Trust Score Plots?",
abstract = "In this paper, we discuss the validity of using score plots of component models such as partial least squares regression, especially when these models are used for building classification models, and models derived from partial least squares regression for discriminant analysis (PLS-DA). Using examples and simulations, it is shown that the currently accepted practice of showing score plots from calibration models may give misleading interpretations. It is suggested and shown that the problem can be solved by replacing the currently used calibrated score plots with cross-validated score plots.",
author = "Marta Bevilacqua and Rasmus Bro",
year = "2020",
doi = "10.3390/metabo10070278",
language = "English",
volume = "10",
journal = "Metabolites",
issn = "2218-1989",
publisher = "M D P I AG",
number = "7",

}

RIS

TY - JOUR

T1 - Can We Trust Score Plots?

AU - Bevilacqua, Marta

AU - Bro, Rasmus

PY - 2020

Y1 - 2020

N2 - In this paper, we discuss the validity of using score plots of component models such as partial least squares regression, especially when these models are used for building classification models, and models derived from partial least squares regression for discriminant analysis (PLS-DA). Using examples and simulations, it is shown that the currently accepted practice of showing score plots from calibration models may give misleading interpretations. It is suggested and shown that the problem can be solved by replacing the currently used calibrated score plots with cross-validated score plots.

AB - In this paper, we discuss the validity of using score plots of component models such as partial least squares regression, especially when these models are used for building classification models, and models derived from partial least squares regression for discriminant analysis (PLS-DA). Using examples and simulations, it is shown that the currently accepted practice of showing score plots from calibration models may give misleading interpretations. It is suggested and shown that the problem can be solved by replacing the currently used calibrated score plots with cross-validated score plots.

U2 - 10.3390/metabo10070278

DO - 10.3390/metabo10070278

M3 - Journal article

C2 - 32650451

VL - 10

JO - Metabolites

JF - Metabolites

SN - 2218-1989

IS - 7

M1 - 278

ER -

ID: 244687297