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BMC Medical Research Methodology

Open Access

Erratum to: Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA) wastewater data

BMC Medical Research MethodologyBMC series – open, inclusive and trusted201717:34

https://doi.org/10.1186/s12874-017-0311-y

Published: 23 February 2017

The original article was published in BMC Medical Research Methodology 2016 16:81

Erratum

After publication of the original article [1], it came to the authors’ attention that there were errors in Fig. 3, Fig. 4 and Additional file 1: Figure S1.
Fig. 3

Bootstrapping confidence intervals (CIs) resulting from functional principal component analysis (FPCA) on 1000 re-samples obtained by a random sample with repetition from the original data sets. Panel a – Bootstrapping CI resulting from a FPCA using Fourier basis functions and no smoothing parameter; Panel b – Bootstrapping CI resulting from a FPCA using Fourier basis functions and common-optimal smoothing parameter; Panel c – Bootstrapping CI resulting from a FPCA using Fourier basis functions and individual-optimal smoothing parameter; Panel d – Bootstrapping CI resulting from a FPCA using B-splines basis functions and no smoothing parameter; Panel e – Bootstrapping CI resulting from a FPCA using B-splines basis functions and common-optimal smoothing parameter; Panel f – Bootstrapping CI resulting from a FPCA using B-splines basis functions and individual-optimal smoothing parameter

Fig. 4

Sensitivity to missing for functional principal component analysis (FPCA) results. Panel a – Functional principal components (FPCs) resulting from a FPCA using Fourier basis functions and no smoothing parameter for 5, 10, 15, 20 % of missing; Panel b – Functional principal components (FPCs) resulting from a FPCA using Fourier basis functions and common-optimal smoothing parameter for 5, 10, 15, 20 % of missing; Panel c – Functional principal components (FPCs) resulting from a FPCA using Fourier basis functions and individual-optimal smoothing parameter for 5, 10, 15, 20 % of missing; Panel d – Functional principal components (FPCs) resulting from a FPCA using B-splines basis functions and no smoothing parameter for 5, 10, 15, 20 % of missing; Panel e – Functional principal components (FPCs) resulting from a FPCA using B-splines basis functions and common-optimal smoothing parameter for 5, 10, 15, 20 % of missing; Panel f – Functional principal components (FPCs) resulting from a FPCA using B-splines basis functions and individual-optimal smoothing parameter for 5, 10, 15, 20 % of missing

In each Figure, panels A, B and C are not correct (but panels D, E and F are). This error was due to a mistake in the last stages of the submission process while adjusting the Figures’ size to fit the journal’s requirements. This error does not impact the results, discussion and conclusions of the paper.

The correct version of the affected Figures are published in this erratum.

Notes

Declarations

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Norwegian Centre for Addiction Research, University of Oslo
(2)
Oslo Centre for Biostatistics and Epidemiology, Institute of Basic Medical Sciences

References

  1. Salvatore S, Bramness JG, Røislien J. Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA) wastewater data. BMC Med Res Methodol. 2016;16:81. doi:10.1186/s12874-016-0179-2.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s). 2017

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