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@article{1838140,
author = {Spyroglou, Ioannis and Skalák, Jan and Balakhonova, Veronika and Benedikty, Z. and Rigas, A.G. and Hejátko, Jan},
article_location = {BASEL},
article_number = {2},
doi = {http://dx.doi.org/10.3390/plants10020362},
keywords = {Arabidopsis; linear mixed models; time series analysis; ARIMA},
language = {eng},
issn = {2223-7747},
journal = {PLANTS-BASEL},
title = {Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences},
url = {https://www.mdpi.com/2223-7747/10/2/362},
volume = {10},
year = {2021}
}
TY - JOUR
ID - 1838140
AU - Spyroglou, Ioannis - Skalák, Jan - Balakhonova, Veronika - Benedikty, Z. - Rigas, A.G. - Hejátko, Jan
PY - 2021
TI - Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences
JF - PLANTS-BASEL
VL - 10
IS - 2
SP - 362
EP - 362
PB - MDPI
SN - 22237747
KW - Arabidopsis
KW - linear mixed models
KW - time series analysis
KW - ARIMA
UR - https://www.mdpi.com/2223-7747/10/2/362
N2 - Plants adapt to continual changes in environmental conditions throughout their life spans. High-throughput phenotyping methods have been developed to noninvasively monitor the physiological responses to abiotic/biotic stresses on a scale spanning a long time, covering most of the vegetative and reproductive stages. However, some of the physiological events comprise almost immediate and very fast responses towards the changing environment which might be overlooked in long-term observations. Additionally, there are certain technical difficulties and restrictions in analyzing phenotyping data, especially when dealing with repeated measurements. In this study, a method for comparing means at different time points using generalized linear mixed models combined with classical time series models is presented. As an example, we use multiple chlorophyll time series measurements from different genotypes. The use of additional time series models as random effects is essential as the residuals of the initial mixed model may contain autocorrelations that bias the result. The nature of mixed models offers a viable solution as these can incorporate time series models for residuals as random effects. The results from analyzing chlorophyll content time series show that the autocorrelation is successfully eliminated from the residuals and incorporated into the final model. This allows the use of statistical inference.
ER -
SPYROGLOU, Ioannis, Jan SKALÁK, Veronika BALAKHONOVA, Z. BENEDIKTY, A.G. RIGAS and Jan HEJÁTKO. Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences. \textit{PLANTS-BASEL}. BASEL: MDPI, 2021, vol.~10, No~2, p.~362-377. ISSN~2223-7747. Available from: https://dx.doi.org/10.3390/plants10020362.
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