Abstract
A key step in any analysis is data visualisation. This is a challenging topic for multivariate data, because (if we have more than two responses) it is not possible to jointly visualise the data in a way that captures correlation across responses or how they relate to predictors. In this chapter, we will discuss a few key techniques to try.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Anderson, E. (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.
Grace, J. B. (2006). Structural equation modeling and natural systems. Cambridge: Cambridge University Press.
Hui, F. K. C. (2016). boral—Bayesian ordination and regression analysis of multivariate abundance data in R. Methods in Ecology and Evolution, 7, 744–750.
Hui, F. K. C., Taskinen, S., Pledger, S., Foster, S. D., & Warton, D. I. (2015). Model-based approaches to unconstrained ordination. Methods in Ecology and Evolution, 6, 399–411.
Kruskal, J. B., & Wish, M. (1978). Multidimensional scaling. Beverley Hills: Sage Publications.
Legendre, P., & Legendre, L. (2012). Numerical ecology. Third English edition. Amsterdam: Elsevier Science.
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O’Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Szoecs, E., & Wagner, H. (2017). vegan: Community ecology package. R package version 2.4-3.
Ovaskainen, O., & Abrego, N. (2020). Joint species distribution modelling: With applications in R. Cambridge: Cambridge University Press.
Ovaskainen, O., Roy, D. B., Fox, R., & Anderson, B. J. (2016). Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models. Methods in Ecology and Evolution, 7, 428–436.
Ovaskainen, O., Tikhonov, G., Norberg, A., Guillaume Blanchet, F., Duan, L., Dunson, D., Roslin, T., & Abrego, N. (2017b). How to make more out of community data? A conceptual framework and its implementation as models and software. Ecology Letters, 20, 561–576.
Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal and structural equation models. Boca Raton: Chapman & Hall.
Thorson, J. T., Scheuerell, M. D., Shelton, A. O., See, K. E., Skaug, H. J., & Kristensen, K. (2015). Spatial factor analysis: A new tool for estimating joint species distributions and correlations in species range. Methods in Ecology and Evolution, 6, 627–637.
Walker, S. C., & Jackson, D. A. (2011). Random-effects ordination: Describing and predicting multivariate correlations and co-occurrences. Ecological Monographs, 81, 635–663.
Warton, D. I. (2018). Why you cannot transform your way out of trouble for small counts. Biometrics, 74, 362–368
Warton, D. I., Wright, S. T., & Wang, Y. (2012b). Distance-based multivariate analyses confound location and dispersion effects. Methods in Ecology and Evolution, 3, 89–101.
Warwick, R. M., Clarke, K. R., & Suharsono (1990). A statistical analysis of coral community responses to the 1982–1983 El Niño in the Thousand Islands, Indonesia. Coral Reefs, 8, 171–179.
Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. New York: Springer. ISBN: 978-3-319-24277-4.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2022 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Warton, D.I. (2022). Visualising Many Responses. In: Eco-Stats: Data Analysis in Ecology. Methods in Statistical Ecology. Springer, Cham. https://doi.org/10.1007/978-3-030-88443-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-88443-7_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88442-0
Online ISBN: 978-3-030-88443-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)