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Chapter 11: Positive Continuous Data: Gamma and Inverse Gaussian GLMs

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Book cover Generalized Linear Models With Examples in R

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Abstract

This chapter considers models for positive continuous data. Variables that take positive and continuous values often measure the amount of some physical quantity that is always present. The two most common glms for this type of data are based on the gamma and inverse Gaussian distributions. Judicious choice of link function and transformations of the covariates ensure that a variety of relationships between the response and explanatory variables can be modelled. Modelling positive continuous data is introduced in Sect. 11.2, then the two most common edms for modelling positive continuous data are discussed: gamma distributions (Sect. 11.3) and inverse Gaussian distributions (Sect. 11.4). The use of link functions is then addressed (Sect. 11.5). Finally, estimation of ϕ is considered in Sect. 11.6.

It has been said that data collection is like garbage collection: before you collect it you should have in mind what you are going to do with it.

Fox, Garbuny and Hooke [ 6 , p. 51]

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Authors and Affiliations

  1. Faculty of Science, Health, Education and Engineering, School of Health of Sport Science, University of the Sunshine Coast, Queensland, QLD, Australia

    Peter K. Dunn

  2. Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia

    Gordon K. Smyth

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  1. Peter K. Dunn
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  2. Gordon K. Smyth
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Dunn, P.K., Smyth, G.K. (2018). Chapter 11: Positive Continuous Data: Gamma and Inverse Gaussian GLMs. In: Generalized Linear Models With Examples in R. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0118-7_11

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