Extending the Linear Model with R:Generalized Linear, Mixed Effects and Nonparametric Regression Models, (2nd ed.)
By Julian J. Faraway. 2016
896 citations recorded by the source corpus.
Subfields: Applied Mathematics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/3f239c2c01c9d15a433688158bf7794af351de1e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Applied Mathematics
- Robert A. I. Davies
- Antoine Guisan
- Alistair Willis
- Lotte Meteyard
- Paul Piwek
- Sasha D. Hafner
- Jacopo Amidei
- C. Randin
In Statistics and Probability
- Robert A. I. Davies
- Antoine Guisan
- Alistair Willis
- Lotte Meteyard
- Paul Piwek
- Sasha D. Hafner
- Jacopo Amidei
- C. Randin
Citations in the corpus
- Does pollen-assemblage richness reflect floristic richness? A review of recent developments and future challenges (2016, neutral): “More detailed 657 accounts of GLM modelling include Crawley (1993, 2005, 2007), Faraway (2005, 2006), and Fox and 658 Weisberg (2011).”
- Best practice guidance for linear mixed-effects models in psychological science (2019, methodology): “This is because REML likelihood values depend on the fixed effects in the model (Faraway, 2016; Zuur et al, 2009).”
- The use of rating and Likert scales in Natural Language Generation human evaluation tasks: A review and some recommendations (2019, methodology): “Furthermore, recent advances in statistics have introduced new options for ordinal data which are worth to be taken into account, for example ordinal regression models (Bürkner and Vuorre, 2019) or generalized mixed effect models (Faraway, 2016) which are able to work with several different data dis”
- Changes in reproductive investment with altitude in an alpine plant (2009, methodology): “We used generalized linear mixed-effects models (GLMMs; (Faraway 2005; Gelman and Hill 2007; Maindonald and Braun 2007) since our design includes fixed and random effects and our responses include variables with normal and non-normal error distributions.”
- Improving Inter-Laboratory Reproducibility in Measurement of Biochemical Methane Potential (BMP) (2020, methodology): “The lmer() function from the lme4 package in R was used for parameter estimation based on a restricted maximum likelihood algorithm [60,61].”