Linear Models With R

By Timothy J. Robinson. Technometrics, 2005

452 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/90d1e6a2546e29ea56f345da690775812abe0a92

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Citations in the corpus

The effects of stand structure on ground-floor bryophyte assemblages in temperate mixed forests (2009, background): “Many studies have shown that in the boreal region, where spruce is the dominant forest forming species, the presence of deciduous species (e.g. aspen, Populus tremula, Berg et al. 1994; Gustafsson and Eriksson 1995; Esseen et al. 1997) has a positive effect on bryophyte species richness.”
Multimodel inference in ecology and evolution: challenges and solutions (2011, methodology): “Incidentally, regression analysis by GLMM does not require predictors (input variables) to be normally distributed, although in some cases, normalization transformations can reduce residual variance and therefore affect inference regarding parameter estimates (Faraway, 2005).”
Climatic variability and landscape heterogeneity impact urban mosquito diversity and vector abundance and infection (2011, methodology): “We also weighted the regression with the number of traps-nights per year and site to correct for the heteroscedasticity, i.e., unequal variance, due to the unequal sampling effort across years and sites (Faraway 2004).”
Simple means to improve the interpretability of regression coefficients (2010, methodology): “Data analysis in ecology and evolution is largely based on the use of linear regression models such as anova, ancova, multiple regression, GLM or mixed models (Quinn & Keough 2002; Faraway 2005; Bolker et al. 2009).”
European cancer mortality predictions for the year 2014. (2014, unknown): “95% prediction intervals (PI) [12].”
On Testing Independence and Goodness-of-fit in Linear Models (2013, unknown): “The other example we consider is the savings data set given in [Far05] (see Chapter 3, page 31).”
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance (2013, background): “Selecting the correct variable to retain in a model is more error-prone under collinearity (Faraway 2005), and the resulting reduced data set will also be biased (see Elith et al.”
Applied Predictive Modeling (2013, methodology): “R fits the largest identifiable model by removing variables in the reverse order of appearance in the model formula” (Faraway 2005).”
Assessing the Calibration of High-Dimensional Ensemble Forecasts Using Rank Histograms (2013, methodology): “To create an ensemble that appropriately represents the prediction uncertainty we additionally inﬂate ε ij to adjust for the uncertainty in the bias correction (Faraway, 2004, Section 3.5).”
Effect size, confidence interval and statistical significance: a practical guide for biologists (2007, methodology): “The approximate width of 95% CIs for an effect size is: 95%CI ¼ ES [ 1:96se to ES] 1:96se; ð15Þ where ES stands for effect size (e.g. d, or z-transformed r) and se is the asymptotic standard error for the effect size (note that these formulae are also used for calculations of unstandardised effect s”
Mixed Effects Models and Extensions in Ecology with R (2009, methodology): “Chapter 5 in Faraway (2005) gives a very nice overview how serious violation of this assumption results in biased regression parameters.”
Improving Inter-Laboratory Reproducibility in Measurement of Biochemical Methane Potential (BMP) (2020, methodology): “As above, the lm(), aov(), summary.lm(), summary.aov() functions were used in R [47] for ANOVA [62], and the lmer() function from the lme4 package in R was used for mixed-e ﬀ ects models, based on a restricted maximum likelihood algorithm [60,61].”
The effect of stand structure and tree species composition on epiphytic bryophytes in mixed deciduous-coniferous forests of Western Hungary (2010, methodology): “…selected biological variables of bryophyte assemblages at the stand level (cover and species richness of the whole assemblage; species richness of specialist and facultative epiphytes) and the potential explanatory variables were analyzed by generalized linear models (Table 1; Faraway, 2005, 2006).”