Statistical Computing: An Introduction to Data Analysis Using S-PLUS

By R. H. Kushler. Technometrics, 2003

1,924 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/53c420c721be4d381727a2d9fa107dbf0b4d1337

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Citations in the corpus

The effects of reindeer grazing on the composition and species richness of vegetation in forest–tundra ecotone (2008, methodology): “To obtain minimum adequate models (Crawley 2002), non-signiWcant terms were removed and Wnal models contained only signiWcant explanatory variables.”
Child-orientated environmental education influences adult knowledge and household behaviour (2013, result): “An additional interpretation is that practical hands-on interactions outside of the school environment enhance learning (Kusmawan et al 2006, Crater and Megs 1981, Aird and Tomera 1977). Awareness of a local river’s location, in contrast to knowledge score, shows no significant difference between st”
Ecology of butterflies in Europe (2009, methodology): “ANOVA estimates mean values for the respective categories (Quinn & Keough, 2002; Crawley, 2005). Huntzinger (2003), for instance, considered different soil-related or fire-related management plans as categorical predictors to explain butterfly diversity.”
Lentic odonates have larger and more northern ranges than lotic species (2006, methodology): “Again we used type I analyses (Crawley, 2002) to evaluate the influence of the independent variables.”
Life history correlates of oxidative damage in a free‐living mammal population (2009, methodology): “In general, we used a model simplification approach, sequentially dropping terms with the lowest marginal F-statistic until only significant terms remained in the model (following Crawley 2002).”
A real experiment is a factorial experiment (2010, background): “This advantage of sampling at the extremes is well-known (see, e.g., Crawley, 2002, p. 67): the further apart the values of soa are, the larger the corresponding sum of squares, and the smaller the standard error for the slope.”
Should we use proportional sampling for species–area studies? (2004, methodology): “In such designs we must be careful to judge the significance of factors when they are removed from the optimal model (Crawley, 2002).”
Preplant release of Nesidiocoris tenuis and supplementary tactics for control of Tuta absoluta and Bemisa tabaci in greenhouse tomato (2012, methodology): “Thereafter, treatments were compared through model simplification by combining treatments (Crawley, 2002).”
Plant traits relate to whole‐community litter quality and decomposition following land use change (2007, methodology): “In order to account for the repeated harvests in the Home Plot and Standard Litter experiments, litter mass loss differences among age classes were analyzed using linear mixed effects models, with harvests nested within plots as a random factor (Crawley 2002).”
Biological correlates of description date in carnivores and primates (2004, methodology): “Multiple models were constructed using model simplification by first removing the high order interaction terms (order × trait interaction), and the main effects (Crawley, 2002).”
Exploring species attributes and site characteristics to assess plant invasions in Spain (2009, methodology): “As an alternative, we decided to model invasion success with a Poisson distribution of errors (Crawley, 2002).”
Control of Bemisia tabaci and Frankliniella occidentalis in cucumber by Amblyseius swirskii (2011, methodology): “Thereafter, treatments were compared through model simplification by combining treatments (Crawley 2002).”
Organic versus conventional systems in viticulture: Comparative effects on spiders and carabids in vineyards and adjacent forests (2015, methodology): “Likelihood ratios were used for testing the explanatory power of themodels and, using the drop1 function, we selected the minimum adequate model best explaining the data (Crawley, 2002).”
Does one size fit all? An evaluation of length–weight relationships for New Zealand's freshwater fish species (2013, methodology): “The Akaike information criterion (AIC; Akaike 1973) was used to apply a penalised log-likelihood method to evaluate the trade-off between degrees of freedom and fit of the model as the predictors were added or removed (Crawley 2002).”
Predicting susceptibility to future declines in the world's frogs (2008, methodology): “Due to the binary nature of the response variables, and the consequent non-normal error structure, we conducted the analyses using generalized linear models specifying the link function as either logit or complementary log-log, and preferred the link with the lowest residual deviance (Crawley 2002).”
Improved Bankfull Discharge Prediction Using 2‐Year Recurrence‐Period Discharge 1 (2008, methodology): “Statistics used for comparing Models 1, 2, and 3 are the root mean squared error or residual squared error (s), coefficient of determination (r2), and Akaike’s information criterion (AIC) (Graybill and Iyer, 1994; Crawley, 2003).”
Herbivore and neighbour effects on tundra plants depend on species identity, nutrient availability and local environmental conditions (2007, methodology): “the data were pooled across sites for these analyses (Crawley 2002),”
Ecological Models and Data in R (2008, background): “Further reading: If you need to review statistics, try Crawley (2002), Dalgaard (2003), or Gotelli and Ellison (2004).”