Simple means to improve the interpretability of regression coefficients

By H. Schielzeth. 2010

2,747 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, background): “For example, prototype vector machines [4], regression models [36, 35], generalized additive models [16, 40, 26, 27], Bayesian case models [19] mind-the-gap models [20], and scoring systems [37] have all been proposed as interpretable models.”
Diving seabirds share foraging space and time within and among species (2010, background): “…to fitting the model we z-transformed all continuous predictor variables (i.e., chlorophyll a concentration, bathymetric depth, depth of the cube, distance to coast and temperature) such that their mean equaled zero and their standard deviation equaled one (Aiken and West 1991, Schielzeth 2010).”
Multiple facets of diversity effects on plant productivity: Species richness, functional diversity, species identity and intraspecific competition (2019, methodology): “We standardized all variables before analysis to facilitate 262 the interpretation of the regression coefficients (Schielzeth, 2010).”
Cover Crop Biomass Production Is More Important than Diversity for Weed Suppression (2019, methodology): “…all explanatory and response variables were standardized by subtracting the dataset mean and dividing crop science, vol. 59, march–april 2019 www.crops.org 7 by the standard deviation to increase the ease of interpretability of the relative effects of different variables (Schielzeth, 2010).”
A global quantitative synthesis of local and landscape effects on wild bee pollinators in agroecosystems. (2013, positive): “To interpret the main effects in the presence of interactions, we mean-centred continuous covariates (Gelman & Hill 2007; Schielzeth 2010).”
Environmental influence on transmitter detection probability in biotelemetry: developing a general model of acoustic transmission (2013, methodology): “All variables were centred and scaled by z-transformation (subtracting themean and dividing by standard deviation) prior to modelling, in order evaluate the effect size by the model coefficients (Schielzeth 2010).”
Foraging ecology of black bears in urban environments: guidance for human‐bear conflict mitigation (2015, methodology): “We rescaled attribute data of percent bear habitat, distance to riparian areas, and human density by dividing each value by the largest value within each respective attribute to allow for quick interpretation of coefficient estimates (Schielzeth 2010).”
A general and simple method for obtaining R2 from generalized linear mixed‐effects models (2013, background): “In GLMMs, r2e can be expressed as three components: (i) multiplicative dispersion (x), (ii) additive dispersion (r2e) and (iii) distribution-specific variance (r2d) (detailed in Nakagawa & Schielzeth 2010).”
Global quantification of contrasting leaf life span strategies for deciduous and evergreen species in response to environmental conditions (2012, background): “These indicate the relative contribution of each environmental variable to LLS, while statistically holding all other variables constant (Schielzeth, 2010).”
Seabird population changes following mammal eradications on islands (2018, methodology): “To ensure resulting parameter estimates would be comparable, we standardized the data by subtracting the mean and dividing by the standard deviation (Schielzeth, 2010).”
Avian pest control in vineyards is driven by interactions between bird functional diversity and landscape heterogeneity (2017, methodology): “All model predictors were scaled and centred to allow comparing their relative effects (Schielzeth 2010).”
Predictor complexity and feature selection affect Maxent model transferability: Evidence from global freshwater invasive species (2020, methodology): “This was performed using the following formula: standardized X = [X – mean (X)] / SD (X), where X is a metric, and mean (X) and SD (X) are the mean value and standard deviation, respectively, of X across all selected models for a particular species (Schielzeth, 2010).”
A global synthesis reveals biodiversity-mediated benefits for crop production (2019, background): “By doing this the focus of our analysis is on within399 study effects rather than between-study effects (46, 47).”
A farewell to the sum of Akaike weights: The benefits of alternative metrics for variable importance estimations in model selection (2017, methodology): “…about variable importance in that context shifts from dichotomous decisions about the presence or absence of a variable’s statistical effect to more tempered, biologically sound infer - ences about how much variables affect a biological phenomenon in the sampled population (Schielzeth, 2010).”