Visualizing Fit and Lack of Fit in Complex Regression Models with Predictor Effect Plots and Partial Residuals

Fox, John; Weisberg, Sanford

doi:10.18637/jss.v087.i09

Cited by 432 publications

(286 citation statements)

References 21 publications

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“…Corresponding negative binomial or Poisson models did not fit the data adequately. Results were plotted using the effects package (Fox and Weisberg 2018).…”

Section: Statisticsmentioning

confidence: 99%

Plant–pollinator interactions along an urbanization gradient from cities and villages to farmland landscapes

et al. 2020

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Urbanization affects pollinator diversity and plant–pollinator networks by changing resource availability locally and in the surrounding landscape. We experimentally established (N = 12) standardized plant communities in farmland, villages, and cities to identify the relative role of local and landscape effects on plant–pollinator communities along this urbanization gradient. We found that the number of flower visits by solitary bees, but not bumblebees, was highest in cities and lowest in farmland, with villages being intermediate, whereas syrphid flies exhibited lowest numbers in cities. Villages supported the richest pollinator communities, as they appeared to benefit from both farmland and city communities. Plant–pollinator network metrics such as robustness, interaction evenness, and interaction diversity decreased with increasing urbanization, although local plant richness increased toward urban areas. In conclusion, pollinator communities were most diverse and stable in farmland and village sites, despite the high plant richness in cities. The different composition of pollinator communities along the urbanization gradient suggests considering all three landscape types for conservation schemes.

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“…Corresponding negative binomial or Poisson models did not fit the data adequately. Results were plotted using the effects package (Fox and Weisberg 2018).…”

Section: Statisticsmentioning

confidence: 99%

Plant–pollinator interactions along an urbanization gradient from cities and villages to farmland landscapes

et al. 2020

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“…, C × S, β‐std = −1.32, P = 0.07; Fig. , 95% CI show GLM fit, following Fox & Weisberg, ). In contrast, sap‐feeders had a negative effect on shoot growth only when ants were present (Fig.…”

Section: Resultsmentioning

confidence: 88%

Keystone mutualism influences forest tree growth at a landscape scale

et al. 2019

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Interactions between ants and phloem‐feeding herbivores are characterised as a keystone mutualism because they restructure arthropod communities and generate trophic cascades. Keystone interactions in terrestrial food webs are hypothesised to depend on herbivore community structure and bottom‐up effects on plant growth. Here, we tested this prediction at a landscape scale with a long‐term ant‐exclusion experiment on hickory saplings in the context of spatial variation in herbivore community structure and habitat quality. We quantified top‐down effects of ants, herbivore communities as well as abiotic factors impacting hickory shoot growth. We found that ants influenced shoot growth via strong, context‐dependent, compensatory effects, with clear cascading benefits only when phloem‐feeders were present and chewing herbivore abundance was high. By contrast, while several landscape variables predicted hickory growth, they did not mediate the strength of cascading effects of ants. These results suggest that ant/sap‐feeder mutualisms may regulate forest productivity by mediating effects of multiple herbivore guilds.

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“…The package lmerTest [50] was used to calculate standard errors, effect sizes, and significance values. The effects of the models were visualized in plots for a better interpretation of each model by applying the effects package [51].…”

Section: Resultsmentioning

confidence: 99%

Translation Quality and Error Recognition in Professional Neural Machine Translation Post-Editing

Vardaro¹,

Schaeffer²,

Hansen‐Schirra³

2019

Informatics

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This study aims to analyse how translation experts from the German department of the European Commission's Directorate-General for Translation (DGT) identify and correct different error categories in neural machine translated texts (NMT) and their post-edited versions (NMTPE). The term translation expert encompasses translator, post-editor as well as revisor. Even though we focus on neural machine-translated segments, translator and post-editor are used synonymously because of the combined workflow using CAT-Tools as well as machine translation. Only the distinction between post-editor, which refers to a DGT translation expert correcting the neural machine translation output, and revisor, which refers to a DGT translation expert correcting the post-edited version of the neural machine translation output, is important and made clear whenever relevant. Using an automatic error annotation tool and the more fine-grained manual error annotation framework to identify characteristic error categories in the DGT texts, a corpus analysis revealed that quality assurance measures by post-editors and revisors of the DGT are most often necessary for lexical errors. More specifically, the corpus analysis showed that, if post-editors correct mistranslations, terminology or stylistic errors in an NMT sentence, revisors are likely to correct the same error type in the same post-edited sentence, suggesting that the DGT experts were being primed by the NMT output. Subsequently, we designed a controlled eye-tracking and key-logging experiment to compare participants' eye movements for test sentences containing the three identified error categories (mistranslations, terminology or stylistic errors) and for control sentences without errors. We examined the three error types' effect on early (first fixation durations, first pass durations) and late eye movement measures (e.g., total reading time and regression path durations). Linear mixed-effects regression models predict what kind of behaviour of the DGT experts is associated with the correction of different error types during the post-editing process.Informatics 2019, 6, 41 2 of 29 the development of their own machine translation systems. Statistical machine translation (internally referred to as MT@EC) has been available at the DGT since 2013 and is now coming to an end. The new neural system, eTranslation, was launched in November 2017 and is able to "translate between any pair of the 24 official EU languages, as well as Icelandic and Norwegian (Bokmål): it can handle formatted documents and plain text; it translates multiple documents into multiple languages in "one go"; it accepts diverse input formats including XML and PDF; it retains formatting; and it provides specific output formats for computer-aided translation, i.e., TMX11 and XLIFF."It is trained with the aligned source and target segments that have been produced over the years-amounting to approximately 1.2 billion training segments ([1], p. 3).However, the usage of machine translation requires a different form of revisi...

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Visualizing Fit and Lack of Fit in Complex Regression Models with Predictor Effect Plots and Partial Residuals

Cited by 432 publications

References 21 publications

Plant–pollinator interactions along an urbanization gradient from cities and villages to farmland landscapes

Plant–pollinator interactions along an urbanization gradient from cities and villages to farmland landscapes

Keystone mutualism influences forest tree growth at a landscape scale

Translation Quality and Error Recognition in Professional Neural Machine Translation Post-Editing

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