VSURF: An R Package for Variable Selection Using Random Forests

Genuer, Robin; Poggi, Jean‐Michel; Tuleau-Malot, Christine

doi:10.32614/rj-2015-018

Cited by 500 publications

(422 citation statements)

References 35 publications

(33 reference statements)

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“…In addition, we used the R package VSURF (73) (Variable Selection using Random Forests) for feature selection on the 37 phyla. This method uses Random Forests, which are an ensemble approach from machine learning that rank the importance of features in terms of their ability to classify a variable of interest, while taking into account the complex interrelationships of the features (74).…”

Section: Methodsmentioning

confidence: 99%

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The Microbiome in Posttraumatic Stress Disorder and Trauma-Exposed Controls: An Exploratory Study

Malan‐Müller²,

et al. 2017

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Objective Inadequate immunoregulation and elevated inflammation may be risk factors for posttraumatic stress disorder (PTSD), and microbial inputs are important determinants of immunoregulation; however, the association between the gut microbiota and PTSD is unknown. This study investigated the gut microbiome in a South African sample of PTSD-affected individuals and trauma-exposed (TE) controls, to identify potential differences in microbial diversity or microbial community structure. Methods The Clinician Administered Posttraumatic Stress Disorder Scale for DSM-5 (CAPS-5) was used to diagnose PTSD according to DSM-5 criteria. Microbial DNA was extracted from stool samples obtained from 18 individuals with PTSD and 12 TE control participants. Bacterial 16S ribosomal RNA (rRNA) gene V3/V4 amplicons were generated and sequenced. Microbial community structure, alpha-diversity, and beta-diversity were analyzed; random forest analysis was used to identify associations between bacterial taxa and PTSD. Results There were no differences between PTSD and TE control groups in alpha- or beta-diversity measures (e.g., alpha-diversity, Shannon index, t = 0.386, P = .70; beta diversity, based on analysis of similarities (ANOSIM), Bray Curtis test statistic = −0.033, P = .70); however, random forests analysis highlighted three phyla as important to distinguish PTSD status: Actinobacteria, Lentisphaerae, and Verrucomicrobia. Decreased total abundance of these taxa was associated with higher PTSD CAPS scores (r = −.387, P = .035). Conclusions In this exploratory study, measures of overall microbial diversity were similar among individuals with PTSD and TE controls; however, decreased total abundance of Actinobacteria, Lentisphaerae, and Verrucomicrobia was associated with PTSD status.

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Section: Methodsmentioning

confidence: 99%

“…We used the R package VSURF (73) (Variable Selection using Random Forests) for feature selection on the 37 phyla. Random forest analysis identified three phyla (Actinobacteria, Lentisphaerae, and Verrucomicrobia) as highly important to classify PTSD versus TE controls.…”

Section: Random Forest/vsurfmentioning

confidence: 99%

The Microbiome in Posttraumatic Stress Disorder and Trauma-Exposed Controls: An Exploratory Study

Malan‐Müller²,

et al. 2017

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“…This was done in order to obtain a reduced variable set for both interpretation and prediction issues. Therefore, we applied the variable elimination process described by Genuer et al (2015) on the same data as for RF all . In contrast to the former approach, we here included the geographic coordinates (x = easting, y = northing; UTM ETRS89) of the FC in order to account for the spatial distribution of the feeding plots.…”

Section: Methodsmentioning

confidence: 99%

“…Variables are thus kept or eliminated from the nested model according to a threshold of the minimum error gain relating to the “out-of-bag” error (OOB). The threshold was calculated by the mean of the first-order differentiated OOB errors (see Genuer et al, 2015). Using the ‘VSURF’ package (Genuer et al, 2015), we provided an additional variable selection suggested for prediction (RF V SURF ).…”

Section: Methodsmentioning

confidence: 99%

Importance of Ecological Variables in Explaining Population Dynamics of Three Important Pine Pest Insects

Hentschel

Möller²,

Wenning³

et al. 2018

Front. Plant Sci.

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Climate change challenges forest vitality both directly by increasing drought and heat periods and indirectly, e.g., by creating favorable conditions for mass outbreaks of phyllophagous insects. The large forests dominated by Scots pine (Pinus sylvestris L.) that cover the lowland regions in northeast Germany have already been affected regularly by cyclic mass propagations of defoliating insect species in the past with climate projections implying an even more advantageous environment for devastating outbreaks in the future. To improve predictive and responsive capacities we have investigated a wide range of ecological parameters to identify those most strongly related to past outbreak waves of three central species. In total, we analyzed 3,748 variables covering stand and neighborhood properties, site quality, and climatic conditions for an area of roughly 750,000 ha of pine forests in the period 2002–2016. To reflect sensitivity against varying climate, we computed “floating windows” in relation to critical phenological phases of the respective insects. The parameters with the highest explanatory power resulted from the variable importance measures of the Random Forest (RF) methodology and have been evaluated by a 10-fold cross-validation process. Our findings closely reflect the known specific gradation patterns and show that relative variable importance varies with species. While Lymantria monacha L. feeding was mainly dependent on the surroundings of the respective stand, Diprion pini L. proved to be almost exclusively susceptible to climatic effects in its population dynamics. Dendrolimus pini L. exhibited a mixed pattern of variable importance involving both climatic and forest structure parameters. In many cases the obtained statistical results support well-known ecological cause-effect relations and long-term population change dynamics. The RF delivered very high levels of sensitivity and specificity in the developed classifications and proved to be an excellent tool to handle the large amounts of data utilized for this study. While the presented classification approach may already support a better prognosis of the amplitude during the outbreak culmination, the obtained (most important) variables are proposed as preferable covariates for modeling population dynamics of the investigated insect species.

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“…Random forests are used for variable selection using the 'VSURF' package (Genuer et al, 2016). The variable importance plots for each predictor variable and for both ranges of relative ammonia values are obtained using this function.…”

Section: Data Analysesmentioning

confidence: 99%

Assessment of wastewater treatment facility compliance with decreasing ammonia discharge limits using a regression tree model

Suchetana

Rajagopalan

Silverstein

2017

Science of The Total Environment

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VSURF: An R Package for Variable Selection Using Random Forests

Cited by 500 publications

References 35 publications

The Microbiome in Posttraumatic Stress Disorder and Trauma-Exposed Controls: An Exploratory Study

The Microbiome in Posttraumatic Stress Disorder and Trauma-Exposed Controls: An Exploratory Study

Importance of Ecological Variables in Explaining Population Dynamics of Three Important Pine Pest Insects

Assessment of wastewater treatment facility compliance with decreasing ammonia discharge limits using a regression tree model

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