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Quantifying the Effects of Correlated Covariates on Variable Importance Estimates from Random ForestsKimes, Ryan Vincent 01 January 2006 (has links)
Recent advances in computing technology have lead to the development of algorithmic modeling techniques. These methods can be used to analyze data which are difficult to analyze using traditional statistical models. This study examined the effectiveness of variable importance estimates from the random forest algorithm in identifying the true predictor among a large number of candidate predictors. A simulation study was conducted using twenty different levels of association among the independent variables and seven different levels of association between the true predictor and the response. We conclude that the random forest method is an effective classification tool when the goals of a study are to produce an accurate classifier and to provide insight regarding the discriminative ability of individual predictor variables. These goals are common in gene expression analysis, therefore we apply the random forest method for the purpose of estimating variable importance on a microarray data set.
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Analysis of Vegetation Vulnerability Dynamics and Driving Forces to Multiple Drought Stresses in a Changing EnvironmentWei, Xiaoting, Huang, Shengzhi, Huang, Qiang, Liu, Dong, Leng, Guoyong, Yang, Haibo, Duan, Weili, Li, Jianfeng, Bai, Qingjun, Peng, Jian 15 January 2024 (has links)
Quantifying changes in the vulnerability of vegetation to various drought stresses in
different seasons is important for rational and effective ecological conservation and restoration.
However, the vulnerability of vegetation and its dynamics in a changing environment are still
unknown, and quantitative attribution analysis of vulnerability changes has been rarely studied. To
this end, this study explored the changes of vegetation vulnerability characteristics under various
drought stresses in Xinjiang and conducted quantitative attribution analysis using the random
forest method. In addition, the effects of ecological water transport and increased irrigation areas
on vegetation vulnerability dynamics were examined. The standardized precipitation index (SPI),
standardized precipitation-evapotranspiration index (SPEI), and standardized soil moisture index
(SSMI) represent atmospheric water supply stress, water and heat supply stress, and soil water supply
stress, respectively. The results showed that: (1) different vegetation types responded differently to
water stress, with grasslands being more sensitive than forests and croplands in summer; (2) increased
vegetation vulnerability under drought stresses dominated in Xinjiang after 2003, with vegetation
growth and near-surface temperature being the main drivers, while increased soil moisture in the
root zone was the main driver of decreased vegetation vulnerability; (3) vulnerability of cropland
to SPI/SPEI/SSMI-related water stress increased due to the rapid expansion of irrigation areas,
which led to increasing water demand in autumn that was difficult to meet; and (4) after ecological
water transport of the Tarim River Basin, the vulnerability of its downstream vegetation to drought
was reduced.
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