In this study, we applied a statistical approach (the Generalized Additive Model, GAM) to explain the decline of atmospheric mercury concentration in Beijing, China, which followed a trend (Sen's slope) of 0.37 ng m yr (8.0% yr). The statistical model represented 56.5% of the variance in...
The aim was to test a non-parametric water temperature generalized additive model (GAM) and to compare its performance to three previously developed approaches: the logistic, residuals regression and linear regression models. Due to its flexibility, the GAM was able to capture some of the ...
I would like to use Generalized Additive Model (GAM) to smooth the measurments to better integrates trend in my dataset. Here is my dataset. Does anyone can help me how to use GAM to smooth my dataset? 댓글 수: 5 이전 댓글 3개 표시 Sahar khalili 2022년 9...
Background Generalized Additive Model (GAM) provides a flexible and effective technique for modelling nonlinear time-series in studies of the health effects of environmental factors. However, GAM assumes that errors are mutually independent, while time series can be correlated in adjacent time points....
This study combined Random Forest (RF) and Generalized Additive Model (GAM) to evaluate the influence of environmental factors on phytoplankton biomass in Lake Okeechobee for the past 15 years. RF was used to investigate the importance of the vast environmental predictors, and GAM was applied to ...
This study performs multivariate analysis using generalized additive models (GAM) to understand how the covariates fit the model and affect pipe failure. GAMs are an approach used extensively in environmental modelling and provide great scope to model complex relationships between covariates. We look ...
Due to the nonlinear correlation between parasite and time, we used the generalized additive model (GAM) to analyze the trend of infection intensity by month. GAM is a generalized linear model offering a middle ground that can be fit to complex, nonlinear relationships and make good predictions ...
temporal autocorrelation, we used generalized additive mixed models (GAMMs) that included an autoregressive term for model errors. We used a likelihood ratio test to compare our GAMM to a GAM that did not include autoregressive errors, and in both cases GAMMs were selected over GAMs so they were...
Instead of modeling only the mean, gamboostLSS enables the user to model various distribution parameters such as location, scale and shape at the same time (hence the name GAMLSS, generalized additive models for location, scale and shape). Using gamboostLSS For installation instructions see below...
Generalized Additive ModelsSpatiotemporal delayed effectsHabitat predictions improved including delayed effects and spatial autocorrelation.Model performances of SDE-GAMs were 25.4% higher than for traditional GAMs.SDE-GAM's predicted both general patterns and smaller details of the distribution.SST with 1,...