|Saturday, February 22|
|PS3 Poster Session III & Continental Breakfast||
Sat, Feb 22, 7:30 AM - 9:00 AM
Fitting a GAM to estimate hourly Ozone levels in the air from climate variables (302782)*Javier Olaya, Universidad del Valle
Keywords: Linear models, Smoothing, Regression, Non-parametrics, Splines
We use climate variables to construct a Generalized Additive Model to estimate hourly Ozone levels in the air. Instead of fitting a linear model using the original variables, or fitting a linear model using some functions of the original variables picked by experts, we fit an additive model using some data-driven functions gotten through one-dimensional smoothing techniques. For illustrative purposes, firstly we fit a model using the Ozone levels as the response. Then we fit another model using as a response a dichotomous variable indicating whether or not a particular hourly Ozone level has been reached. It highlights the fact that GAM models are useful on predicting a quantitative response, but also a non-numerical one. On both models we used as predictors the covariates Solar radiation, Relative humidity, Temperature, and Wind speed. Smooth functions were based on spline smoothing. We show the procedure in such a way that potential users may be able to reproduce it step-by-step on their own projects. Computations and figures were done using The R Project for Statistical Computing. Data come from a monitoring station of the Air Quality Surveillance System at Cali, Colombia.