Using Generalized Additive Models (GAMs) for Large Datasets to Determine the Effect of Air Pollution on Human Health

The impact of air pollution and human health has been the subject of a colossal measure of epidemiological activity. This research considers an application in the effects of air pollution and human health where generalized additive models (GAMs) are proper. However, due to the large size of data, the use of GAMs is practically inexible with existing methods. In this manner, Wood et al. (2014) developed generalized additive model tting methods for substantial data sets for the situation in which the smooth terms are replaced by using penalized cubic regression splines. We extended our analysis to ten (10) cities simultaneously using generalized additive model. Through the utilization of the environmental package from the National Morbidity, Mortality, Air Pollution study, GAMs ends up being adaptable with large data set.