This Is The Technique To Achieve Fluorouracil Expertise

1) using the gbm package version 1.5-7 (Ridgeway 2006) and code provided by Elith et al. (2008). The optimal model was determined following the recommendations of Elith et al. (2008) by altering the learning rate and tree complexity (the number of split nodes in a tree) until the predictive deviance was minimized without over-fitting, and by limiting our choice of the final model to those that contained at least 1000 trees (where each successive tree is built for the prediction residuals of the preceding tree). Once the optimal combination of learning rate and tree complexity was found, model performance UNC2881 was evaluated using a 10-fold cross-validation procedure with resubstitution. For each cross-validation trial, 80 % of the dataset was randomly selected for model fitting and the excluded 20 % was used for testing. We calculated the response Selleckchem Fluorouracil variance explained, the area under the receiver operator characteristic curve (AUC), the overall accuracy, the omission error rate and the commission error rate based on the aggregated CV results. We evaluated the reliability and validity of our models as fair (0.50 SRT1720 cell line (false negative) error rate was 9.8 %. Recursive feature elimination tests showed that 45 variables could be removed from the model before the resulting predictive deviance exceeded the initial predictive deviance of the model with all variables. Thirteen variables were included in the final model (Table?1), with variables associated with climatic conditions and landscape features accounting for ?53.5 and 46.5 %, respectively, of the contribution in the overall model (Fig.?2). Examination of the relative contribution of the predictor variables indicated that the top four accounted for ?70.