def summary_stat_of_accuracy_ls(self, acc_ls): """ 11-17-05 get average, standard deviation from acc_ls """ accuracy_ls, no_of_predictions_ls, no_of_genes_ls = [], [], [] for row in acc_ls: accuracy_ls.append(row[0]) no_of_predictions_ls.append(row[2]) no_of_genes_ls.append(row[3]) no_of_samples = float(len(acc_ls)) accuracy_avg = sum(accuracy_ls)/no_of_samples accuracy_std = r.sqrt(r.var(accuracy_ls)/no_of_samples) no_of_predictions_avg = sum(no_of_predictions_ls)/no_of_samples no_of_predictions_std = r.sqrt(r.var(no_of_predictions_ls)/no_of_samples) no_of_genes_avg = sum(no_of_genes_ls)/no_of_samples no_of_genes_std = r.sqrt(r.var(no_of_genes_ls)/no_of_samples) return [accuracy_avg,accuracy_std,no_of_predictions_avg,no_of_predictions_std,no_of_genes_avg,no_of_genes_std]
def run(self): for df in range(self.df_lower, self.df_upper+1): cor_list = [] for p_value in self.p_value_list: t=r.qt(p_value,df,lower_tail=r.FALSE) cor = r.sqrt(t*t/(t*t+df)) cor_list.append(cor) self.result_array.append(cor_list) #output the table and plot a sample self.output()
