search for: base_per

...o 0.0008723241 0.001757602 0.0013181209 1.930695 freq_intra 0.0009449492 0.001943090 0.0014431451 2.611950 log_hypo 0.0004514713 0.001366561 0.0009096419 1.736749 acid_per 0.0125815445 0.023360179 0.0179634375 21.131681 base_per 0.0070077737 0.012196570 0.0096129124 13.675893 charge_per 0.0095668425 0.024125997 0.0168345956 20.969665 hydrophob_per 0.0185736697 0.031941513 0.0252200036 25.994903 polar_per 0.0169369327 0.023633413 0.0202776247 20.890415...

...00000 0.00149 0 5 0.00312 0 0 0.00467 0.00467 0.00000 0.00156 0.00467 0.00312 0.00467 0 6 0.00587 0 0 0.02053 0.00587 0.00000 0.00293 0.00587 0.00293 0.00000 0 7 0.00000 0 0 0.02422 0.00346 0.00000 0.00346 0.00346 0.00000 0.00346 0 WD WG WN YW acid_per base_per charge_per 1 0.00000 0.00000 0.00000 0.00000 0.14402174 0.12228261 0.019021739 3 0.00178 0.00178 0.00534 0.00178 0.12277580 0.09252669 0.016014235 4 0.00149 0.00448 0.00448 0.00000 0.16591928 0.11509716 0.022421525 5 0.00000 0.00156 0.00000 0.00156 0.13084112 0.10903427 0.009345794 6 0.00293 0.000...

...train<-rbind(train.cc, train.nn) #training data valid<-rbind(valid.cc, valid.nn) # validation data #creat data set contains the following variables myvar<-names(gh5_h) %in% c(varr, "num_cell","num_genes","position", "acid_per", "base_per", "charge_per", "hydrophob_per", "polar_per", "out") train<-train[myvar] # update training set valid<-valid[myvar] control<-rpart.control(xval=10, cp=0.01, minsplit=5, minbucket=5) #control the size of the initial tree tre...

...8 15 WW 0.05460215 16 FC 0.04913720 17 QT 0.01987817 18 CT 0.00000000 19 SK 0.00000000 20 YG 0.00000000 21 position 0.00000000 22 freq_hypo 0.00000000 23 freq_intra 0.00000000 24 log_hypo 0.00000000 25 base_per 0.00000000 -- Sincerely, Changbin -- [[alternative HTML version deleted]]