search for: num_genes

...lem occurs again. Can anyone help me with this? > attach(total) The following object(s) are masked from total ( position 3 ) : acid base cell_evalue cell_hit charged freq_cell freq_hypo freq_intra gene_id gene_name hydrophobic hypo_evalue hypo_hit log_cell log_hypo log_pfam num_cell num_genes operon_id outcome pfam_align pfam_evalue pfam_per_id polar position target total_length -- Sincerely, Changbin -- [[alternative HTML version deleted]]

...0.0328547215 0.053142508 0.0429480441 30.663749 WW 0.0093983693 0.006377956 0.0078681474 7.250101 YG 0.0051691399 0.007338639 0.0062618144 11.084111 num_cell 0.0061355526 0.005373049 0.0057463613 5.060577 num_genes 0.0364878788 0.044544488 0.0404558096 32.745034 position 0.0025375614 0.011566496 0.0070255302 10.070505 freq_hypo 0.0008723241 0.001757602 0.0013181209 1.930695 freq_intra 0.0009449492 0.001943090 0.0014431451 2.611950 l...

HI, Dear Greg, I have one question about the variable relative influence plot: THE following is the rel.inf value of 25 variables, but wen I plot, not all the variables are labeled. i.e. num_genes, wg, hydrophob_per etc are not labeled on the y-axis. also the variables are labeled vertically, can it be labeled horizontally just like the summary table? Thanks! > summary(gbm1, n.trees=best.iter, plotit=TRUE, order=TRUE, cBars=14) # based on the estimated best number of trees...

...49 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 0.00000 0.00000 0.00000 0.07038123 0.08797654 0.002932551 7 0.00000 0.00346 0.00000 0.00346 0.05536332 0.08650519 0.010380623 hydrophob_per polar_per num_cell num_genes position out 1 0.3804348 0.1929348 1 4 1 0 3 0.3540925 0.2508897 1 4 3 0 4 0.3393124 0.2032885 1 4 4 1 5 0.3753894 0.2305296 2 7 1 0 6 0.4868035 0.1964809 2...

...n <- n.nn[-out.fold.n,] valid.nn <- n.nn[out.fold.n,] 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...