similar to: how to save this result in a vector

HI, Dear R community, I have one data set like this, What I want to do is to calculate the cumulative coverage. The following codes works for small data set (#rows = 100), but when feed the whole data set, it still running after 24 hours. Can someone give some suggestions for long vector? id reads Contig79:1 4 Contig79:2 8 Contig79:3 13 Contig79:4 14 Contig79:5 17

I want to calculate the commulative sum of any numeric vector with the following command but this following command does not work "comsum" My question is , how we can calculate the commulative sum of any numeric vector with above command Thanks Zahid Khan Lecturer in Statistics Department of Mathematics Hazara University Mansehra. ---------------------------------

somfunc<- function (file) { aa_som<-scale(file) final.som<-som(data=aa_som, rlen=10000, grid=somgrid(5,4, "hexagonal")) pdf(file="/home/cdu/changbin/file.pdf") #output graphic file. plot(final.som, main="Unsupervised SOM") dev.off() } I have many different files, if I want output pdf file with the same name as for each dataset I feed to the function

> #sort the data by predicted probability > b.order<-bo.id.pred[(order(-predict)),] > b.order[1:20,] gene_id predict 43 637882902 0.07823997 53 638101634 0.66256490 61 639084581 0.08587504 41 637832824 0.02461066 25 637261662 0.11613879 22 637240022 0.06350477 62 639084582 0.02238538 63 639097718 0.06792841 44 637943079 0.04532625 80 640158389 0.06582658 3 637006517 0.57648451

> head(en.id.pr) valid.gene_id b.pred rf.pred svm.pred 1521 2500151211 0 0 0 366 639679745 0 0 0 1965 2502081603 1 1 1 1420 644148030 1 1 1 1565 2500626489 1 1 1 1816 2501711016 1 1 1 > p.pred <- data.frame(en.id.pr, sum=apply(en.id.pr[,2:4], 1, sum)) #

HI, Dear Greg, I AM A NEW to GBM package. Can boosting decision tree be implemented in 'gbm' package? Or 'gbm' can only be used for regression? IF can, DO I need to combine the rpart and gbm command? Thanks so much! -- Sincerely, Changbin -- [[alternative HTML version deleted]]

svm.fit<-svm(as.factor(out) ~ ., data=all_h, method="C-classification", kernel="radial", cost=bestc, gamma=bestg, cross=10) # model fitting svm.pred<-predict(svm.fit, hh, decision.values = TRUE, probability = TRUE) # find the probability, but can not find. attr(svm.pred, "probabilities") > attr(svm.pred, "probabilities") 1 0 1 0 0 2 0

> x <- train[,c( 2:18, 20:21, 24, 27:31)] > y <- train$out > > svm.pr <- svm(x, y, probability = TRUE, method="C-classification", kernel="radial", cost=bestc, gamma=bestg, cross=10) > > pred <- predict(svm.pr, valid[,c( 2:18, 20:21, 24, 27:31)], decision.values = TRUE, probability = TRUE) > attr(pred, "decision.values")[1:4,]

HI, Dear R community, I am using the following codes to plot, however, the lines code works. But the line was not drawn on the previous plot and did not shown up. How comes? # specify the data for missense simulation x <- seq(0,10, by=1) y <- c(0.952, 0.947, 0.943, 0.941, 0.933, 0.932, 0.939, 0.932, 0.924, 0.918, 0.920) # missense z <- c(0.068, 0.082, 0.080, 0.099, 0.108, 0.107,

HI, Dear community, I am building the following loop, ww<-function(file) { lossw<-vector() for (x in seq(0.1, 0.9, by=0.1)) { cat('xweight ', x, '\n') lossw[i] <- cross.validation(file, x)$avg } return(lossw) } MY question is how to index the lossw[i]? for (i in 1:9) for (x in seq(0.1, 0.9, by=0.1)) Thanks so much!

HI, Dear R community, My original file has 1932 lines, but when I read into R, it changed to 1068 lines, how comes? cdu@nuuk:~/operon$ wc -l id_name_gh5.txt 1932 id_name_gh5.txt > gene_name<-read.table("/home/cdu/operon/id_name_gh5.txt", sep="\t", skip=0, header=F, fill=T) > dim(gene_name) [1] 1068 3 -- Sincerely, Changbin -- Changbin Du DOE Joint Genome

HI, Dear R community, I want to split a data frame by using two variables: let and g > x = data.frame(num = c(10,11,12,43,23,14,52,52,12,23,21,23,32,31,24,45,56,56,76,45), let = letters[1:5], g = 1:2) > x num let g 1 10 a 1 2 11 b 2 3 12 c 1 4 43 d 2 5 23 e 1 6 14 a 2 7 52 b 1 8 52 c 2 9 12 d 1 10 23 e 2 11 21 a 1 12 23 b 2 13 32 c 1 14

HI, Dear Greg and R community, I have one question about the output of gbm package. the output of Boosting should be f(x), from it , how to calculate the probability for each observations in data set? SInce it is stochastic, how can guarantee that each observation in training data are selected at least once? IF SOME obs are not selected, how to calculate the training error? Thanks? --

Dear R community, I have one function, it works for small data set, but does not work on large data set, can anyone help me with this? > #creat new variable by dividing each aa dimer by total_length. > imper<-function(x, file) { + round(x/file$length, 5) + } > dim(test) [1] 999 2402 > test[varname[2:2401]]<-

Dear community, I have one file named ca_boost_feature.txt, Feature selection (Boosting:0.0025,5)! H.2.C C.1.D C.3.R E.0.N C.2.S C.0.G H.3.G log file: ep If I want to use the second line of this file, how to read it into R? varr<-read.table("/home/cdu/operon/carbonic/ca_boost_feature.txt", sep=" ", skip=1, header=F, strip.white=TRUE, nrows=1) Warning message: In

HI, Dear R community, Last friday, I used the codes, it works, but today, it does not run? > fit.dimer <- rpart(outcome ~., method="class", data=p.df) Error in `[.data.frame`(frame, predictors) : undefined columns selected DOEs anyone have comments or suggestions? Thanks in advance! -- Sincerely, Changbin -- [[alternative HTML version deleted]]

plot(svm.auc, col=2, main="ROC curves comparing classification performance\n of six machine learning models") legend(0.5, 0.6, c(ns, nb, nr, nt, nl,ne), 2:6, 9) # Draw a legend. plot(bo.auc, col=3, add=T) # add=TRUE draws on the existing chart plot(rf.auc, col=4, add=T) plot(tree.auc, col=5, add=T) plot(nn.auc, col=6, add=T) plot(en.auc, col=9,lty="dotted",lwd=3, add=T) Hi,

HI, Guys, I am trying to use the AdaBoosting. M.1 algorithm to integrate three models. I found the sum of weights for each model is not equal to one. How to deal with this? Thanks, any response or suggestions are appreciated! -- Sincerely, Changbin -- [[alternative HTML version deleted]]

HI, Dear R community, My data set has 2409 variables, the last one is response variable. I have used the nnet after feature selection and works. But this time, I am using nnet to fit a model without feature selection. I got the following error information: > dim(train) [1] 1827 2409 nnet.fit<-nnet(as.factor(out) ~ ., data=train, size=3, rang=0.3, decay=5e-4, maxit=500) # model

HI, Dear R community, I try to create 100 dummy variables like the following: ack$id_1 <- (ack$ID==1)*1 ack$id_2 <- (ack$ID==2)*1 .. . ack$id_100 <- (ack$ID==100)*1 I used the following codes: for(i in 1:100){ ack$id_[i] <- (ack$ID==i)*1 } But only one column is created, can anyone help me? Thanks a lot! -- Sincerely, Changbin --