similar to: r-loop

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

> 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 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]]<-

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

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]]

> #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

HI, Dear R community, I am writing the following function to create one data set(*tree.pred*) and one vector(*valid.out*) from loops. Later, I want to use the data set from this loop to plot curves. I have tried return, list, but I can not use the *tree.pred* data and *valid.out* vector. auc.tree<- function(msplit,mbucket) { * tree.pred<-data.frame()

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 am using the following codes to do the som. I tried to label the notes by the majority vote. either through mapping or prediction. I attached my output, the left one dont have any labels in the note, the right one has more than one label in each note. I need to have only one label for each note either by majority vote or prediction. Can anyone give some suggestions or

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

Hi, there. Suppose I already have sensitivities and specificities. What is the quick R-function to calculate AUC for the ROC plot? There seem to be many R functions to calculate AUC. Thanks. Yulei [[alternative HTML version deleted]]

Hi Everyone, I need to use genetic algorithm to find the minimum. The problem is, I cannot define the fitness function, but I can build a neural network from the input data and use the output as a fitness function. Can this be done? The other problem is, I know there are a few package in R related to GA. So far I know all of them take a specific function as fitness function, is

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, i have a fairly large amount of genomic data. I have created a dataframe which has "Reference" as one column and "Variation" as another. I want to plot a ROC curve based on these 2 columns. I have serached the R manual but I could not understand. Can anybody help me with the R code for plotting ROC curve. Thnx ashu6886 -- View this message in context:

Dear Whom it may concern, I need help to figure the "macro" function in R: I need to plot the different data sets by a plotxyf function, I want the title to be different for different data set. # get the data set final.xyf<- xyf(data=as.matrix(my.final), Y=classvec2classmat(final$outcome), xweight = 0.5, grid=somgrid(5, 4, "hexagonal")) #plot function

> 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,

Dear Dr. Sarkar, When I try to run the codes, I found the following problem: > h<- sample(1:14, 319, rep=T) > c<- sample(1:14, 608, rep=T) > n<- sample(1:14, 1140, rep=T) > vt<-c(h, c, n) > ta<-rep(c("h", "c", "n"), c(319, 608, 1140)) > > to<-data.frame(vt,ta) > library(lattice) Attaching package: 'lattice'

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]]