similar to: sum of list elements

Dear R users, I am trying to apply SVM regression for a set of microarray data. I am using the function svm() under the package {e1071}. Can anyone tell me what the *residuals *value represents? I have some observed values *y_obs* for the parameter that I want to estimate and I would expect that *svm$residuals = y_obs - svm$fitted. *However, this does not happen...Does anyone have any idea on

Hi all, I am trying to draw a Kaplan-Meier curve and I found online that Kaplan - Meier estimates are computed with a function called km in the event package. Is there an update for that because when I choose to download packages in R,. there is no package called event, even though I have selected all the repositories. Thanks in advance, Eleni [[alternative HTML version deleted]]

Hello list, I am trying to perform ROC analysis and count the AUC in order to validate my results. I use package ROCR. I would like to count the AUC not under the cutoff found by "performance" but to use another cutoff that I calculate. How could I change the following command in order to get what I want? perform=performance(pred,measure="auc",x.measure="cutoff"),

Hello list, I am trying to apply the paired t.test between diseased and not diseased patients to identify genes that are more expressed in the one situation under the other. In order to retrieve the genes that are more expressed in the positive disease state I do: p.values<-c() for(i in 1:length(Significant[,1])){ p.values[i]<-try(t.test(positive[i,],negative[i,],alternative

Hi all! I am new to R and I would like to ask you the following question:How can I substitute the NA values with 0 in a data frame? I cannot find a command to check if a value is NA... Thank you very much! Eleni

Hi all, I am trying to install the package "GEOquery" in unix. I have downloaded the standard version of R and this package is not contained in the default. I know that I can select repositories under windows but I don't know how to do it in unix. Does anyone have any idea on this? Thank you in advance, Eleni [[alternative HTML version deleted]]

Hi list! I have recently tried to take my office work home, meaning that I tried to transfer my "... .RData" workspace from my PC on my laptop. The office PC runs on Windows XP and my laptop runs on Windows Vista. I have saved the workspace at the office PC and kept it in a usb drive. When I tried to open it on my laptop I got an error: "Fatal Error: Unable to restore saved data in

Hello R users, I am trying to run a cox model for the prediction of relapse of 80 cancer tumors, taking into account the expression of 17000 genes. The data are large and I retrieve an error: "Cannot allocate vector of 2.4 Mb". I increase the memory.limit to 4000 (which is the largest supported by my computer) but I still retrieve the error because of other big variables that I have in

Hello all, I have the following problem. I want to insert blank rows in an data frame so as to make it comparable with another data frame with more rows. How can I insert these rows? If this is not solvable, then, alternatively, is it easy to delete rows from a data frame? Thanks for helping, Eleni [[alternative HTML version deleted]]

Hello R community, I am processing raw Affymetrix CEL files and I am using the Michigan custom CDF library hgu133plus2hsentrezgprobe. I have been looking for documentation on the function that it contains...I am specifically interested in converting probe names to gene symbols. Does anybody know where I can find it? Thank a lot! Eleni [[alternative HTML version deleted]]

Hello everyone, I wanted to know if it was possible to change the color of certain leaves in a hclust object in order to make my graph more readable. I know I can color certain groups but I would like to enter a vector telling the plot function which leaves to color in which color. Thanks in advance, Eleni [[alternative HTML version deleted]]

Hello all! I am performingsome clustering analysis on microarray data using agnes{cluster} and I have created my own dissimilarity matrix according to a distance measure different from "euclidean" or "manhattan" etc. My question is, if I choose for example method="complete", how are the distances between the elements calculated? Are they taken form the dissimilarity

Dear list, I am trying to perform a significance analysis of a microarray experiment with survival data using the {samr} package. I have a matrix containing my data which has 17816 rows corresponding to genes, and 286 columns corresponding to samples. The name of this matrix is data.matrix2. Some of the first values of this matrix are: data.matrix2[1:3,1:5] GSM36777 GSM36778 GSM36779

Dear list, I have a couple of questions concerning ridge regression. I am using the lm.ridge(...) function in order to fit a model to my microarray data. Thus *model=lm.ridge(...)* I retrieve some coefficients and some scales for each gene. First of all, I would like to ask: the real coefficients of the model are not included in the first argument of the output but in the result of coef(model),

Hi I am doing this sort of thing: POLY: > > obj = best.tune(svm, similarity ~., data = training, kernel = "polynomial") > summary(obj) Call: best.tune(svm, similarity ~ ., data = training, kernel = "polynomial") Parameters: SVM-Type: eps-regression SVM-Kernel: polynomial cost: 1 degree: 3 gamma: 0.04545455 coef.0: 0

Dear list, I am having a set of human oligo ids (H200006022 H200002025 H300004703 H200000442 H200005719 H300018350) which I want to map to Ensembl or RefSeq. I am sure R has a function to do that. I downloaded the {oligo} package and tried to use the probeNames function. Although the factor of ologo ids is an object (as the argument to probeNames should be) I retrieve the following error:

If do: > library("e1071") > example(svm) I get: svm> data(iris) svm> attach(iris) svm> ## classification mode svm> # default with factor response: svm> model <- svm(Species ~ ., data = iris) svm> # alternatively the traditional interface: svm> x <- subset(iris, select = -Species) svm> y <- Species svm> model <- svm(x, y) svm>

Hi This is a question regarding classification performance using different methods. So far I've tried NaiveBayes (klaR package), svm (e1071) package and randomForest (randomForest). What has puzzled me is that randomForest seems to perform far better (32% classification error) than svm and NaiveBayes, which have similar classification errors (45%, 48% respectively). A similar difference in

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,]