similar to: Display Median in plot

I got the data working, but now I got another problem with KSVM: line search fails -2.793708 -0.5831701 1.870406e-05 -5.728611e-06 -5.059796e-08 -3.761822e-08 -7.308871e-13Error in prob.model(object)[[p]]$A : $ operator is invalid for atomic vectors On Tue, Jul 7, 2009 at 6:45 PM, Steve Lianoglou<mailinglist.honeypot at gmail.com> wrote: > Hi, > > On Jul 7, 2009, at 6:44 PM,

Hello all, I have a seemingly simple question which I have searched for an answer for for a few hours without luck. I have a matrix of both values and characters with thousands of rows. I would like to run a search of this matrix for certain values and would like the search to retrieve the entire row. How would I accomplish this? Thanks! Mehdi Khan [[alternative HTML version deleted]]

Not sure if there is an R way to do this or a regular express way, but here is what I am trying to do. I've got lots of data where the format is HH:MM:SS, but I need to format it like HH:MM:00, i.e. round the second down to zero. What is the best way to do this? Thanks again. Jason

Hello, I have simulation results in the form of Time V I 0.000000000000e+000 7.218354344368e-001 5.224478627497e-006 1.000000000000e-009 7.218354344368e-001 5.224477718002e-006 2.000000000000e-009 7.218354344368e-001 5.224477718002e-006 4.000108361244e-009 7.218354344368e-001 5.224478627497e-006 8.000325083733e-009

Hi, I was wondering if I could get R to warn me, or give me a rude awakening somehow, if I'm accessing a variable that is out of my function's scope. For example, often times I'm creating a function as I'm testing it in the REPL, copying and pasting between both. As a simple example, I might end up with a function like: f <- function(a, b) { a + b.test } Where

Dear All I hope that someone can help. I am working with sp pakage and akima library("akima") library(sp) imagine lots of different dataframes, of row = 100 columns = 3 of x and y coordinates with z values I will call these data frames for the sake of this example akima akima<-as.list(1:100) producing 100 dataframes dataframes of the form akima[[i]] I then wish to interp this

What's wrong? Very sad about this... model <- ksvm(x=mytraindata[, -1], y=factor(mytraindata[, 1]), prob.model=T) Error in .local(x, ...) : x and y don't match.

Is there a command as "mat.or.vec(nr,nc)" to create an array that I must calculate with more cicle? [[alternative HTML version deleted]]

Hi, Is there a function in R to do a-b where a and b are two non-numeric sets (or intersection complement of these two sets). Kind Regards, Praveen. [[alternative HTML version deleted]]

Hi I want to simulate random numbers normal distributed with this size (2000,10000). I tried this but my computer exhaust there is a fast way to make it? randz<-matrix(rnorm(2000000),2000,10000) [[alternative HTML version deleted]]

Hi all, could someone send me examples of the EM Clustering algorithm and/or some pdf describing it? Thanks, Douglas Sousa.

Dear R People: I'm running from R to a grid computer and getting some unusual results with the system command: > e7 <- system("globus-job-run xxxxx /bin/sh -c 'cd $OSG_APP/engage;chmod 777 oops'",intern=TRUE) chmod: changing permissions of `oops': Operation not permitted > e7 character(0) > I xx'ed out the site name. Anyhow, I would expect that the e7

Hello, I have a question about lars package, probably basic. The returned values of lars function include R squares along the variable selection path. However, such values are always slightly different from the R squares returned by the regression function lm using the same models. Anyone know the reasons? Very important, and needs quick answers. Thanks a million! -- View this message in

Hi list, Could someone help me to explain why the leave-one-out cross validation results I got from svm using the internal option "cross" are different from those I got manually? It seems using "cross" to do cross validation, the results are always better. Please see the code below. I also include lda as a comparison. I'm using WinXP, R-2.9.0, and e1071_1.5-19. Many

Hi I wrotte this function but when I get the "tmp.xls" file it shows data in a rare way. I mean not appears a matrix with 2000 rows and 100 columns. Can anyone help me, guide me? kim<-function(){ tmp<-randz<-matrix(rnorm(200000, mean=0,sd=0.01 ),2000,100); dim(tmp) write(tmp,file="tmp.xls") Thanks in advance. On the other hand, everytime I execute a function

Hello, I'm currently using a boxplot to visualize data for three different models. As I have three models, I'm plotting three parallel boxplots for each factor. This works fine - what I need now is a line connecting the medians of each boxplot of each model. I want to do this in order to visualize the trend that one of the models exhibit. Basically, I want to plot a curve for each model

With Dotplot, I'm trying to make a figure that will ultimately have the same x-axis (which will be my response variable and the error bars), but the y-axis will consist of a different label for every point. Here's my code: Dotplot(fTaxonGrouped ~ Cbind(normSlope,normLwr,normUpr)|fGroup, groups=fEpoch, pch=c(17,15,19), col=c(3:1), scales = list( y = list(relation =

Dear R People: I'm having trouble with something that should be very simple. I'm setting up a matrix outside of a loop and writing items into it during the loop. Here is the output: > glob3b("sites.info") dim 27 3 [1] "/raid1/osg-app" Error in xy[i, ] : incorrect number of dimensions Here is the function: > glob3b function(xx) { x.df <-

Dear experts, I'm new in R and trying to learn by writing a version of the Perceptron Algorithm. How can I tell in the code below to stop the iteration when the condition in the "for loop" is not satisfied for all training examples? Thanks in advance for your help! ## Generate a linearly separable data set in R2 sample <- as.data.frame(cbind(runif(n=100),runif(n=100)))

It is known that function bkde2D (package "KernSmooth") returns a matrix of density estimates over the mesh induced by x1 and x2. In Details it is written that "... heights of the kernel, scaled by the bandwidths, at each datapoint are summed. This sum, after a normalization, is the corresponding fhat value in the output". There are several questions: 1) How to calculate