search for: micromata

Might not be the best place to ask, but i could get lucky.. I have setup an eclipse environment to write sweave files lately and wanted to switch to knitr. I could get it to work on easy files, but my earlier written sweave file fails to be knit properly. Here is the error message: Quitting from lines 273-276: Error in setwd(base.dir) : kann Arbeitsverzeichnis nicht wechseln (it says: cannot

Hi everyone I have banking data set in long format with 4 columns.One of these columns is bank name which consist of 49 banks and I want the data for only 40 banks out of these 49 so can anyone help me on how to get this 40 banks data My data looks like Year Name totalliabilties assets 1990 a 90 10 1991 a 89 48 1992 a 87

I'm not sure what the parameter specifies: random if an integer value is specified, random parameter vectors are drawn from the parameter space. What are the parameter vectors and what is the parameter space? What means drawn? greetings Jessi [[alternative HTML version deleted]]

Hi All, I need to normalize/scale string variable which represents interests of customers (e.g., 'cycling, rollerblading, swimming' etc). Does anybody know how to do this, I want then use it along with other numeric variables for SVM classification. Appreciate for any advice. -Alex [[alternative HTML version deleted]]

Hi! how do i get to the source code of kpca or even better predict.kpca(which it tells me doesn't exist but should) ? (And if anyone has too much time: Now if i got that right, the @pcv attribute consists of the principal components, and for kpca, these are defined as projections of some random point x, which was transformed into the other feature space -> f(x), projected onto the actual

Hello R-Help! I've looked around and have not found: A simple(short) way to hide functions and variables from the global environment. What i want is for a few of them to only be accessable from the scriptfile they're in. I probably could do fun things with environments , but that seems quite a hassle. As example: I have a file that gets me stuff from the database and creates an R object

Hi! I am getting a lot of numbers in the background of the pca screeplots if i use call("plot") and eval(somecall). Til now, creating the calls and plotting later on this way worked fine. Example: pcaI<-prcomp(iris[,1:4]) plot(pcaI) x<-call("plot",pcaI) eval(x) Anyone got an idea how i can avoid that? (also it might take a second or so for the numbers to appear,

I'm making tables for prediction results of classifiers (2 classes) that show the usual numbers, true positives, false positives, etc I used the command table(predictedLabels,realLabels) to make those. I just had a case though ,where one of the label vectors had only one class in it. This will result in only half a table. Compare: x<-c(1,1,1,0,0) y<-c(1,1,1,0,1) table(x,y) to

Can i somehow append objects to an .Rdata file? I didn't see an option for it in the save() method. dump() won't work since i have s4 objects in there.

From: ?????? <NAVER-MAILER at naver.com> Subject: [???? ??] envy721c at naver.cobSDsnLzroZwg66mU7J287J20IOyghOyGoeuQmOyngCDrqrvtlojsig==teuLiOuLpC4= The only plain english in the message is that the mail was denied by the receiver Anyone else getting this?

Hi! If i found a problem with the code of one of the libraries (not core), or, in my current case, would wish something minor changed for convenience, where can i get contact? Can i put it in the "official" bug repository? (Problem discription for anyone interested: Why call the default function kpca for a matrix with kpar=list(sigma=0.2), instead of putting this default sigma into the

Following problem: Say you have a bunch of parameters and want to produce results for all combinations of those: height<-c("high","low") width<-c("slim","wide") then what i used to do was something like this: l<-list() for(h in height){ l[[h]]<-list() for(w in width){ l[[h]][[w]] <- doSomething() } } Now those parameters aren't

Everyone, I'm very new to R, especially when it comes to loops and functions, so please bear with me if this is an elementary question. I cannot seem to figure out how to construct a loop which runs a function until a certain value is computed. For example, say I have the following: num = numeric (10) num [1] = 0 for (i in 2:10) { num [i] = num [i-1] + 5 } This adds 5 to the

Hi! I'm doing a PCA on some testdata (only 2 features) currently to see what happens. I read a lot of times one should center ones data, so i left the option on. But now i am scratching my head on how to correctly transform new data to be comparable to the data used in the pca. pca$x are the points (in PC system) the function returns origpoints%*%pca$rotation is not quite the same, because

Hi there! I have found a trange problem with getting pairs()-plots to show properly in latex \subfloat environments. If i generate images of these plots with pdf() and include them in subfloats, they will either show up in grayscale, or sometimes the datapoints of the pairplots are missing. Mind you, the PDFs themselves are properly colored and look perfectly fine, both as pdf-image in acrobat

When i make Boxplots with a lot of boxes, the names of them get only written down every second "column". Since they aren't in any way ordered, you don't see anymore to what they belong. example: l<-rep(list(1:5),20); boxplot(l,names=sample(20,1:20)) Is there a way to show them all, or do i have to split the plots? [[alternative HTML version deleted]]

with pred.pca<-predict(splits[[i]]$pca,trainingData at samples)[,1:nPCs] dframe<-as.data.frame(cbind(pred.pca,class=isExplosive(trainingData,2))); results[[i]]$classifier<-ksvm(class~.,data=dframe,scaled=T,kernel="polydot",type="C-svc", C=C,kpar=list(degree=degree,scale=scale,offset=offset),prob.model=T) and a degree of 5 i get an error of 0 reported by the ksvm

Hi again! This might be more of a statistical question, but anyway: If i train several support vector machines with different degrees of polynomials, and as result, get that higher degrees not only have a higher test error, but also a higher in-sample error, why is that? I would assume i should get an in-sample error lower or at least the same as the linear case. i'm worried i did

I'd like to make plots that do not have the quadratic layout, like having a plot that is twice as wide as it is high (without distorting everything). Sadly i wasn't able to find anything in par that does that. Best would be with plot(), but i'd use the ggplot package as well if necessary. thanks, Jessica

Hi! The kernlab function kpca() mentions that new observations can be transformed by using predict. Theres also an example in the documentation, but as you can see i am getting an error there (As i do with my own data). I'm not sure whats wrong at the moment. I haven't any predict functions written by myself in the workspace either. I've tested it with using the matrix version and the