similar to: Question about framework to weighting different classes in SVM

Hi, Sorry that I have many questions today. I am using svm function on about 180,000 points of training set. It takes very long time to run. However, I would like it to spit out something to make sure that the run is not dead in between. Would you please suggest anyway to do so? And is there anyway to speed up the performance of this svm function? Thank you. - adschai

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>

I want to create a neural network, and then everytime it receives new data, instead of creating a new nnet, i want to use a backpropagation algorithm to adjust the weights in the already created nn. I'm using nnet package, I know that nn$wts gives the weights, but I cant find out which weights belong to which conections so I could implement the backpropagation algorithm myself. But if anyone

Hi,I have a fundamental questions that I'm a bit confused. If any guru from this circle could help me out, I would really appreciate.I have a system of equations in which some of the endogs appear on right hand sides of some equations. To solve this, one needs a technique like 2SLS or FIML to circumvent inconsistency of the estimated coefficients. My question is that if I apply the nonlinear

Hi All, I'm attempting to use eps-regression or nu-regression SVM to compute probabilities but the predict function applied to an svm model object returns values outside [0, 1]: Variable Data looks like: Present X02 X03 X05 X06 X07 X13 X14 X15 X18 1 0 1634 48 2245.469 -1122.0750 3367.544 11105.013 2017.306 40 23227 2 0 1402 40 2611.519 -811.2500 3422.769 10499.425 1800.475 40 13822 3 0 1379

Hello list, I want to apply svm from library e1071, and I want to supply class weights. I do not really understand the help entry (and there is no example) class.weights: a named vector of weights for the different classes, used for asymetric class sizes. Not all factor levels have to be supplied (default weight: 1). All components have to be named. I have two

I expected, that I will get the same prediction, if I multiply the weights for all classes with a constant factor, but I got different results. Please look for the following code. > library(e1071) > data(Glass, package = "mlbench") > index <- 1:nrow(Glass) > testindex <- sample(index, trunc(length(index)/5)) > testset <- Glass[testindex, ] > trainset <-

Hello, I've been working with "nnet" and now I'd like to use the weigths, from the fitted model, to iterpret some of variables impornatce. I used the following command: mts <- nnet(y=Y,x=X,size =4, rang = 0.1, decay = 5e-4, maxit = 5000,linout=TRUE) X is (m x n) Y is (m x 1) And then I get the coeficients by: Wts<-coef(mts) b->h1 i1->h1

Hi I come across an idea of using SVM to fit ordinal label data rather than nominal ones. I am wondering if there is any pacakge that does that. Or if you could please recommend me the package that is flexible enough the let me do so, I'd really appreciate. Thank you. - adschai

Hello all, Ive been playing with nnet (package 'nnet') and Ive come across this problem. nnet doesnt seems to like to have more than 1000 weights. If I do: > data(iris) > names(iris)[5] <- "species" > net <- nnet(species ~ ., data=iris, size=124, maxit=10) # weights: 995 initial value 309.342009 iter 10 value 21.668435 final value 21.668435 stopped after 10

Hi - Classic question that I tried to look up online and couldn't find a clear answer. It seems that I can have R to act as a server. But I never know how this works. Would anyone please provide an example or introduction material where I can learn about this? I'm trying to build an environment where computation are distributed/delegated among different servers requested whenever I need.

Hi, I encounter a situation where I have a list whose element is a column matrix. Says, $'1' [,1] 1 2 3 $'2' [,1] 4 5 6 Is there fast way to collapse the list into a matrix like a cbind operation in this case? Meaning, the result should be a matrix that looks like: [,1] [,2] [1,] 1 4 [2,] 2 5 [3,] 3 6 I can loop through all elements and do

I have a dataset and I wish to use two different models to predict. Both models are SVM. The reason for two different models is based on the sex of the observation. I wish to be able to make predictions and have the results be in the same order as my original dataset. To illustrate I will use iris: # Take Iris and create a dataframe of just two Species, setosa and versicolor, shuffle them

Hi, I have a question around how to build data structure in R. I have to implement a tree structure of data. I'm wondering if R already has something like a tree where I can extend from or I need to start from scratch. If so, what would be the most effective way to represent parent and child node relationship? I realize that R is not based on pointer so it's quite a different paradigm for

Hi - I tried to get some answer before but there seems to have no one response. My question is that I have a class like below definition: setClass("DBManager", representation(channel="RODBC")) My purpose of the conn slot is to hold a channel to database connection which is returned by a command like: channel <- odbcConnect("DB", uid="user",

Hi,I have a result from polr which I fit a univariate variable (of ordinal data) with probit function. What I would like to do is to overlay the plot of my fitted values with the different intercept for each level in my ordinal data. I can do something like:lines(rep(intercept1, 1000), seq(from=0,to=max(fit),by=max(fit)/1000))where my intercept1 is, for example, the intercept that breaks between

Hi - I am using polr. I can get a result from polr fit by calling result.plr <- polr(formula, data=mydata, method="probit"); However, from the 'result.plr', how can I access standard error of the estimated coefficients as well as the t statistics for each one of them? What I would like to do ultimately is to see which coefficients are not significant and try to refit the

Hi, I am using tsls function from sem package to estimate a model which includes large number of data. Among its predictors, it has a nominal data which has about 10 possible values. So I expand this parameter into 9-binary-value predictors with the coefficient of base value equals 0. I also have another continuous predictor. The problem is that, whenever I run the tsls, I will get 'System

Hi I have the code like I show below. The problem here is that I have a setReplacementMethod to set the value of my class slot. However, this function doesn't work when I call it within another function definition (declared by setMethod) of the same class. I do not understand this behavior that much. I'm wondering how to make this work? Any help would be really appreciated. Thank you.

Hi - I'm quite wondering what makes the lag operator does not work for my time series. I have a time series of length about 200000 elements. I would like to have a lag 1 of this time series. I did the following: logprice = log(price, base=exp(1)) # this is my log price which is a vector of price time series of length 200000 ts_logprice = as.ts(logprice, frequency=1) # convert to time series