similar to: about nnet package

Dear All, After followed the help of nnet, I could get the networks trained and, excitedly, get the prediction for other samples. It is a two classes data set, I used "N" and "P" to label the two. My question is, how do I get the predicted numerical value for each sample? Not just give me the label(either "N" or "P")? Thanks! FYI: The nnet example I

Dear All, I have such a two-class problem, one class is very large(~98% of total), and the other is just 2%. According to manual of nnet, I need setup "weights", so I intend to set 1 for class one, 49 for class 2. How do I do that? Just weights=49? Meanwhile I'd like to try svm(e1071), again, how do I setup "class.weights"? Thanks. BTW: Many thanks to Jake and Uwe for

Dear All, I have a data, suppose it is an N*M matrix data. All I want is to classify it into, let see, 3 classes. Which method(s) do you think is(are) appropriate for this purpose? Any reference will be welcome! Thanks! Best, Baoqiang Cao

Dear All, I am producing a figure with many curves on it. How do I make the legends for all those curves smaller so that it can fit the figure itself? The commands I used for ploting are: plot(x1,y1,col=1,lty=1) lines(x2,y2,col=2,lty=2) ... legend(0.3,0.4,c("name1","name2",...),col=1:20,lty=1:20) Any tips for making the legend fit the figure will very welcome! Thanks! Best,

Dear All, I am using wilcox.test to test two samples, data_a and data_b, earch sample has 3 replicates, suppose data_a and data_b are 20*3 matrix. Then I used the following to test the null hypothesis (they are from same distribution.): wilcox.test(x=data_a, y=data_b, alternative="g") I got pvalue = 1.90806170863311e-09. When I switched data_a and data_b by doing the following:

Dear All, I tried to plot a variety of lines(curves) on same figure. What I did is, plot(x=x1,y=y1) lines(x=x2,y=y2) lines(x=x3,y=y3) ... In my data, the maximum of y1 is much smaller than those maximums of other y vectors. So, in the figure I got, there are some curves which are not complete, I mean, they were cut off at the maximum of y1 at the y axis. Could anybody point out some right

Hi Everyone, I'm trying to use cutree to get the clusters after hclust. What I used is: mycluster<-cutree(cnclust,h=0.5) Now, my problem is, how can I get the actual clusters? Thanks! Best, Baoqiang Cao

Dear All, I was trying to format a numeric vector (100*1) by using outd <- format(x=m, sci=F, digits=2) > outd[1:10] [1] " 0.01787758" "-0.14760306" "-0.45806041" "-0.67858525" "-0.64591748" [6] "-0.05918100" "-0.25632276" "-0.15980138" "-0.08359873" "-0.37866688" >m[1:10] [1]

Hello R-Users, I have been using (nnet) by Ripley to train a neural net on a test dataset, I have obtained predictions for a validtion dataset using: PP<-predict(nnetobject,validationdata) Using PP I can find the -2 log likelihood for the validation datset. However what I really want to know is how well my nueral net is doing at classifying my binary output variable. I am new to R and I

Hi, Am working on neural network. Below is the coding and the output > library (nnet) > uplift.nn<-nnet (PVU~ConsumerValue+Duration+PromoVolShare,y,size=3) # weights: 16 initial value 4068.052704 final value 3434.194253 converged > summary (uplift.nn) a 3-3-1 network with 16 weights options were - b->h1 i1->h1 i2->h1 i3->h1 16.64 6.62 149.93

One possibility is to look at predict.nnet(), and - Write an R function that write out parts of an nnet object that are needed by predict.nnet() to an external file. - Re-write predict.nnet() in C, reading the model information from the external file. Obviously you'll also need the C source for the code that predict.nnet() calls, and modify those as needed to strip out dependency on R, if

======= At 2005-07-22, 12:56:12 you wrote: ======= >On Fri, 22 Jul 2005, Baoqiang Cao wrote: > >> Dear All, >> >> I have such a two-class problem, one class is very large(~98% of total), >> and the other is just 2%. According to manual of nnet, I need setup >> "weights", so I intend to set 1 for class one, 49 for class 2. How do I >> do that?

Hi there, I really couldn't find out how to plot histogram with point/line instead of rectangle for each bin? Any help please? Thanks! Best, -Cao

Hi, I have a question about using lm on matrix, have to admit it is very trivial but I just couldn't find the answer after searched the mailing list and other online tutorial. It would be great if you could help. I have a matrix "trainx" of 492(rows) by 220(columns) that is my x, and trainy is 492 by 1. Also, I have the newdata testx which is 240 (rows) by 220 (columns). Here is

Dear All, Just start to use the long expected R, my focus will be doing clustering on microarray data, just wonder, anyone can show me any references to conquer the steep learning curve? Thanks! Best regards, Baoqiang Cao

Hello, I use nnet to do prediction for a continuous variable. after that, I calculate correlation coefficient between predicted value and real observation. I run my code(see following) several time, but I get different correlation coefficient each time. Anyone know why? In addition, How to calculate prediction accuracy for prediction of continuous variable? Aimin thanks, > m.nn.omega

Greetings list, I've just swapped from the "neural" package to the "nnet" package and I've noticed that the training is orders of magnitude faster, and the results are way more accurate. This leads me to wonder, what training algorithm is "nnet" using? Is it a modification on the standard backpropagation? Or a completely different algorithm? I'm

Dear List, I am trying to solve a problem by the neural network method(library: nnet). The problem is to express Weight in terms of Age , Sex and Height for twenty people. The data frame consists of 20 observations with four variables: Sex, Age, Height and Weight. Sex is treated as a factor, Age and Weight are variables normalized to unity, as usual. I wanted to construct a neural network, and so

Hi, I'm trying to plot something in the following way and would like if you could help: I'd like in a same plot window, two plots are shown, the left one is a bird-view plot of the whole data, the right half keep changing, i.e., different plots will be shown up on request, so that when I select/click on some where in the left plot, the right plot will be the corresponding plot. What I

Hi, I am trying to make a neural network learning a "noisy sine wave". Suppose I generate my data like so.. x <- seq(-2*pi, 2*pi, length=500) y <- sin(x) + rnorm(500, sd=sqrt(0.075)) I then train the neural net on the first 400 points using c <- nnet(as.matrix(x[1:400]),as.matrix(y[1:400]), size=3, maxit=10000, abstol=0.075, decay=0.007) Inspecting the fit of the training