similar to: modifications to text.tree function

I am working through Tom Minka's lectures on Data Mining and am now on Day 32. The following is the link: http://alumni.media.mit.edu/~tpminka/courses/36-350.2001/lectures/day32/ In order to use the functions cited I followed the instructions as follows: Installed tree package from CRAN mirror (Ca-1) Downloaded and sourced the file "tree.r" Downloaded the function

Help! I tried nearly everything I could and still was not able to get IE working with wine 0.9.19. Manual install using App DB, winetools, ies4linux, etc... everything fails, with different problems and error messages of course. It's really frustrating. Is there a way to install IE that actually works ?

It appears that while Linux has plenty of traffic shaping mechanism on transmit, there is nothing on receive side. While generally it does make sense since transmit is more CPU intensive operation, after all receive also consumes CPU cycles. It is clear that it''s best to drop the packet as soon as possible, i.e. on receive, if possible - by the driver itself. It may not be feasible in

I''m trying to configure 4 queues with strict priorities based on DSCP. I tried to following commands, but it seems that the filters I defined have no effect tc qdisc add dev eth0 root handle 1: prio bands 4 tc qdisc add dev eth0 parent 1:0 handle 10: pfifo limit 100 tc qdisc add dev eth0 parent 1:1 handle 20: pfifo limit 100 tc qdisc add dev eth0 parent 1:2 handle 30: pfifo limit 100 tc

> From: Sirotkin, Alexander [mailto:demiurg at ti.com] > > P.S. Anybody who finds my postings insulting, please write a > mail filter > for my address. Although I can not understand why it may insult > anybody. > > P.P.S. Must be related to cultural differences, I guess... It's not insulting to me, personally, but quite possibly to the author of that

Hello, I'm attempting to classify data using tree(). summary(tree()) indicates that I have a very good classification rate. What I'd like to know is which tokens in the data set are correctly classified and which are not. Is there a method for associating the classification with the token? I've been reading Chambers and Hastie (1992) chapter 9 on tree-based models, but find no

Thanks for all the help on my previous questions. One more (hopefully last one) : I've been very surprised when I tried to fit a model (using aov()) for a sample of size 200 and 10 variables and their interactions. It turns out that even 2GB of RAM is not anough for aov() with this sample size, which does not seem so big for me. Am I doing something wrong or this is considered a normal

could someone explain why the name of FPVAL gets " .value" concatenated onto it when the code below is run and temp is returned. I've been trying to figure this out for too long. It doesn't matter when I put the FPVAL in the return statement. It happens regardless of whether it's first or last. Thanks. f.lmmultenhanced <- function(response, pred1, pred2) {

I use knncat to make a predictive model and get misclass rate > knncat.m<-knncat(training.new,k=c(10,20),classcol=5) > knncat.m Training set misclass rate: 36.88% then I try to calculate prediction accuracy by the following: > pr.knncat.train <- predict (knncat.m,training.new,training.new,train.classcol=5,newdata.classcol=5) > tb.knncat.train <-table (pr.knncat.train,

Dear R community I have a question regarding the value of cost complexity parameter "k" used in "tree" package for pruning purpose. Any help in finding the optimum value of "k" is requested. Please give some suggestion in this regard. In the example below i used k=0 but i don't know why? But if i use k=NULL, then it will not plot the resultant tree.

hi, i'm working with mgcv packages and specially gam. My exemple is: >test<-gam(B~s(pred1)+s(pred2)) >plot(test,pages=1) when ploting test, you can view pred1 vs s(pred1, edf[1] ) & pred2 vs s(pred2, edf[2] ) I would like to know if there is a way to access to those terms (s(pred1) & s(pred2)). Does someone know how? the purpose is to access to equation of smooths terms

I was wondering, whether there is a way to have statistical significance test for cluster agreement. I know that I can use classAgreement() function to get Rand index, which will give me some indication whether the clusters agree or not, but it would be interesting to have a formal test. Thanks.

So I have 2 sets of data - a training data set and a test data set. I've been doing the analysis on the training data set and then using predict and feeding the test data through that. There are 114 rows in the training data and 117 in the test data and 1024 columns in both. It's actually the same set of data split into two. The rows are made of 5 different numbers. They do represent

THANKS so very much for your help (previous and future!). I have a two follow-up questions. 1) You say that dispersion = 1 by definition ....dispersion changes from 1 to 13.5 when I go from binomial to quasibinomial....does this suggest that I should use the binomial? i.e., is the dispersion factor more important that the 2) Is there a cutoff for too much overdispersion - mine seems to be

Say I fit a logistic model and want to calculate an odds ratio between 2 sets of predictors. It is easy to obtain the difference in the predicted logodds using the predict() function, and thus get a point-estimate OR. But I can't see how to obtain the confidence interval for such an OR. For example: model <- glm(chd ~age.cat + male + lowed, family=binomial(logit)) pred1 <-

I'm new to R and am trying to extract the factors of a dataframe using numeric indices (e.g. df[1]) that are input to a function definition instead of the other types of references (e.g. df$out). df[1] is a list(?) whose class is "dataframe". These indexed lists can be printed successfuly but are not agreeable to the plot() and lm() functions shown below as are their df$out

I have a function that, among other things, runs a linear model and returns r2. But, the number of predictor variables passed to the function changes from 1 to 3. How can I change the formula inside the function depending on the number of variables passed in? An example: get.model.fit <- function(response.dat, pred1.dat, pred2.dat = NULL, pred3.dat = NULL) { res <- lm(response.dat ~

Hi - How can I 'manually' reproduce the results in 'pred1' below? My attempt is pred_manual, but is not correct. Any help is much appreciated. library(splines) set.seed(12345) y <- rgamma(1000, shape =0.5) age <- rnorm(1000, 45, 10) glm1 <- glm(y ~ ns(age, 4), family=Gamma(link=log)) dd <- data.frame(age = 16:80) mm <- model.matrix( ~ ns(dd$age, 4)) pred1 <-

It is unclear to me how aov() handles non-categorical variables. I mean it works and produces results that I would expect, but I was under impression that ANOVA is only defined for categorical variables. In addition, help(aov) says that it "call to 'lm' for each stratum", which I presume means that it calls to lm() for every group of the categorical variable, however I

Hi, My questions concerns the ROCR package and I hope somebody here on the list can help - or point me to some better place. When evaluating a model's performane, like this: pred1 <- predict(model, ..., type="response") pred2 <- prediction(pred1, binary_classifier_vector) perf <- performance(pred, "sens", "spec") (Where "prediction" and