similar to: Function for misclassification rate/type I,II error??

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

Hi all, Can anyone who is familar with CART tell me what I missed in my tree code? library (MASS) myfit <- tree (y ~ x1 + x2 + x3 + x4 ) # tree.screens () # useless plot(myfit); text (myfit, all= TRUE, cex=0.5, pretty=0) # tile.tree (myfit, fgl$type) # useless # close.screen (all= TRUE) # useless My current tree plot resulted from above code shows as:

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

Hi users I'm student who is struggling with basic R programming. Would you please help me with this problem. "My english is bad" I hope that my question is clear: I have a matrix in wich there are two colmns( yp, yt) Yp: predicted values from my model. yt: true values ( my dependante variable y is a categorical;3 modalities (0,1,2) I don't know how to procede to calculate the

Hello I want to build a classification tree for a binary response variable while the condition for the final tree should be : The total misclassification for each group (zero or one) will be less then 10% . for example: if I have in the root 100 observations, 90 from group 0 and 10 from group 1, I want that in the final tree a maximum of 9 and 1 observations out of group 0 and 1, respectively,

I have a classification tree that I computed via the tree function (in the tree package). I'd like to compute a misclassification table (if that's the right term) on the data used to compute the tree. That is, I want to compute a table with the different classes (i.e.,levels of the response factor) on the rows and the columns, and where entry [i,j] is the number of times the tree

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.

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 users, In my psychometric test i have applied logistic regression on my data. My data consists of 50 predictors (22 continuous and 28 categorical) plus a binary response. Using glm(), stepAIC() i didn't get satisfactory result as misclassification rate is too high. I think categorical variables are responsible for this debacle. Some of them have more than 6 level (one has 10 level).

Hi, Until recently I was using the knncat classifier function of knncat on an old computer (2.12, Mac OS X 10.4), and everything worked great. However, now that I have updated to R 2.14.1 (on Mac OS X 10.7), knncat seems broken. Problems: 1. It seems to output verbose output by default, and regardless of whether I put 0 or 1 into the verbose option. 2. It seems to just predict

> From: Peter Dalgaard BSA <p.dalgaard@biostat.ku.dk> > > Martin Maechler <maechler@stat.math.ethz.ch> writes: > > > Reading the long output of ``cc -flags'', > > I see that more than average optimization is done using > > cc -xO[1-4] > > # And -O is -xO2. If you do want higher speed, you need to use other flags too, and -fast is a

Hi, Is there any package for logistic model selection using BIC and Mallow's Cp statistic? If not, then kindly suggest me some ways to deal with these problems. Thanks. -- View this message in context: http://www.nabble.com/Stepwise-logistic-model-selection-using-Cp-and-BIC-criteria-tf4464430.html#a12729613 Sent from the R help mailing list archive at Nabble.com.

B <- A <- factor(c("a", "b")) outer(A, B, "!=") Warning: "FUN" not meaningful for factors [,1] [,2] [1,] NA NA [2,] NA NA Now, this used to work in 0.63.2, but someone `improved' outer. There it did an implicit as.numeric. The problem is that get in match.fun does not understand group generics, and gets Browse[1]> FUN

Hello, I am pretty new to R, I have always used SAS and SAS products. My target variable is binary ('Y' and 'N') and i have about 14 predictor variables. My goal is to compare different variable selection methods like Forward, Backward, All possible subsests. I am using misclassification rate to pick the winner method. This is what i have as of now, Reg <- glm (Graduation ~.,

Hi, I think I'm missing something very obvious, but I am missing it, so I would be very grateful for help. I'm using rpart to analyse data on skull base morphology, essentially predicting sex from one or several skull base measurements. The sex of the people whose skulls are being studied is known, and lives as a factor (M,F) in the data. I want to get back predictions of gender, and

Hi everybody, I am new in e1071 and with SVMs. I am trying to understand the performance of SVMs but I face with a situation that I thought as not meaningful. I added the R code for you to see what I have done. /set.seed(1234) data <- data.frame( rbind(matrix(rnorm(1500, mean = 10, sd = 5),ncol = 10), matrix(rnorm(1500, mean = 5, sd = 5),ncol = 10))) class <- as.factor(rep(1:2,

Hello, I would like to know how to obtain the misclassification error when performing a boosting analisis with ADABAG package? With: > prop.table(Tesis.boostcv$confusion) I obtain the confusion matrix, but not the overall missclassification error. Thanks in advance, BSc. Cecilia Lezama Facultad de Ciencias - UDELAR Montevideo - Uruguay. [[alternative HTML version deleted]]

Hi, I am trying to model a dataset with the response variable Y, which has 6 levels { Great, Greater, Greatest, Weak, Weaker, Weakest}, and predictor variables X, with continuous and factor variables using random forests in R. The variable Y acts like an ordinal variable, but I recoded it as factor variable. I ran a simulation and got OOB estimate of error rate 60%. I validated against some

Greetings, I'm using MSM (mutli-state markov modeling) package to study the progression of fibrosis in U.S hepatitis C population. I find this is a very fascinating tool for an applied researcher like myself. I have a four stage progression only model without any absorbing stage, also assuming no misclassification error in the data for the time being. I also have a couple covariates in the

Hi users, I need to calculate .632 (and .632+) bootstrap and the cross-validation of area under curve (AUC) to compare my models. Is there any package for the same. I know about 'ipred' and using it i can calculate misclassification errors. Please help. It's urgent. -- View this message in context: