similar to: using rpart with a tree misclassification condition

Greetings R world, I know some version of the this question has been asked before, but i need to save the output of a loop into a data frame to eventually be written to a postgres data base with dbWriteTable. Some background. I have developed classifications models to help identify problem accounts. The logic is this, if the model classifies the record as including variable X and it turns out

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

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

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

Dear friends, I have several sets of points in a transformed environmental space. Each set of points can be represented as a cloud in the environmental space. This space is spanned by n coordinates, corresponding to the first n PCs of 36 PCs of some environmental variables (12 monthly minimum temperatures, 12 monthly maximum temperature, 12 monthly precipitations). I would like to calculate

HI Is there any function in R that tells us error rate(misclassification rate) for logistic regression type classification? i also want to know the function to determine type I and type II error. I have found a link where "misclass" and "confusion" are used. But I dont know the package name. http://alumni.media.mit.edu/~tpminka/courses/36-350.2001/lectures/day32/

Hi All, I have some questions on using library rpart. Given my data below, the plotcp gives me increasing 'xerrors' across different cp's with huge xstd (plot attached). What causes the problem or it's not a problem at all? I am thinking 'xerror's should be decreasing when 'cp' gets smaller. Also what the 'xstd' really tells us? If the error bars for

Greetings, I am using rpart for classification with "class" method. The test data is the Indian diabetes data from package mlbench. I fitted a classification tree firstly using the original data, and then exchanged the order of Body mass and Plasma glucose which are the strongest/important variables in the growing phase. The second tree is a little different from the first one. The

Greetings, I checked the Indian diabetes data again and get one tree for the data with reordered columns and another tree for the original data. I compared these two trees, the split points for these two trees are exactly the same but the fitted classes are not the same for some cases. And the misclassification errors are different too. I know how CART deal with ties --- even we are using the

Now available on CRAN. Package: mspath Title: Multi-state Path-Dependent Models in Discrete Time Description: Functions for fitting path-dependent (non-Markov) multi-state models to categorical processes observed at arbitrary times, optionally with misclassified responses, and covariates on transition or misclassification rates. Uses discrete-time approximation. Based on the Jackson's

Now available on CRAN. Package: mspath Title: Multi-state Path-Dependent Models in Discrete Time Description: Functions for fitting path-dependent (non-Markov) multi-state models to categorical processes observed at arbitrary times, optionally with misclassified responses, and covariates on transition or misclassification rates. Uses discrete-time approximation. Based on the Jackson's

Hi, I've searched online, in a few books, and in the archives, but haven't seen this. I believe that xerror is scaled to rel error on the first split. After fitting an rpart object, is it possible with a little math to determine the percentage of true classifications represented by a xerror value? -seth -- View this message in context:

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:

Hello: I am using rpart and would like more background on how the splits are made and how to interpret results - also how to properly use text(.rpart). I have looked through Venables and Ripley and through the rpart help and still have some questions. If there is a source (say, Breiman et al) on decision trees that would clear this all up, please let me know. The questions below pertain to a

Hello I am trying to make a graph of 10 different lines built each from 4 different segments and to add a darker line that will represent the average of all graphs - all in the same plot.Actually each line is a ROC plot The code I'm using for plotting one line is as follows: logit.roc.plot <- function(r, title="ROC curve") { old.par <- par(no.readonly = TRUE);

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 there. I was wondering if somebody knows how to perform a bagging procedure on a classification tree without running the classifier with weights. Let me first explain why I need this and then give some details of what I have found out so far. I am thinking about implementing the bagging procedure in Matlab. Matlab has a simple classification tree function (in their Statistics toolbox) but

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,

Dear R users, I'm working with the rpart package and want to evaluate the performance of user defined split functions. I have some problems in understanding the meaning of the xval argument in the two functions rpart.control and xpred.rpart. In the former it is defined as the number of cross-validations while in the latter it is defined as the number of cross-validation groups. If I am

Dear experts, I'm new in R and trying to learn by writing a version of the Perceptron Algorithm. How can I tell in the code below to stop the iteration when the condition in the "for loop" is not satisfied for all training examples? Thanks in advance for your help! ## Generate a linearly separable data set in R2 sample <- as.data.frame(cbind(runif(n=100),runif(n=100)))