similar to: Fitting a predefined classification tree

I don't know of one. If building your own you could use rpart with the "maxdepth=1" as the tool to find the best split at each node. Terry Therneau On 12/20/2012 05:00 AM, r-help-request at r-project.org wrote: > Hi, > > I've searched R-help and haven't found an answer. I have a set of data from which I can create a classification tree using > rpart. However,

Dear all For my research, I am learning classification now. I was trying some example about classification tree pakages, such as tree and rpart, for instance, in Pima.te dataset have 8 variables (include class=type): library(rpart) library(datasets) pima.rpart <- rpart(type ~ npreg+glu+bp+skin+bmi+ped+age,data=Pima.te, method='class') plot(pima.rpart, uniform=TRUE) text(pima.rpart)

Hi Experts, I am new to R, using decision tree model for getting segmentation rules. A) Using behavioural data (attributes defining customer behaviour, ( example balances, number of accounts etc.) 1. Clustering: Cluster behavioural data to suitable number of clusters 2. Decision Tree: Using rpart classification tree for generating rules for segmentation using cluster number(cluster id) as target

I am trying to make a decision tree using rpart. The function runs very quickly considering the size of the data (1742, 163). When I call the summary command I get this: > summary(bookings.cart) Call: rpart(formula = totalRev ~ ., data = bookings, method = "class") n=1741 (1 observation deleted due to missingness) CP nsplit rel error 1 0 0 1 Error in yval[, 1] :

Hi, I am planning on using classification trees to build a predictive model for data which includes a random variable. I intend to use the R functions 'rpart' (and potentially also 'randomForest' and 'bagging'). I have a data set with 390 data points. The response variable is binary. There are a large number of variables (>20, both categorical and continuous). The

Hi, I created an rpart object and pruned the tree using 1-SE rule. I used 10-fold cross validation while creating the tree. Then, I extracted the cross-validated predictions for my data points using xpred.rpart and obtained some statistics like precision, recall, overall error rate, etc. However, these values change each time I run xpred.rpart because of the random shuffling going on before

Hi! I'm trying to construct and plot a decision tree to class a set of only 8 objects and tried to use the rpart and tree function, but get a error message both times: rpart: fit is not a tree, just a root tree: cannot plot singlenode tree I read in the post 'question regression trees' that rpart doesn't split a set of fewer than 20 objects...so I guess the same holds true for

Hello Everyone, I'm doing a Classification trees with categorical explanatory variables using library rpart and I would like to do a prediction for some data imputs. I don't know where's a function or how can I do it?. Is there someone can help ?? ¿. Here's the code that I'm using. library(rpart) fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis) plot(fit)

Hi dear R-help memebers, When building a CART model (specifically classification tree) using rpart, it is sometimes obvious that there are variables (X's) that are meaningful for predicting some of the outcome (y) variables - while other predictors are relevant for other outcome variables (y's only). *How can it be estimated, which explanatory variable is "used" for which of

Hi, I have a linear model ("mod1 <- lm(V3~V1+V2) and I would like to get the model's prediction at values of V1 and V2 not included in the original sample. samp <- read.table("data.dat",nrows=100) attach(samp) out.poly <- lm(V3 ~ V1 + V2) If I try to use out.poly to predict values for the function I run into problems. It seems that it isn't possible to use a new

I am trying to use the CART in a survival analysis. I have three variables of interest (all 3 ordinal - x, y and z, each of them with 5 categories) from which I want to make smaller groups (just an example 1st category from X variable with the 2nd and 3rd categories from the Y category and 2, 3 and 4 categories from the Z category etc) based on their, let's say, association with mortality. Now

My decision tree grows only with one split and based on what I see in E-Miner it should split on more variables. How can I adjust splitting criteria in R? Also is there way to indicate that some variables are binary, like variable Info_G is binary so in the results would be nice to see "2) Info_G=0" instead of "2) Info_G<0.5". Thank you in advance! And thanks for Eric who

Dear helpers, I am a Dutch student from the Erasmus University. For my Bachelor thesis I have written a script in R using boosting by means of classification and regression trees. This script uses the function the predefined function rpart. My input file consists of about 4000 vectors each having 2210 dimensions. In the third iteration R complains of a lack of memory, although in each iteration

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

I've been groping my way through a classification/discrimination problem, from a consulting client. There are 26 observations, with 4 possible categories and 24 (!!!) potential predictor variables. I tried using lda() on the first 7 predictor variables and got 24 of the 26 observations correctly classified. (Training and testing both on the complete data set --- just to get started.) I

Hi all, Does anyone knows how to add a new variable to the predefined variables sent by asterisk to AGI script? regards -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.digium.com/pipermail/asterisk-users/attachments/20090304/80ba7bd7/attachment.htm

Hello, I am trying to include legend text with math symbols from a predefined character variable that is read in from a file. ? If there is only one line of text in the legend, the following, although cumbersome, works for me: ? > LegendText = " 'U' [infinity], '=10 m/s' "?? # (read in from a file) ??> LegendName = paste("bquote(paste(",LegendText,

Hi all, I run R 2.11.1 under ubuntu 10.10 and caret version 2.88. I use the caret package to compare different models on a dataset. In order to compare their different performances I would like to use the same data partitions for every models. I understand that using a LGOCV or a boot type re-sampling method along with the "index" argument of the trainControl function, one is able to

Hi, I am trying to use CART to find an ideal cut-off value for a simple diagnostic test (ie when the test score is above x, diagnose the condition). When I put in the model fit=rpart(outcome ~ predictor1(TB144), method="class", data=data8) sometimes it gives me a tree with multiple nodes for the same predictor (see below for example of tree with 1 or multiple nodes). Is there a way

hello mailing list! i still consider myself an R beginner, so please bear with me if my questions seems strange. i'm in the field of biology, and have done consecutive hydraulic conductivity measurements in three parallels ("Sample"), resulting in three sets of conductivity values ("PLC" for percent loss of conductivity, relative to 100%) at multiple pressures