similar to: Classification trees and written conditions

Hi all, What parameter do I normally change in the rpart function? How do I set the "cp" option? Is there a way to read off error rate directly from the "rpart" function for training data; I imagine for testing data I have to apply a "predict", but for training data I guess the error count would be somewhere existing once the "rpart" function is

Hi all, I used rpart to fit a model, where the covariates in the model are categorical variables. Then I plotted the tree (mytree) and used the command "text" to add labels to the tree. In the nodes of the tree, the values of the covariates are represented with a, b or c. Is there a way to show the real value(s) of the variable in the nodes instead of a, b or c ? I found that the

Hi Everyone, I have been using the "tree" package for a while with no problems until now. When I run predict(tree, newdata), I get an error with the message "Corrupt tree" for about 50% of the trees that I generate with tree. For other trees, the predict function completes with no errors. I haven't identified a correlation between the corrupt trees and the working tree.

--- El lun, 12/9/11, Cristalina <pa100cia77@yahoo.es> escribió: De: Cristalina <pa100cia77@yahoo.es> Asunto: Re: [R-es] Cosinor Analysis Para: "Carlos Ortega" <coforfe@gmail.com> Fecha: lunes, 12 de septiembre, 2011 08:43 Hola,   Carlos, muchas gracias.   El método empleado en http://tolstoy.newcastle.edu.au/R/e6/help/09/01/0626.html (el url que se referencia

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

Dear all, I don't understand the following behaviour: Running compar.gee (in library ape ) with and without the option 'data', it give me different results Example: .... Start R .... > load("eiber.RData") > ls() [1] "gee.na" "mydata" "mytree" > library(ape) > # runnig with the option data= mydata > compar.gee(alt ~ R,

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

Hola a todos, estoy procesando unos datos con las siguientes intrucciones que creo son del paquete stats: cloud(Imp89 ~ ImpTot89 * NDA | Edad, data = mtif, +       screen = list(x = -90, y = 45), distance = .4, zoom = .7) y obtengo el error: Error en eval(substitute(groups), data, environment(formula)) :  argumento ''envir'' númerico no tiene longitud uno La verdad es que no se

Hello all, I have a problem with the way attributes are dealt with in the function xmlToList(), and I haven't been able to figure it out for days now. Say I have a document (produced by nmap) like this: > mydoc <- '<host starttime="1365204834" endtime="1365205860"><status state="up" reason="echo-reply"

Hola tengo q hacer un trabajo, donde me piden q todas las salidas q haga sean en Power Point (graficos, tablas, etc). Para eso habia empezado a leer algo del paquete R2PPT, pero la verdad es q hay cosas q no entiendo de este paquete. Queria saber si alguien estuvo trabajando con el y obtuvo buenos resultados o si han usado otro paquete para hacerlo. Por ultimo una pregunta: desde Sweave se

Hola, Estoy buscando una representación gráfica similar a la que da: ternatyplot del paquete vcd : data(Lifeboats) attach(Lifeboats) ternaryplot( Lifeboats[,4:6], pch = ifelse(side=="Port", 1, 19), col = ifelse(side=="Port", "red", "blue"), id = ifelse(men/total > 0.1, as.character(boat), NA), main = "Lifeboats on Titanic" ) Pero

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

Hi, I have done an analysis using 'rpart' to construct a Classification Tree. I am wanting to retain the output in tree form so that it is easily interpretable. However, I am wanting to compare the 'accuracy' of the tree to a Random Forest to estimate how much predictive ability is lost by using one simple tree. My understanding is that the error automatically displayed by the two

Estimados usuarios de R: Tengo que hacer una desagregación temporal de una serie anual del producto interno bruto en trimestral. ¿Hay alguna forma de utilizar la librería en matlab de Quilis de desagregación temporal en R? La libreria se puede bajar de http://www.mathworks.com/matlabcentral/fileexchange/24438-temporal-disaggregation-library Saludos, Sebastián.

Hola a todos, He echado un vistazo a alguno de los paquetes de R que incluyen clasificadores y no sonsigo encontrar ninguno que tenga alguna función para usar un clasificador de maxima probabilidad tradicional. Alquien sabe de alguna? Gracias Víctor. [[alternative HTML version deleted]]

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)

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] :

Hello I am trying to store output from a loop into an empty matrix. The current code I am using is: > M<-mat.or.vec(11,89) > for (j in list(3,91)) + {M[,(j-2)]<-pic(datain[,j], mytree)} datain is a matrix (11,91). I only want to use the pic() function on the columns 3:91. When I use this code output is stored into the 1st column of M and the 89th column of M but no other columns. Any

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