similar to: Probability trees

Hi everyone, this email basically pursues two distinct main goals. I appreciate any help! First of all I was wondering if there is any possibility to get WEKA files (coded in Java) to run in R respectively RWeka. I first considered if it was possible with the Weka_interface implemented in RWeka but apparently it is not. Is there any way? Secondly I am looking for implementations to produce

Dear all, I've used glm(family=binomial(link="logit")) several times, but now I think that a log link is more appropriate. I want to fit a model for probability of tree fall (TF)), with tree diameter (dbh) and soil moisure (soil) as predictors. A large number of trees have been checked every second year whether they stand up (0) or have fallen (1). I assume that the tree fall

Hello all, Suppose in a logistic regression model, the binary outcome is coded as 0 or 1. In SAS, the default probability computed is for Y = 0 (smaller of the two values) . However, in SPSS the probability computed is for Y = 1 (greater of the two values). Which one does R compute, the probability for the smaller or the greater value? I went through the documentation in a hurry but

Hello everyone, I working in a public health project and we have created a Decision Tree for categorical variables usign the package rpart. Our goal is to develop a model (Using the ROC tool) in order to predict presence/ausent of  diabetes and get a better understanding  of what are the important factors in a particular chilean population.  There are some importants variable that we have found. 

Hi, I am working with CART regression now to predict a probability; the response is binary. Could anyone tell me in which cases it is better to use mean square error for splitting nodes and when mean absolute error should be preferred. I am now using the default (MSE) version and I can see that the obtained optimal tree is very different from the tree with the least mean absolute error. Thanks in

Dear all , I would like to use the function randomForest to predict the probability of relocation failure of a GPS collar as a function of several environmental variables x (both factor and numeric: slope, vegetation, etc.) on a given area. The response variable y is thus success (0)/failure(1) of the relocation, and the sampling unit is the pixel of a raster map. My aim is to build a map

Since you only have 3 predictors, each categorical with a small number of categories, you can use expand.grid to make a data.frame containing all possible combinations and give that the predict method for your model to get all possible predictions. Something like the following untested code. newdata <- expand.grid( Humidity = levels(Humidity), #(High, Medium,Low)

I still need the output to match my requiremnt in my original post. With decision rules "clusters" and probability attached to them. The examples are sort of similar. You just provided links to general info about trees. Sent from my Verizon, Samsung Galaxy smartphone<div> </div><div> </div><!-- originalMessage --><div>-------- Original message

Tjats great that you are familiar and thanks for responding. Have you ever done what I am referring to? I have alteady spent time going through links and tutorials about decision trees and random forrests and have even used them both before. Mike On Apr 13, 2016 5:32 PM, "Sarah Goslee" <sarah.goslee at gmail.com> wrote: It sounds like you want classification or regression trees.

Hello, I've been trying to figure out how to access the individual elements from an object of class phylo. I am reading in 201 trees created by paup as below. > read.nexus("A_30knj_200t.txt", tree.names= NULL) -> anj30 > anj30[1] $tree1 $edge [,1] [,2] [1,] "-1" "-2" [2,] "-2" "-3" [3,] "-3" "1"

I need the output to have groups and the probability any given record in that group then has of being in the response class. Just like my email in the beginning i need the output that looks like if A and if B and if C then %77 it will be D. The examples you provided are just simply not similar. They are different and would take interpretation to get what i need. On Apr 14, 2016 1:26 AM,

Ah yes I will have to use the predict function. But the predict function will not get me there really. If I can take the example that I have a model predicting whether or not I will play golf (this is the dependent value), and there are three independent variables Humidity(High, Medium, Low), Pending_Chores(Taxes, None, Laundry, Car Maintenance) and Wind (High, Low). I would like rules like

Hi Roscoe, I couldn't make it though that excel worksheet without my eyes glazing over... but do you want something like data(mtcars) tab <- xtabs(~cyl + gear, data=mtcars) tab/sum(tab) ? Best, Isat On Mon, May 3, 2010 at 10:08 PM, Roscoe <Roscoe at exemail.com.au> wrote: > Dear R-Help, > > First post, be gentle... > > I am new to R: I can perform basic functions

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

Dear all, I am trying to create a consensus skyline plot using the "ape" package(newbie). I have a nexus file that looks like the one from above containing many trees. (my_file.trees) #NEXUS begin trees; [Treefile generated by sim_coal.exe (Laurent Excoffier)] tree true_tree_1 = [&U] (((13.1:6, 43.1:5):12, 28.1:14):284, (((((21.1:0, (20.1:1, 4.1:0):0):1, ((37.1:0,

Hi R User, I was trying to find a final model in the following example by using the Boosted regression trees (GBM). The program gives the fitted values but I wanted to calculate the fitted value by hand to understand in depth. Would you give moe some hints on what is the final model for this example? Thanks KG ------- The following script I used #----------------------- library(dismo)

I have used packages rpart, mvpart and tree for classification and regression trees. I want to calculate fraction of null deviance explained by each node and variable in the tree. For instance, at the first split, this would be (1 - (sum of residual deviance in each of the two leaves)/deviance at the root). In the subsequent splits, this formula is slightly different. There probably is a function

Dear R users, I’m trying to understand how correlated predictors impact the Relative Importance measure in Stochastic Boosting Trees (J. Friedman). As Friedman described “ …with single decision trees (referring to Brieman’s CART algorithm), the relative importance measure is augmented by a strategy involving surrogate splits intended to uncover the masking of influential variables by others

Hi, I am using rpart decision trees to analyze customer churn. I am finding that the decision trees created are not effective because they are not able to recognize factors that influence churn. I have created an example situation below. What do I need to do to for rpart to build a tree with the variable experience? My guess is that this would happen if rpart used the loss matrix while creating

Hi, while working with decision trees and unbalanced data, I came across the use of the Hellinger distance as an alternative to information gain [1,2], when dealing with skewed data. Does anybody know of R implementations of this approach to decision trees? Thanks, [1] http://www.cse.nd.edu/Reports/2008/TR-2008-06.pdf [2] http://csmr.ca.sandia.gov/~wpk/slides/wdmda-sem.pdf -- Rajarshi Guha NIH