search for: ctree_control

Dear Group, What is the actual usage of "mtry" in ctree(), or specifically, ctree_control() since it's a single tree? Thanks in advance. Regards, Kelvin Lam, MSc. Analyst, Programming & Biostatistics Institute for Clinical Evaluative Sciences (ICES) 2075 Bayview Avenue, G179 Toronto, ON M4N 3M5 (416) 480-4055 Ext. 3057 Fax: (416) 480-6048 email: kelvin.lam@ices.on....

...get slot "responses" from an object (class "boost_data") that is not an S4 object Simple test case that produce bug: dt=expand.grid(y=c(2,3,4), x1=c(1,2), x2=c(1,2)) library(mboost) bd=boost_dpp(y ~ .,data=dt, weights = NULL) blackboost_fit(bd, tree_controls = ctree_control( teststat = "max", testtype = "Teststatistic", mincriterion = 0, maxdepth = 2...

...er set of factors. (I have the same question for missing values among numeric [non-factor] values, but I assume the answer is similar.) > require(party) > require(mlbench) > data(BreastCancer) > BreastCancer$Id <- NULL > ct <- ctree(Class ~ . , data=BreastCancer, controls = ctree_control(maxdepth = 1)) > ct Conditional inference tree with 2 terminal nodes Response: Class Inputs: Cl.thickness, Cell.size, Cell.shape, Marg.adhesion, Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli, Mitoses Number of observations: 699 1) Bare.nuclei == {1, 2}; criterion = 1,...

...(y, w) { p <- weighted.mean(y > 0, w) 1/(10+1) * log(10*p/1*(1 - p)) } CSAdaExp <- Family(ngradient = ngradient, loss = loss, offset = offset); model.blackboost <- blackboost(tr[,1:DIM], tr.y, family=CSAdaExp, weights=tr.w, control=boost_control(mstop=100, nu=0.1), tree_controls=ctree_control(teststat = "max",testtype = "Teststatistic",mincriterion = 0,maxdepth = 10)); or #loss <- function (y, f) #{ # exp(-y * f * ifelse(y==1,COST_FN,COST_FP)) #} #ngradient <- function (y, f, w = 1) #{ # y * ifelse(y==1,COST_FN,COST_FP) * exp(-y * f * ifelse(y==1,COST_F...

...;m trying to understand how to set graphical parameters for trees created with the party package.  For example take the following code:   library(party)     data(airquality)     airq <- subset(airquality, !is.na(Ozone))     airct <- ctree(Ozone ~ ., data = airq,                    controls = ctree_control(maxsurrogate = 3))     plot(airct)   My problem is, I've got a ctree that has 14 terminal nodes, and as a result of the default graphical paramters of plot(ctree), all of the text is completely undreadable (decison node text is too large and as a result, these nodes overlap one another, termina...

Hello. I have used the "party" package to generate a regression tree as follows: >origdata<-read.csv("origdata.csv") >ctrl<-ctree_control(mincriterion=0.99,maxdepth=10,minbucket=10) >test.ct<-ctree(Y~X1+X2+X3,data=origdata,control=ctrl) The above works fine. Orig data was my training data. I now have a test data file (testdata), and would like to run the testdata through the above tree to see predictions. I trie...

Hi, I am new to R and am using the ctree() function to do customer segmentation. I am using the following code to generate the tree: treedata$Response<-factor(treedata$Conversion) fit<-ctree(Response ~ .,controls=ctree_control(mincriterion=0.99,maxdepth=4),data=treedata) plot(fit) print(fit) The variable "Response" above equals 1 if the customer responded to an offering and 0 otherwise. Everything works great, however I am struggling to extract the information I need from the output. When I look at the outp...

Dear R-helpers, I am looking for a segmentation package that gives some "tree identifier" as output for every observation in the data set (my response variable is binary). I have skimmed through "rpart", "ada" and "adabag": The output "trees" gives you the formula, but I have to run several thousand segmentations on different data sets and it

Dear R.Users, using ctree() (from "party" library) on a data.frame, I want to append a column with the references for the groups/segments detected. While these nodes are easy readable in output, I need a vector for my obs. Hints? Cheers -- Daniele Medri

...l("try(load(\"C:\\Documents and Settings\\daten2.rda\"))"); v.voidEval("try(pdf(\"C:\\Documents and Settings\\test4.pdf\"))"); v.voidEval("plot (ctree(ZF2~TKL_f+Regio_f+km1000+SF_f+Geschlecht_f+Alter_VN+Random, data=daten2, weights=daten2$Tage, controls=ctree_control(mincriterion=0.95,maxdepth=4,teststat=\"quad\",testtype=\"Bonferroni\")), type=\"simple\")"); //line 6 v.voidEval("dev.off()"); }catch(RserveException e){e.printStackTrace();} I get the error-message "voidEval failed" for the line 6! Wh...

I have run the ctree function, and my dependent variable is broken into 3 categories: low cost, moderate cost and high cost. When i plot the results (eg. using plot(test.ct)), the plot shows, at the very bottom of each node, the probability of falling into each cost category. When i print the actual results (eg. using print(test.ct)), i get all of the backup

I tried to use ctree but am not sure about the meaning of the plot. My.data.ct<-ctree(Resp~., data=My.data) plot(My.data.ct) My data.frame contains 88 explanatory variables (continous,ordered/unordered multistate,count data) and one response with two groups. In the plot are only two variables shown (2 internal nodes) and 3 final nodes. Does it mean that only these two variables show a

...e-free survival (time and event): i want to know which variables can predict best a poor/good prognosis based on survival data. I am using function "ctree" from the "party" package. I came up with this command: test <- ctree(Surv(time, event)~., data =data.test, controls=ctree_control(teststat="max", testtype="Bonferroni", mincriterion=0.95,savesplitstats = TRUE), ytrafo = function(data)trafo(data, numeric_trafo = rank), xtrafo=function(data)trafo(data, surv_trafo=logrank_trafo(data, ties.method = "logrank")) ) which works well but as I am not a...

Hi, Being a novice this is my first usage of R. I am trying to use rpart for building a decision tree in R. And I have the following dataframe Outlook Temp Humidity Windy Class Sunny 75 70 Yes Play Sunny 80 90 Yes Don't Play Sunny 85 85 No Don't Play Sunny 72 95 No Don't Play Sunny 69 70 No Play Overcast 72 90 Yes Play Overcast 83 78 No Play Overcast 64 65 Yes Play Overcast 81 75