search for: mincriterion

Like earth can be trained simultaneously for degree and nprune, is there a way to train ctree simultaneously for mincriterion and maxdepth? Also, I notice there are separate methods ctree and ctree2, and if both options are attempted to tune with one method, the summary averages the option it doesn't support. The full log is attached, and notice these lines below for method="ctree" where maxdepth=c(2,4)...

Hi, Could anyone help me understand how the mincriterion threshold works in ctree and cforest of the party package? I've seen examples which state that to satisfy the p < 0.05 condition before splitting I should use mincriterion = 0.95 while the documentation suggests I should use mincriterion = qnorm(0.95) which would obviously feed the function...

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

...n a ctree model as: model <- train(dat.dts, dat.dts.class, method='ctree', tuneLength=8, trControl=trainControl(number = 5, workers=1), metric='Kappa') I get the following puzzling numbers: mincriterion Accuracy Kappa Accuracy SD Kappa SD 0.01 0.961 0.0609 0.00151 0.0264 0.15 0.962 0.049 0.00116 0.0248 0.29 0.963 0.0405 0.00227 0.035 0.43 0.964 0.0349 0.00257 0.0247 0.57 0.964 0.0382 0.0022...

...data=dt, weights = NULL) blackboost_fit(bd, tree_controls = ctree_control( teststat = "max", testtype = "Teststatistic", mincriterion = 0, maxdepth = 2 ), fitmem = ctree_memory( bd, TRUE ), family =...

...ict 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 statistician it is quite confusing and i might not run it properly. My...

...)) } 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_FN,COST_FP)) #} #offset <- function (y, w) #{ # p <- weighted.mean(y...

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 tried using the f...

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 output from print...

...shuffled=shuffled) { require(party) require(epiR) test<-rownumber[[i]] train<-shuffled[shuffled!=test] cv.train <- ml.frame[train,] cv.evaluate <- ml.frame[test,] cv.model <- cforest(as.factor(remitter)~., data=cv.train, control=cforest_control(mtry=mtry, ntree=ntrees, mincriterion=0.95)) cv.evaluate$prediction <- predict(cv.model, newdata=cv.evaluate) obj.pred<-predict(cv.model) pred.table<-table(cv.evaluate$prediction, cv.evaluate$remitter) truepos<-pred.table[1,1] trueneg<-pred.table[2,2] falsepos<-pred.table[2,1] falseneg<-pred.table...

...complete.cases(arabidopsis)) arabidopsis <- subset(arabidopsis, is.na(arabidopsis$edit)==FALSE) arabidopsis <- arabidopsis[, !(names(arabidopsis) %in% c("X0", "loc"))] my_cforest_control <- cforest_control(teststat = "quad", testtype = "Univ", mincriterion = 0, ntree = 50, mtry = 3, replace = TRUE) my_cforest <- cforest(edit ~ ., data = arabidopsis, controls = my_cforest_control) varimp_cforest <- varimp(my_cforest) By the way, the same issue arises for the randomForest package. Does anyone have any suggestions? I...

...ad(\"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! What can I do?...

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

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