search for: cforest_unbiased

...I am using cforest to predict age of fishes using several variables; as it is rather difficult to age fishes I would like to show that a small subset of fish (training dataset) can be aged, then using RF analysis, age can accurately be predicted to the remaining individuals not in the subsample. In cforest_unbiased the samples are drawn without replacement and so it creates a default testing dataset (approx 35%) and training dataset from the rest. My question is that if I have already separated my data into a testing and training dataset prior to RF analysis is there a reason I should not set the fraction opt...

...sing the Party and RandomForest packages. Any comment is welcome and useful. myparty <- cforest(SalePrice ~ ModelID+ ProductGroup+ ProductGroupDesc+MfgYear+saledate3+saleday+ salemonth, data = trainRF, control = cforest_unbiased(mtry = 3, ntree=300, trace=TRUE)) rf_model <- randomForest(SalePrice ~ ModelID+ ProductGroup+ ProductGroupDesc+MfgYear+saledate3+saleday+ salemonth, data = trainRF,na.action = na.omit, importance=TRUE, do...

...ll. In total I have 20 features with 1100 observations. I checked the type my data in R using class(my_data_cell), no factor has been observed. I received a commond error like others did from the past. > lu = read.csv(file=file.choose()) > lu.cf <- cforest(Target ~ ., data = lu, control = cforest_unbiased(mtry = 2, ntree = 50)) > lu.cf <- cforest(Target ~ ., data = lu, control = cforest_unbiased(mtry = 2, ntree = 100)) > cvi_lu = varimp(lu.cf,threshold = 0.2,conditional= TRUE,OOB=TRUE) Error in model.matrix.default(as.formula(f), data = blocks) : allocMatrix: too many elements specified...

...ead.csv("/Users/Abigail/Documents/OldData250412.csv") OLDDATA$YD <- factor(OLDDATA$YD, label=c("Yes", "No"))? OLDDATA$ND <- factor(OLDDATA$ND, label=c("Yes", "No"))? attach(OLDDATA) defaults <- cbind(YD, ND) set.seed(47) data.controls <- cforest_unbiased(ntree=500, mtry=3) data.cforest <- cforest(defaults~LN+LV+LT+RV+MR+TL+DIA+CB, data = OLDDATA, controls=data.controls) data.cforest.varimp <- varimp(data.cforest, conditional = TRUE) barplot(sort(data.cforest.varimp)) And this is the error I get: > data.cforest <- cforest(defaults~LN...

...57486 Max. :11.76877 > library(HH) <output deleted> > vif(y ~ ., data=df) x1 x2 x3 x4 x5 x6 x7 x8 1.374583 1.252250 1.021672 1.218801 1.015124 1.439868 1.075546 1.060580 > library(party) <output deleted> > mycontrols <- cforest_unbiased(ntree=50, mtry=3) # Small forest but requires a few minutes > myforest <- cforest(y ~ ., data=df, controls=mycontrols) > varimp(myforest) x1 x2 x3 x4 x5 x6 x7 x8 11.924498 103.180195 16.228864 30.658946 5.053500 12.82...

...guess it might be something to do with the very large number of variables (e.g. 23 variables, 250 or so data points) but I was wondering if anyone had any other ideas. It works fine for regular variable importance calculation. Code: biomass.cf<-cforest(Total.biomass ~ ., data=biomass, control=cforest_unbiased(ntree=2500, mtry=8)) biomass.cf.vi<-varimp(biomass.cf, conditional=TRUE) Error: Error in if (node[[5]][[1]] == variableID) cp <- node[[5]][[3]] : argument is of length zero In addition: Warning messages: 1: In matrix(as.logical(cl), nrow = nlevels(x)) : data length [2] is not a sub-mult...

...************* someguys use the following codes to plot, but I can not use the treeresponse commad in randomforest object directly. # create model using random forest and bagging ensemble using conditional inference trees 035 x.cf <- cforest(Class ~ ., data=BreastCancer[ind == 1,], control = cforest_unbiased(mtry = ncol(BreastCancer)-2)) 036 x.cf.pred <- predict(x.cf, newdata=BreastCancer[ind == 2,]) 037 x.cf.prob <- 1- unlist(treeresponse(x.cf, BreastCancer[ind == 2,]), use.names=F)[seq(1,nrow(BreastCancer[ind == 2,])*2,2)] 038 -- Sincerely, Changbin -- Changbin Du DOE Joint Ge...

> > Hello everyone, > > I use the following command lines to get important variable from training > dataset. > > > data.controls <- cforest_unbiased(ntree=500, mtry=3) > data.cforest <- cforest(V1~.,data=rawinput,controls=data.controls) > data.cforest.varimp <- varimp(data.cforest, conditional = TRUE) > > I got error: "Error in model.matrix.default(as.formula(f),data = blocks): > term 1 would require 4e+17 columns&quo...

...randomForest and that solved my space issue. Is there a similar option for cforest (besides savesplitstats = FALSE, which isn't helping) below is my code and error message Thanks in advance! > fit <- cforest(formula = y ~ x1 + x2+ x3+ x4+ x5+ + x6+ x7+ x8+ x9+ x10, data=data, control= cforest_unbiased(savesplitstats = FALSE, ntree = 50, mtry = 5) 1: In mf$data <- data : Reached total allocation of 3955Mb: see help(memory.size) 2: In mf$data <- data : Reached total allocation of 3955Mb: see help(memory.size) -- View this message in context: http://r.789695.n4.nabble.com/cforest-keep-...

...igned as a flexible research tool and is not optimized wrt speed or memory consumption. Best, Torsten > > below is my code and error message > > Thanks in advance! > >> fit <- cforest(formula = y ~ x1 + x2+ x3+ x4+ x5+ > + x6+ x7+ x8+ x9+ x10, data=data, control= > cforest_unbiased(savesplitstats = > FALSE, ntree = 50, mtry = 5) > > 1: In mf$data <- data : > Reached total allocation of 3955Mb: see help(memory.size) > 2: In mf$data <- data : > Reached total allocation of 3955Mb: see help(memory.size) > > > -- > View this message in context...

#Conditional inference forest ("Party" package) error message states that levels in factors of new data do not match original data, but they do... #create conditional inference forest oc_listed.fit1 <- cforest(Listed~ HabMode,controls=cforest_unbiased(ntree=500), data=oc.complete) #use predict function for subset of data #this works correctly predict(oc_listed.fit1,newdata=oc.complete[1:10,]) #use predict on new set of data predict(oc_listed.fit1,newdata=DD_NOT) #produces this error message #Error in checkData(oldData, RET) : #Levels in fa...

...; error will be seen which is simply due to the small substitute data set and of no concern. rm(list=ls()) library(party) library(reshape) puthere <- c("TEST_RESULTS.csv") hsb2 <- read.csv("http://www.ats.ucla.edu/stat/data/hsb2.csv") names(hsb2) set.seed(8296) ctrl <- cforest_unbiased(ntree=500, mtry=2) varlist <- names(hsb2)[3:4] for (h in varlist){ for (k in c(1,0)){ for (i in c(1,2)){ ## Data subset filtered <- subset(hsb2, schtyp == i & female == k, select = c(id:socst)) rank.cf <- cforest(h ~ write + math + science + socst, data = filtered, control = ctrl) p...

...would append/write sequential results as a new column in the file as opposed to being in list form? Your comments are appreciated -- Jay Script in question: > library(party) > rm(list=ls()) > Dynamic <- read.csv(file="Dynamic_DATA.csv") > set.seed(1851) > ctrl <- cforest_unbiased(ntree=500, mtry=5) > > for (i in 1:4){ ## Climate subset + occupied <- subset(Dynamic, WDOccupancy == 1 & Climate == i, select = c(DataSet:DGI)) + Dynamic.cf <- cforest(Fan ~ FormH + FormV + Uratio + Void + Transmis, data = occupied, control = ctrl) + print(Dynamic.cf) + ## round(va...

...library(HH) > <output deleted> >> vif(y ~ ., data=df) > x1 x2 x3 x4 x5 x6 x7 x8 > 1.374583 1.252250 1.021672 1.218801 1.015124 1.439868 1.075546 1.060580 >> library(party) > <output deleted> >> mycontrols <- cforest_unbiased(ntree=50, mtry=3) # Small >> forest > but requires a few minutes >> myforest <- cforest(y ~ ., data=df, controls=mycontrols) >> varimp(myforest) > x1 x2 x3 x4 x5 x6 > x7 > x8 > 11.924498 103.180195 16.22...