search for: mstop

...), fitmem = ctree_memory( bd, TRUE ), family = GaussReg(), control = boost_control( mstop = 2 ), weights = NULL ) Test case session on my computer: > dt=expand.grid(y=c(2,3,4), x1=c(1,2), x2=c(1,2)) > library(mboost) Loading required package: modeltools Loading required package: stats4 Loading required package:...

...=1,10,1)) } offset <- function (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,...

...ing o added function cv() to generate matrices for k-fold cross-validation, subsampling and bootstrap o new function stabsel() for stability selection with error control o added function model.weights() to extract the weights o added interface to expand model by increasing mstop in model[mstop] o alternative definition of degrees of freedom available o Interface changes: - class definition / Family() arguments changed - changed behavior of subset method (model[mstop]). Object is directly altered and not duplicated - argument &quot...

...ing o added function cv() to generate matrices for k-fold cross-validation, subsampling and bootstrap o new function stabsel() for stability selection with error control o added function model.weights() to extract the weights o added interface to expand model by increasing mstop in model[mstop] o alternative definition of degrees of freedom available o Interface changes: - class definition / Family() arguments changed - changed behavior of subset method (model[mstop]). Object is directly altered and not duplicated - argument &quot...

I am having trouble predicting new data with a model created from package mboost: > mb1<-glmboost(as.formula(formula1),data=data_train,control=boost_control(mstop=400,nu=.1)) > f.predict<-predict(mb1,newdata=data_train) Error in scale.default(X, center = cm, scale = FALSE) : length of 'center' must equal the number of columns of 'x' Ultimately I want to predict "data_test", but the orginal dataset won't even work. Ho...

...nly a part of my final solution). If the problem does not become more obvious I will provide more details. Thanks in advance, AS #### R code #### rm(list=ls(all=T)) library(mboost) data(bodyfat) ## Model fit bf_gam <- gamboost(DEXfat ~ ., data = bodyfat) aic <- AIC(bf_gam) fit <- bf_gam[mstop(aic)] ## partial prediction input <- fit$data$input i <- sort(unique(fit$ensemble[,1]))[1] a <- fit$ensembles[fit$ensemble[,1]==i] ### Why this does not work? a[[1]][[1]]$predict(newdata=90) [[alternative HTML version deleted]]

...ingful since the output of this regression needs to be implemented in a production system, and mboost doesn't even expose the ensembles. # default params for blackboost are a gaussian loss, and maxdepth of 2 m.mboost = blackboost(Y ~ X1 + X2, data=tdata, weights=t.ipcw, control=boost_control(mstop=100)) m.gbm = gbm(Y ~ X1 + X2, data=tdata, weights=t.ipcw, distribution="gaussian", interaction.depth=2, bag.fraction=1, n.trees=2500) # compare IPCW weighted squared loss sum((predict(m.mboost, newdata=tdata)-tdata$Y)^2 * t.ipcw) < sum((predict(m.gbm, newdata=tdata, n.trees=2500)-tda...

Hi UseRs - I am new to R, and could use some help making out-of-sample predictions using a boosting model (the mboost command). The issue is complicated by the fact that I have panel data (time by country), and am estimating the model separately for each country. FYI, this is monthly data and I have 1986m1 - 2009m12 for 9 countries. To give you a flavor of what I am doing, here is a simple