search for: bostonh

Dear R-users, Recently, I am facing some problems when converting mlbench data into matrix format. library(mlbench) data(BostonHousing) X<- BostonHousing[,1:13] y<-BostonHousing[,14] I want to convert X and y into matrix form. I am getting these obvious errors... > t(X)%*%y Error in t(X) %*% y : requires numeric/complex matrix/vector arguments > t(as.matrix(X))%*%(as.matrix(y)) Error in t(as.matrix(X)) %*% (as....

This question concerns rpart's facility for user-defined functions that accomplish splitting. I was interested in modifying the code so that in each terminal node, a linear regression is fit to the data. It seems that from the allowable inputs in the user-defined functions, that this may not be possible, since they have the form: function(y, wt, parms) (in the case of the

Hi, I am having problems passing arguments to method="gbm" using the train() function. I would like to train gbm using the laplace distribution or the quantile distribution. here is the code I used and the error: gbm.test <- train(x.enet, y.matrix[,7], method="gbm", distribution=list(name="quantile",alpha=0.5), verbose=FALSE,

...ound --- Debugging "stepAIC" showed me that the problem occurs in the step where "addterm" is called. "addterm" somehow doesn't seem to see the object "learndata" which is present (?) in the function's environment. Here is my code. The dataset "BostonHousing" is just an example for which this problem can be observed. "my.MC" is inspired by the "Lexical scoping" section in the R FAQ (http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html#Lexical-scoping). --- data(BostonHousing, package = "mlbench") my.MC <- functio...

I'm looking for guidance on how to implement forward stepwise regression using lmStepAIC in Caret. The stepwise "direction" appears to default to "backward". When I try to use "scope" to provide a lower and upper model, Caret still seems to default to "backward". Any thoughts on how I can make this work? Here is what I tried: itemonly <-

...le in the caret package shortly. We are also working on a similar port of C5.0 (also GPL'ed). The C code is very similar, so much of the Cubist changes can be extended. That said, we'd appreciate help if anyone wants to contribute. Here is an example cubist session: library(mlbench) data(BostonHousing) ## 1 committee and no instance-based correction, so just an M5 fit: mod1 <- cubist(x = BostonHousing[, -14], y = BostonHousing$medv) summary(mod1) ## example output: ## Cubist [Release 2.07 GPL Edition] Sun Apr 10 17:36:56 2011 ## --------------------------------- ## ## Target at...

...le in the caret package shortly. We are also working on a similar port of C5.0 (also GPL'ed). The C code is very similar, so much of the Cubist changes can be extended. That said, we'd appreciate help if anyone wants to contribute. Here is an example cubist session: library(mlbench) data(BostonHousing) ## 1 committee and no instance-based correction, so just an M5 fit: mod1 <- cubist(x = BostonHousing[, -14], y = BostonHousing$medv) summary(mod1) ## example output: ## Cubist [Release 2.07 GPL Edition] Sun Apr 10 17:36:56 2011 ## --------------------------------- ## ## Target at...

...cted by follows. Thank u! Apply the methods introduced in Sections 4.2.1 and 4.2.2, say the > rank-based variable selection and BIC criterions, to the Boston housing > data. >  The Boston housing data contains 506 observations, and is publicly available in the R package mlbench (dataset “BostonHousing”).  The response variable Y is the median value of owner-occupied homes (MEDV) in each of the 506 census tracts in the Boston Standard Metropolitan Statistical Areas, and there are thirteen predictor variables.  We are interested in the relationship between MEDV and the other predictor vari...

Hello, Is the first time I am using SNOW package and I am trying to tune the cost parameter for a linear SVM, where the cost (variable cost1) takes 10 values between 0.5 and 30. I have a large dataset and a pc which is not very powerful, so I need to tune the parameters using both CPUs of the pc. Somehow I cannot manage to do it. It seems that both CPUs are fitting the model for the same values