similar to: help with programming

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

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 <-

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,

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))

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

Hello, I'm currently dealing with a rather strange problem when using the function "stepAIC" ("MASS" package). The setting is the following: From model learning data sets ("learndata"), I want to be able to build prediction functions (in order to save them in a file for further use). This is done by the function "pred.function" (see below). Therein,

I am having problem using the step function for a linear regression model. I've created an initial model containing only the intercept. Then using the step function, I've selected three variables to be considered for the model. > x0.lm<- lm(MEDV~1, data = x) > > anova(x0.lm) Analysis of Variance Table Response: MEDV Df Sum Sq Mean Sq F value Pr(>F)

Dear All, I have data which contains 14 variables. And I have to regress one of variables on each variable (simple 13 linear regressions) I try to make a loop and store only R-squared colnames(boston) [1] "CRIM" "ZN" "INDUS" "CHAS" "NOX" "RM" "AGE" [8] "DIS" "RAD"

Dear all, I'm having some problems with using the step() function inside another function. I think it is an environment problem but I do not know how to overcome it. Any suggestions are appreciated. I've prepared a simple example to illustrate my problem: > library(MASS) > data(Boston) > my.fun <- function(dataset) { + l <- lm(medv ~ .,data=dataset) + final.l <-

Cubist is a rule-based machine learning model for regression. Parts of the Cubist model are described in: Quinlan. Learning with continuous classes. Proceedings of the 5th Australian Joint Conference On Artificial Intelligence (1992) pp. 343-348 Quinlan. Combining instance-based and model-based learning. Proceedings of the Tenth International Conference on Machine Learning

Cubist is a rule-based machine learning model for regression. Parts of the Cubist model are described in: Quinlan. Learning with continuous classes. Proceedings of the 5th Australian Joint Conference On Artificial Intelligence (1992) pp. 343-348 Quinlan. Combining instance-based and model-based learning. Proceedings of the Tenth International Conference on Machine Learning

According to the man page for formula, "a formula object has an associated environment". However, update.default doesn't use this environment, which creates problems like the following: make.model <- function(x) { lm(medv~.,x) } library(MASS) data(Boston) fit = make.model(Boston) fit = update(fit,".~.-crim") # Object "x" not found Here is a

I've made a package from some benchmark datasets for use with R and uploaded it to CRAN. Here's the Index entry: mlbench-0.1.tar.gz: A collection of artificial and real-world machine learning benchmark problems, including, e.g., the boston housing data from the UCI repository. Written/packaged by Fritz Leisch <Friedrich.Leisch at ci.tuwien.ac.at> Original data sets from

I've made a package from some benchmark datasets for use with R and uploaded it to CRAN. Here's the Index entry: mlbench-0.1.tar.gz: A collection of artificial and real-world machine learning benchmark problems, including, e.g., the boston housing data from the UCI repository. Written/packaged by Fritz Leisch <Friedrich.Leisch at ci.tuwien.ac.at> Original data sets from

I've made a package from some benchmark datasets for use with R and uploaded it to CRAN. Here's the Index entry: mlbench-0.1.tar.gz: A collection of artificial and real-world machine learning benchmark problems, including, e.g., the boston housing data from the UCI repository. Written/packaged by Fritz Leisch <Friedrich.Leisch at ci.tuwien.ac.at> Original data sets from

I would like to know if it is possible to perform negative binomial regression with rate data (incidence density) using the glm.nb (in MASS) function. I used the poisson regression glm call to assess the count of injuries across census tracts. The glm request was adjusted to handle the data as rates using the offset parameter since the population of census tracts can vary by a factor of

On Wed, 29 Nov 2000, Matt Pocernich wrote: > I am having problem using the step function for a linear regression model. I've created an initial model containing only the intercept. Then using the step function, I've selected three variables to be considered for the model. > > > > x0.lm<- lm(MEDV~1, data = x) > > > > anova(x0.lm) > Analysis of

On Wed, 29 Nov 2000, Matt Pocernich wrote: > I am having problem using the step function for a linear regression model. I've created an initial model containing only the intercept. Then using the step function, I've selected three variables to be considered for the model. > > > > x0.lm<- lm(MEDV~1, data = x) > > > > anova(x0.lm) > Analysis of

> -----Original Message----- > From: Chris Murphy > Sent: Tuesday, February 17, 2015 20:48 > > On Tue, Feb 17, 2015 at 7:54 AM, Jason Pyeron wrote: > >> I'd post the entire dmesg somewhere > > > > http://client.pdinc.us/panic-341e97c30b5a4cb774942bae32d3f163.log > > At least part of the problem happens before this log starts. Feb 15 23:41:19

> -----Original Message----- > From: Chris Murphy > Sent: Tuesday, February 17, 2015 3:58 > > I think the panic is the consequence of drive write failure. > So the actual > problem is before the panic call trace. Most of the time it panics without any warning, but once there was: > > -----Original Message----- > > From: Jason Pyeron > > Sent: Sunday,