similar to: Using bartMachine with the caret package

R-Listers: I am doing a quasi-maximum likelihood estimation and I get a "subscript out of bound" error message, Typically I would think this means that a subscript used in the function is literally out of bounds however I don't think this is the case. All I change in the code is a constant, that is hard-wired in (not data dependent and not parameter dependent), furthermore,

Hi, I was very much hoping someone could help me with the following. I am trying to convert some SAS NLMIXED code to NLME in R (v.2.1), but I get an error message. Does anyone have any suggestions? I think my error is with the random effect "u" which seems to be parametrized differently in the SAS code. In case it's helpful, what I am essentially trying to do is estimate parameters

Hi All, I tried to use xgboost to model and predict count data. The predictions are however not as expected as shown below. # sponge count data in library(spm) library(spm) data(sponge) data(sponge.grid) names(sponge) [1] "easting" "northing" "sponge" "tpi3" "var7" "entro7" "bs34" "bs11"

Dear All I am trying to estimate a mixture of regression and get the following error using the mixreg package: Error in y - yhat : non-conformable arrays The instruction I used were: x <- as.matrix(LRHUN) y <- as.matrix(LRINTER) TS <- list(list(beta=c(3.0,1.0),sigsq=1,lambda=0.4), list(beta=c(0.0,1.0),sigsq=1,lambda=0.6)) prova <- mixreg(x,y, ncomp=2, theta.start=TS)

I am trying to run the following function (a hierarchical bayes linear model) and receive the error in solve.default. The function was originally written for an older version of SPlus. Can anyone give me some insights into where the problem is? Thanks R 2.4.1 on MAC OSX 2mb ram Mark Grant markg at uic.edu > attach(Aspirin.frame) > hblm(Diff ~ 1, s = SE) Error in solve.default(R, rinv)

You should read the section on Indexing in the Introduction to R document that comes with R, regarding $ and `[[`. -- Sent from my phone. Please excuse my brevity. On August 1, 2017 2:44:18 AM PDT, Dimlak Gorkehgz <rain8dome9 at gmail.com> wrote: >You are right, maintainer does keep a list of model's packages. > >So how do I use a variable instead of $adaboost$? >

You are right, maintainer does keep a list of model's packages. So how do I use a variable instead of $adaboost$? getModelInfo()$adaboost$library Also, server not found: http://rwiki.sciviews.org/doku.php?id=getting-started:reference-cards:getting-help On Tue, Aug 1, 2017 at 11:46 AM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > I have provided you all the

Dear All, reading an article by Rodolphe Thiebaut and Helene Jacqmin-Gadda ("Mixed models for longitudinal left-censored repeated measures") I have found this program in SAS proc nlmixed data=TEST QTOL=1E-6; parms sigsq1=0.44 ro=0.09 sigsq2=0.07 sigsqe=0.18 alpha=3.08 beta=0.43; bounds $B!](B1< ro < 1, sigsq1 sigsq2 sigsqe >= 0; pi=2*arsin(1); mu=alpha+beta*TIME+a i+b i*TIME;

I want to run some R script using the inline package (which allows to create and run inline C++ code in my humble understanding). So, after loading the required packages and copy and paste the example that runs C code (in the Reference Manual as a PDF), I have a compilation error. Any body has ever tried this inline package? -- View this message in context:

An executable version 0.6 of my libraries is now available at www.luc.ac.be/~jlindsey/rcode.html This works with MS R0.64.2 and appears possibly to work with R65.0. There is a serious problem with the Fortran compiler as some of the examples for elliptic and carma crash it. These same examples do not crash R63.0 with the library executables of Jan 99. I am releasing this anyway because of the

Hola compañeros de la lista. Los molesto con la siguiente duda. En un diseño central compuesto (CCD) con dos factores (V1 y V2) y una variable de respuesta (R), utilizando valores codificados (-1.4142, -1, 0, 1, 1.4182), al aplicar la orden: rsm.segundo.orden <- rsm(R ~ Bloque + SO(V1, V2), data = DATOS.Codificados) Obtengo el siguiente modelo: R = 103.92 -2.16

Dear R-experts, Here below a toy example with some error messages, especially at the end of the code (Tuning the parameters). Your help to correct my R code would be highly appreciated. ####################################### #libraries library(lattice) library(ggplot2) library(caret) library(randomForest) ?? #Data

Hi. I am trying to construct a svmLinear model using the "caret" package (see code below). Using the same data, without changing any setting, sometimes it constructs the model successfully, and sometimes I get an index out of bounds error. Is this unexpected behaviour? I would appreciate any insights this issue. Thanks. ~Kendric > train.y [1] S S S S R R R R R R R R R R R R R R R

I am used to packages like e1071 where you have a tune step and then pass your tunings to train. It seems with caret, tuning and training are both handled by train. I am using train and trainControl to find my hyper parameters like so: MyTrainControl=trainControl( method = "cv", number=5, returnResamp = "all", classProbs = TRUE ) rbfSVM <- train(label~., data =

Hi list, I´m working on a predictive modeling task using the caret package. I found the best model parameters using the train() and trainControl() command. Now I want to evaluate my model and make predictions on a test dataset. I tried to follow the instructions in the manual and the vignettes but unfortunately I´m getting an error message I can`t figure out. Here is my code: rfControl <-

Hi Sacha, On second thought, perhaps this is more the direction that you want ... X2 = cbind(X_train,y_train) colnames(X2)[3] = "y" regr2<-randomForest(y~x1+x2, data=X2,maxnodes=10, ntree=10) regr regr2 #Make prediction predictions= predict(regr, X_test) predictions2= predict(regr2, X_test) HTH, Eric On Tue, May 9, 2023 at 6:40?AM Eric Berger <ericjberger at gmail.com>

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,

Here is a snippet to show what i'm trying to do. library(randomForest) library(ROCR) library(caret) data(iris) iris <- iris[(iris$Species != "setosa"),] fit <- randomForest(factor(Species) ~ ., data=iris, ntree=50) train.predict <- predict(fit,iris,type="prob")[,2]

Version 4.63 of the caret package is now on CRAN. caret can be used to tune the parameters of predictive models using resampling, estimate variable importance and visualize the results. There are also various modeling and "helper" functions that can be useful for training models. caret has wrappers to over 99 different models for classification and regression. See the package vignettes

Version 4.63 of the caret package is now on CRAN. caret can be used to tune the parameters of predictive models using resampling, estimate variable importance and visualize the results. There are also various modeling and "helper" functions that can be useful for training models. caret has wrappers to over 99 different models for classification and regression. See the package vignettes