similar to: Need help with error

I would really like some or any advice on how I can improve (or fix??) the following analysis. I hope I have provided a completely runnable code - it doesn't produce any errors for me. The resulting plot at the end shows a pretty poor correlation (just speaking visually here) to the test set. How can I improve the performance of the mapping and prediction? Here are some of the data

I want to use caret to build a model with an algorithm that actually has no parameters to find. How do I stop it from repeatedly building the same model 25 times? library(caret) data(mdrr) LOGISTIC_model <- train(mdrrDescr,mdrrClass ,method='glm' ,family=binomial(link="logit") ) LOGISTIC_model 528

Dear all, Since I installed the x64 version of R (v2.12.1), I got a problem with tcltk that I did not achieve to resolve. When loading the library, it gives me the following error message: Loading Tcl/Tk interface ...Error : .onLoad failed in loadNamespace() for 'tcltk', details: call: inDL(x, as.logical(local), as.logical(now), ...) error: unable to load shared object

Working on the caret package has exposed me to the wide variety of approaches that different authors have taken to creating predictive modeling functions (aka machine learning)(aka pattern recognition). I suspect that many package authors are neophyte R users and are stumbling through the process of writing their first R package (or R code). As such, they may not have been exposed to some of the

Everyone, I know that this has been discussed a few times on the list, but I think that there is a high false positive rate of messages from findGlobals during R CMD check (I know the help page has that "The result is an approximation"). Here are two examples of from the caret package: This function get the message "predictors.gbm: no visible binding for global variable

Version 4.06 of the caret package was sent to 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 50 different models for classification and regression. See the package

Version 4.06 of the caret package was sent to 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 50 different models for classification and regression. See the package