search for: splsda

Hi R users, I am getting the following error when using the splsda function in R v2.12.1: "Error in switch(classifier, logistic = { : EXPR must be a length 1 vector" What does this mean and how do I fix this? Thank you in advance! Best, Savi

...rder) relImp <- summary(x, plotit = FALSE) varUsed <- as.character(subset(relImp, rel.inf != 0)$var) basicVars(varList, varUsed) } So it didn't take the context into account that subset isn't (necessarily) looking in the global environment. Also, the package has a file splsda.R that has the message "splsda.default: no visible binding for global variable 'ncomp'". The oddness of this one is that there is no variable named ncomp explicitly mentioned in the file (confirmed by grep and I checked that the function wasn't accidentally defined twice). In...

...). (6) Don't require a test set when model building. (7) Control all written output during model-building time with a verbose option. Resampling can make a mess out of things if output/logging is always exposed. (8) Please use RSiteSearch to avoid name collisions between packages (eg. gam(), splsda(), roc(), LogitBoost()). Also search Bioconductor. (9) Allow the predict function to generate results from many different sub-models simultaneously. For example, pls() can return predictions across many values of ncomp. enet(), cubist(), blackboost() are other examples. corollary (9a): [OCD] ensu...

...are fit in train(). Now, the function used to compute the models is passed in as a parameter (defaulting to lapply). In this way, users can use their own parallel processing software without new versions of caret. Examples using MPI and NWS are given in ?train. The package now contains a function (splsda) that extends the spls function to classification (in the same manner than caret's plsda function extends plsr). Also, fixed a bug where the MSE (instead of RMSE) was reported for random forest OOB resampling There are more examples in ?train. Changes to confusionMatrix, sensitivity, specifi...

...are fit in train(). Now, the function used to compute the models is passed in as a parameter (defaulting to lapply). In this way, users can use their own parallel processing software without new versions of caret. Examples using MPI and NWS are given in ?train. The package now contains a function (splsda) that extends the spls function to classification (in the same manner than caret's plsda function extends plsr). Also, fixed a bug where the MSE (instead of RMSE) was reported for random forest OOB resampling There are more examples in ?train. Changes to confusionMatrix, sensitivity, specifi...