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Dear Weavers, Does someone have an example of using eps or any other vector graphics with odfWeave? It tried the example below (and commented variants) with simple.odt in the examples directory, and got an error. Dieter #--- library(odfWeave) plotInfo <- getImageDefs() plotInfo$type = "eps" #plotInfo$device = "postscript" setImageDefs(plotInfo)

Hello I tried the odfWeave package today, by running the formatting.odt and example.odt files that are included with the package. They both ran fine, but when I try to open them in my OpenOffice (OpenOffice 3.1.1 on Kubuntu 9.10) I get an error "Format error discovered in the file in sub-document content.xml at 1293,124(row,col)." I also tried to open them in MS Word 2003 (Windows

Hello, When evaluating different learning methods for a categorization problem with the (really useful!) caret package, I'm getting confusing results from the Kappa computation. The data is about 20,000 rows and a few dozen columns, and the categories are quite asymmetrical, 4.1% in one category and 95.9% in the other. When I train a ctree model as: model <- train(dat.dts,

Position: Statistician in a systems biology team focused on metabolic disorders such as diabetes and obesity. The position is part of a statistics group supporting Pfizer global research and development. Role and Responsibilities: The statistician will be an integral member of a systems biology team which develops and/or uses computational and statistical approaches to manage and derive

Version 4.39 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 75 different models for classification and regression. See the package

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 4.39 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 75 different models for classification and regression. See the package

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

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