Cran.r-project.org rpart
3.2.1 Generalized Gini index The Gini index has the following interesting interpretation.
Cran.r project.org rpart software#
R-release (arm64): prediction_0.3.14.tgz, r-release (x86_64): prediction_0.3.14.tgz, r-oldrel: prediction_0.3.14. The rpart software implements only the altered priors method. Version:ĪER, aod, betareg, biglm, brglm, caret, crch, e1071, earth, ff, ffbase, gam (≥ 1.15), gee, glmnet, glmx, kernlab, lme4, MASS, mclogit, mda, mlogit, MNP, nlme, nnet, ordinal, plm, pscl, quantreg, rpart, sampleSelection, speedglm, survey (≥ See the README or main package documentation page for a complete listing. The package currently supports common model types (e.g., "lm", "glm") from the 'stats' package, as well as numerous other model classes from other add-on packages. Marginal effect estimation is provided by the related package, 'margins'.
Cran.r project.org rpart archive#
It gets posted to the comprehensive R archive (CRAN) as needed after undergoing a thorough testing.
Cran.r project.org rpart code#
The 'summary()' method provides a data frame with average predictions, possibly over counterfactual versions of the data (a la the 'margins' command in 'Stata'). This is the source code for the rpart package, which is a recommended package in R.
Here's some of my data so you can see What I'm working with: > head(data) I've tried the debug and traceback but I'm not understanding why this error is occurring (and like I said, it's not reproducible with iris data). ot Plot 'rpart' Models: An Enhanced Version of 'plot.rpart'. Which I think comes from this line: error.rate = sum(test$Class != predict(tree, test, type="c")) / nrow(test) :exclamation: This is a read-only mirror of the CRAN R package repository. The primary functions include tableby(), a Table-1-like summary of multiple variable types 'by' the levels of one or more categorical variables paired(), a. I'm getting the error: Error in predict.rpart(tree, test, type = "c") : An Arsenal of 'R' functions for large-scale statistical summaries, which are streamlined to work within the latest reporting tools in 'R' and 'RStudio' and which use formulas and versatile summary statistics for summary tables and models.
My data has 37 predictor variables (both numerical and categorical) with the 38th column the Class prediction. It works just fine on the iris dataset, but does not work on my ow ndata. I am using the exact code for best first search from page 4 of this CRAN document ( ), which uses the iris dataset.