library(RcmdrPlugin.NMBU) or library(umb).
Statistical and summary functions included:
dummy(y)PRESS(object=NULL) (default is current model)PRESS.res(object=NULL) (default is current model)R2_pred(object=NULL) (default is current model)forward(model, alpha=0.2, full=FALSE)backward(model, alpha=0.2, full=FALSE)stepWise(model, alpha.enter=0.15, alpha.remove=0.15, full=FALSE)stepWiseBack(model, alpha.remove=0.15, alpha.enter=0.15, full=FALSE)best.subsets(model, nbest=5, nvmax)confint.mvr(object, parm, level=0.95, ...)confusion(true, predicted)DA.scores(object=NULL) (default is current model)plotDA(DAobject=NULL, regions=TRUE, contours=FALSE, resolution=100)hclust.merge(object) (default is last clustering)mixed.model(formula, random.effects=NULL, data, restrictedModel=FALSE, subset="")summary.extra(object)anova_reg(lm.object)predict_CI_PI(model, data, level)prop.test.ordinary(x, n, p = NULL, alternative = c("two.sided", "less",
"greater"), conf.level = 0.95, correct = TRUE)rmsep(object) (default is current model)aovP()clustP()daP()mixP()pcaP()plsP()variablesP()DA.coef()hclust.list()listHclustSolutions(envir=.GlobalEnv, ...)make.colours(object)confint.mvr(object, parm, level=0.95, ...)dummy(y)dummify(y,n,name)Dummify(data, main.effects, response)fparse(f)if.R()anova_reg_GUI()backwardDrop()backwardForward()bestSubsets()coefNMBU()contrastGUI()contrastGUI2()covarianceMatrix()createSequence()discriminantAnalysis()discriminantPlot()dotplotGUI()enterTableNMBU()fittedLinePlot()forwardAdd()forwardBackward()hierarchicalClusterVariable()linearModelANOVA()meanCenter()mixtureGUI()plsRegressionModel()postHocGUI()predictRegressionModel()PRESS.GUI()principalComponentPlots()principalComponentsStat()proportionTest()sortData()twoSamplesTTest()twoWayTableNMBU()CIplot()dotPlot()dots()mixture.contour()panel.ci.plot()plotDA()library(RcmdrPlugin.NMBU) # Starts up the R Commander including this plugin.
library(umb) # Simpler startup with automatic update (when available)Run the code above in your browser using DataLab