Package index • car

All functions
`Anova()` `Manova()` `print(<Anova.mlm>)` `summary(<Anova.mlm>)` `print(<summary.Anova.mlm>)` `print(<univaov>)` `as.data.frame(<univaov>)`	Anova Tables for Various Statistical Models
`Boot()`	Bootstrapping for regression models
`Boxplot()`	Boxplots With Point Identification
`contr.Treatment()` `contr.Sum()` `contr.Helmert()`	Functions to Construct Contrasts
`ellipse()` `dataEllipse()` `confidenceEllipse()` `confidenceEllipses()`	Ellipses, Data Ellipses, and Confidence Ellipses
`Export()`	Export a data frame to disk in one of many formats
`Import()`	Import data from many file formats
`Predict()`	Model Predictions
`S()` `print(<S.lm>)` `print(<S.glm>)` `print(<S.multinom>)` `print(<S.polr>)` `print(<S.lme>)` `print(<S.lmerMod>)` `print(<S.glmerMod>)` `Confint()`	Modified Functions for Summarizing Linear, Generalized Linear, and Some Other Models
`gamLine()` `loessLine()` `quantregLine()`	Smoothers to Draw Lines on Scatterplots
`Tapply()`	Apply a Function to a Variable Within Factor Levels
`basicPowerAxis()` `bcPowerAxis()` `bcnPowerAxis()` `yjPowerAxis()` `probabilityAxis()`	Axes for Transformed Variables
`avPlots()` `avp()` `avPlot()` `avPlot3d()`	Added-Variable Plots
`bcPower()` `bcnPower()` `bcnPowerInverse()` `yjPower()` `basicPower()`	Box-Cox, Box-Cox with Negatives Allowed, Yeo-Johnson and Basic Power Transformations
`boxCox()` `boxCox2d()`	Graph the profile log-likelihood for Box-Cox transformations in 1D, or in 2D with the bcnPower family.
`boxCoxVariable()`	Constructed Variable for Box-Cox Transformation
`boxTidwell()` `print(<boxTidwell>)`	Box-Tidwell Transformations
`brief` `brief.data.frame` `brief.tbl` `brief.matrix` `brief.numeric` `brief.integer` `brief.character` `brief.factor` `brief.function` `brief.list` `brief.lm` `brief.glm` `brief.multinom` `brief.polr` `brief.default`	Print Abbreviated Ouput
`av.plot()` `av.plots()` `box.cox()` `bc()` `box.cox.powers()` `box.cox.var()` `box.tidwell()` `cookd()` `confidence.ellipse()` `ceres.plot()` `ceres.plots()` `cr.plot()` `cr.plots()` `data.ellipse()` `durbin.watson()` `levene.test()` `leverage.plot()` `leverage.plots()` `linear.hypothesis()` `ncv.test()` `outlier.test()` `qq.plot()` `skewPower()` `spread.level.plot()`	Defunct Functions in the car Package
`bootCase()` `nextBoot()`	Deprecated Functions in the car Package
`car-internal.Rd` `.carEnv`	Internal Objects for the car package
`carHexsticker()`	View the Official Hex Sticker for the car Package
`carPalette()`	Set or Retrieve car Package Color Palette
`carWeb()`	Access to the R Companion to Applied Regression Website
`ceresPlots()` `ceresPlot()`	Ceres Plots
`compareCoefs()`	Print estimated coefficients and their standard errors in a table for several regression models.
`crPlots()` `crp()` `crPlot()` `crPlot3d()`	Component+Residual (Partial Residual) Plots
`deltaMethod()`	Estimate and Standard Error of a Nonlinear Function of Estimated Regression Coefficients
`densityPlot()` `adaptiveKernel()` `depan()` `dbiwt()`	Nonparametric Density Estimates
`dfbetaPlots()` `dfbetasPlots()`	dfbeta and dfbetas Index Plots
`durbinWatsonTest()` `dwt()` `print(<durbinWatsonTest>)`	Durbin-Watson Test for Autocorrelated Errors
`hccm()`	Heteroscedasticity-Corrected Covariance Matrices
`hist(<boot>)` `summary(<boot>)` `confint(<boot>)` `Confint(<boot>)` `vcov(<boot>)`	Methods Functions to Support `boot` Objects
`infIndexPlot()` `influenceIndexPlot()`	Influence Index Plot
`influence(<lme>)` `cooks.distance(<influence.lme>)` `dfbeta(<influence.lme>)` `dfbetas(<influence.lme>)`	Influence Diagnostics for Mixed-Effects Models
`influencePlot()`	Regression Influence Plot
`inverseResponsePlot()` `invResPlot()`	Inverse Response Plots to Transform the Response
`invTranPlot()` `invTranEstimate()`	Choose a Predictor Transformation Visually or Numerically
`leveneTest()`	Levene's Test
`leveragePlots()` `leveragePlot()`	Regression Leverage Plots
`linearHypothesis()` `lht()` `print(<linearHypothesis.mlm>)` `matchCoefs()`	Test Linear Hypothesis
`logit()`	Logit Transformation
`marginalModelPlots()` `mmps()` `marginalModelPlot()` `mmp()`	Marginal Model Plotting
`mcPlots()` `mcPlot()`	Draw Linear Model Marginal and Conditional Plots in Parallel or Overlaid
`ncvTest()`	Score Test for Non-Constant Error Variance
`outlierTest()` `print(<outlierTest>)`	Bonferroni Outlier Test
`panel.car()`	Panel Function for Coplots
`poTest()` `print(<poTest>)`	Test for Proportional Odds in the Proportional-Odds Logistic-Regression Model
`pointLabel()`	Label placement for points to avoid overlaps
`powerTransform()`	Finding Univariate or Multivariate Power Transformations
`qqPlot()` `qqp()`	Quantile-Comparison Plot
`recode()` `Recode()`	Recode a Variable
`regLine()`	Plot Regression Line
`residualPlots()` `residualPlot()`	Residual Plots for Linear and Generalized Linear Models
`scatter3d()` `Identify3d()`	Three-Dimensional Scatterplots and Point Identification
`scatterplot()` `sp()`	Enhanced Scatterplots with Marginal Boxplots, Point Marking, Smoothers, and More
`scatterplotMatrix()` `spm()`	Scatterplot Matrices
`showLabels()`	Functions to Identify and Mark Extreme Points in a 2D Plot.
`sigmaHat()`	Return the scale estimate for a regression model
`some()`	Sample a Few Elements of an Object
`spreadLevelPlot()` `slp()` `print(<spreadLevelPlot>)`	Spread-Level Plots
`strings2factors()`	Convert Character-String Variables in a Data Frame to Factors
`subsets()`	Plot Output from regsubsets Function in leaps package
`symbox()`	Boxplots for transformations to symmetry
`testTransform()`	Likelihood-Ratio Tests for Univariate or Multivariate Power Transformations to Normality
`vif()`	Variance Inflation Factors
`wcrossprod()`	Weighted Matrix Crossproduct
`whichNames()` `which.names()`	Position of Row Names

Reference

All functions