| Symbols | |
| .getXlevels | Record Levels of Factors Used in a Model |
| .lm.fit | General fitting for linear (regression) models |
| .preformat.ts | Print a Time Series |
| [.terms | Modify Terms Objects |
| [[.dendrogram | General Tree Structures |
| A | |
| acf | Auto- and Cross- Covariance or Correlation Estimation |
| add.scope | Resolve Scopes for Formulas |
| add1 | Compute Models by Adding One Term |
| add1.default | Compute Models by Adding One Term |
| add1.glm | Add a Single Term to a Linear Model |
| add1.lm | Add a Single Term to a Linear Model |
| addmargins | Puts Arbitrary Margins on Multidimensional Tables or Arrays |
| aggregate | Compute Summary Statistics of Subsets of Data |
| aggregate.data.frame | Compute Column-by-Column Summaries of Groups of Observations |
| aggregate.default | Compute Summary Statistics of Subsets of Data |
| aggregate.formula | Compute Summary Statistics of Subsets of Data |
| aggregate.ts | Compute Summary Statistics of Subsets of Data |
| AIC | Akaike's Information Criterion |
| anova | Anova Tables |
| anova.glm | Analysis of Deviance for Generalized Linear Model Fits |
| anova.glmlist | Analysis of Deviance for Generalized Linear Model Fits |
| anova.lm | Anova Table for Linear Model Objects |
| anova.lmlist | Apply anova to a lmlist Object |
| aov | Fit an Analysis of Variance Model |
| aov.object | Analysis of Variance Objects |
| aovlist.object | Analysis of Variance Objects |
| approx | Interpolation Functions |
| approxfun | Interpolation Functions |
| ar | Fit Autoregressive Models to Time Series |
| ar.yw | Fit Autoregressive Models to Time Series |
| arima | ARIMA Modelling of Time Series |
| arima.sim | Simulate a Univariate ARIMA Series |
| as.data.frame.ftable | Flat Contingency Tables |
| as.dendrogram | General Tree Structures |
| as.dendrogram.dendrogram | General Tree Structures |
| as.dendrogram.hclust | General Tree Structures |
| as.dist | Distance Matrix Calculation |
| as.dist.default | Distance Matrix Calculation |
| as.formula | Define or Extract a Model Formula |
| as.hclust | Converts Objects to Class hclust |
| as.hclust.default | Converts Objects to Class hclust |
| as.hclust.dendrogram | Converts Objects to Class hclust |
| as.hclust.twins | Converts Objects to Class hclust |
| as.matrix.dist | Distance Matrix Calculation |
| as.stepfun | Compute a Step Function |
| as.stepfun.isoreg | Isotonic / Monotone Regression |
| as.table.ftable | Flat Contingency Tables |
| as.terms | Create or Extract a Terms Object |
| as.ts | Time Series Objects |
| asOneSidedFormula | Convert to One-Sided Formula |
| ave | Group Averages Over Level Combinations of Factors |
| B | |
| bartlett.test | Bartlett Test of Homogeneity of Variances |
| bartlett.test.default | Bartlett Test of Homogeneity of Variances |
| bartlett.test.formula | Bartlett Test of Homogeneity of Variances |
| Beta | The Beta Distribution |
| BIC | Akaike's Information Criterion |
| binom.test | Exact Binomial Test |
| Binomial | Generate a Family Object |
| Box.test | Box-Pierce and Ljung-Box Tests |
| C | |
| C | Assign Contrasts to a Factor |
| cancor | Canonical Correlation Analysis |
| Cauchy | The Cauchy Distribution |
| cbind.ts | Union and Intersection of Time Series |
| ccf | Auto- and Cross- Covariance or Correlation Estimation |
| chisq.test | Pearson's Chi-square Test for Count Data |
| Chisquare | The Chi-square Distribution |
| cmdscale | Classical Metric Multi-Dimensional Scaling |
| coef | Extract Information from a Model |
| coef.Arima2 | ARIMA Modelling of Time Series |
| coef.default | Extract Information from a Model |
| coef.listof | Extract Information from a Model |
| coefficients | Extract Information from a Model |
| complete.cases | Find Complete Cases of Observations |
| confint | Confidence Intervals for Model Parameters |
| confint.default | Confidence Intervals for Model Parameters |
| contr.helmert | Contrast or Dummy Variable Matrix |
| contr.poly | Contrast or Dummy Variable Matrix |
| contr.SAS | Contrast or Dummy Variable Matrix |
| contr.sum | Contrast or Dummy Variable Matrix |
| contr.treatment | Contrast or Dummy Variable Matrix |
| contrasts | Contrasts Attribute |
| contrasts<- | Contrasts Attribute |
| cooks.distance | Regression Deletion Diagnostics |
| cooks.distance.glm | Regression Deletion Diagnostics |
| cooks.distance.lm | Regression Deletion Diagnostics |
| cophenetic | Cophenetic Distances for a Hierarchical Clustering |
| cophenetic.default | Cophenetic Distances for a Hierarchical Clustering |
| cophenetic.dendrogram | Cophenetic Distances for a Hierarchical Clustering |
| cor | Correlation, Variance, and Covariance (Matrices) |
| cor.test | Test for Correlation Between Paired Samples |
| cor.test.default | Test for Correlation Between Paired Samples |
| cor.test.formula | Test for Correlation Between Paired Samples |
| cov | Correlation, Variance, and Covariance (Matrices) |
| cov.wt | Weighted Covariance Estimation |
| cov2cor | Correlation, Variance, and Covariance (Matrices) |
| covratio | Regression Deletion Diagnostics |
| cut.dendrogram | General Tree Structures |
| cutree | Create Groups from Hierarchical Clustering |
| cycle | Create Time Vector or Index of Frequency |
| cycle.default | Create Time Vector or Index of Frequency |
| D | |
| dbeta | The Beta Distribution |
| dbinom | The Binomial Distribution |
| dcauchy | The Cauchy Distribution |
| dchisq | The Chi-square Distribution |
| decompose | Classical Seasonal Decomposition by Moving Averages |
| delete.response | Modify Terms Objects |
| dendrapply | Apply a Function to All Nodes of a Dendrogram |
| dendrogram | General Tree Structures |
| density | Kernel Estimate of Probability Density Function |
| density.default | Kernel Estimate of Probability Density Function |
| deriv | Symbolic Partial Derivatives of Expressions |
| deriv.default | Symbolic Partial Derivatives of Expressions |
| deriv.formula | Symbolic Partial Derivatives of Expressions |
| deriv3 | Symbolic Partial Derivatives of Expressions |
| deriv3.default | Symbolic Partial Derivatives of Expressions |
| deriv3.formula | Symbolic Partial Derivatives of Expressions |
| deviance | Deviance of a Fitted Model |
| deviance.default | Deviance of a Fitted Model |
| deviance.glm | Deviance of a Fitted Model |
| deviance.lm | Deviance of a Fitted Model |
| deviance.mlm | Deviance of a Fitted Model |
| deviance.nls | Deviance of a Fitted Model |
| dexp | The Exponential Distribution |
| df | The F Distribution |
| df.residual | Extract Residual Degrees of Freedom from a Model |
| DF2formula | Define or Extract a Model Formula |
| dfbeta | Regression Deletion Diagnostics |
| dfbeta.lm | Regression Deletion Diagnostics |
| dfbetas | Regression Deletion Diagnostics |
| dfbetas.lm | Regression Deletion Diagnostics |
| dffits | Regression Deletion Diagnostics |
| dgamma | The Gamma Distribution |
| dgeom | The Geometric Distribution |
| dhyper | The Hypergeometric Distribution |
| diffinv | Discrete Integration: inverse of diff |
| diffinv.default | Discrete Integration: inverse of diff |
| diffinv.ts | Discrete Integration: inverse of diff |
| dist | Distance Matrix Calculation |
| dlnorm | The Lognormal Distribution |
| dlogis | The Logistic Distribution |
| dmultinom | The Multinomial Distribution |
| dnbinom | The Negative Binomial Distribution |
| dnorm | The Normal Distribution |
| dpois | The Poisson Distribution |
| drop.scope | Resolve Scopes for Formulas |
| drop.terms | Modify Terms Objects |
| drop1 | Investigate models by dropping single terms |
| drop1.default | Investigate models by dropping single terms |
| drop1.glm | Investigate models by dropping single terms |
| drop1.lm | Investigate models by dropping single terms |
| dsignrank | Distribution of the Wilcoxon Signed Rank Statistic |
| dt | The Student's t Distribution |
| dummy.coef | Extract Original Coefficients from a Linear Model |
| dummy.coef.aovlist | Extract Original Coefficients from a Linear Model |
| dummy.coef.lm | Extract Original Coefficients from a Linear Model |
| dunif | The Uniform Distribution |
| dweibull | The Weibull Distribution |
| dwilcox | The Distribution of the Wilcoxon Rank Sum Statistic |
| E | |
| ecdf | Empirical Cumulative Distribution Function |
| eff.aovlist | Compute Efficiency Factors for aovlist Model Terms |
| effects | Single Degree-of-freedom Effects from a Fitted Model |
| effects.glm | Single Degree-of-freedom Effects from a Fitted Model |
| effects.lm | Single Degree-of-freedom Effects from a Fitted Model |
| estVar | SSD Matrix and Estimated Variance Matrix in Multivariate Models |
| estVar.mlm | SSD Matrix and Estimated Variance Matrix in Multivariate Models |
| estVar.SSD | SSD Matrix and Estimated Variance Matrix in Multivariate Models |
| expand.model.frame | Add new variables to a model frame |
| Exponential | The Exponential Distribution |
| extractAIC | Extract AIC from a Fitted Model |
| extractAIC.glm | Extract AIC from a Fitted Model |
| extractAIC.lm | Extract AIC from a Fitted Model |
| F | |
| factanal | Estimate a Factor Analysis Model |
| factor.scope | Resolve Scopes for Formulas |
| family | Generate a Family Object |
| family.object | Family of GLM Models |
| FDist | The F Distribution |
| fft | Fast Fourier Transform |
| filter | Apply a Filter to a Time Series |
| fisher.test | Fisher's Exact Test for Count Data |
| fitted | Extract Information from a Model |
| fitted.default | Extract Information from a Model |
| fitted.isoreg | Isotonic / Monotone Regression |
| fitted.kmeans | K-Means Clustering |
| fitted.values | Extract Information from a Model |
| fivenum | Tukey Five-Number Summaries |
| format.dist | Distance Matrix Calculation |
| format.ftable | Flat Contingency Tables |
| formula | Define or Extract a Model Formula |
| formula.call | Define or Extract a Model Formula |
| formula.character | Define or Extract a Model Formula |
| formula.data.frame | Define or Extract a Model Formula |
| formula.default | Define or Extract a Model Formula |
| formula.formula | Define or Extract a Model Formula |
| formula.lm | Define or Extract a Model Formula |
| formula.nls | Define or Extract a Model Formula |
| formula.terms | Define or Extract a Model Formula |
| friedman.test | Friedman Rank Sum Test |
| friedman.test.default | Friedman Rank Sum Test |
| friedman.test.formula | Friedman Rank Sum Test |
| ftable | Flat Contingency Tables |
| ftable.default | Flat Contingency Tables |
| ftable.formula | Flat Contingency Tables |
| G | |
| Gamma | Generate a Family Object |
| GammaDist | The Gamma Distribution |
| gaussian | Generate a Family Object |
| Geometric | The Geometric Distribution |
| get_all_vars | Construct or Extract a Model Frame |
| getCall | Update a Fitted Model Object |
| getInitial | Get Initial Parameter Estimates for Nonlinear Least Squares Models. |
| getInitial.default | Get Initial Parameter Estimates for Nonlinear Least Squares Models. |
| getInitial.formula | Get Initial Parameter Estimates for Nonlinear Least Squares Models. |
| getInitial.selfStart | Get Initial Parameter Estimates for Nonlinear Least Squares Models. |
| glm | Fit a Generalized Linear Model |
| glm.control | Set Control Parameters for Generalized Linear Model |
| glm.fit | Fit a GLM without Computing the Model Matrix |
| glm.object | Generalized Linear Model Object |
| H | |
| hat | Hat Diagonal Regression Diagnostic |
| hatvalues | Regression Deletion Diagnostics |
| hatvalues.lm | Regression Deletion Diagnostics |
| hclust | Hierarchical Clustering |
| HoltWinters | Holt-Winters Filtering |
| Hypergeometric | The Hypergeometric Distribution |
| I | |
| influence | Regression Diagnostics |
| influence.glm | Regression Diagnostics |
| influence.lm | Regression Diagnostics |
| influence.measures | Regression Deletion Diagnostics |
| integrate | Integral of a Real-valued Function |
| inverse.gaussian | Generate a Family Object |
| is.empty.model | Does a Model Contain any Predictors |
| is.leaf | General Tree Structures |
| is.mts | Time Series Objects |
| is.stepfun | Compute a Step Function |
| is.ts | Time Series Objects |
| isoreg | Isotonic / Monotone Regression |
| K | |
| kmeans | K-Means Clustering |
| knots | Compute a Step Function |
| kruskal.test | Kruskal-Wallis Rank Sum Test |
| kruskal.test.default | Kruskal-Wallis Rank Sum Test |
| kruskal.test.formula | Kruskal-Wallis Rank Sum Test |
| ks.test | Kolmogorov-Smirnov Tests |
| ksmooth | Kernel Regression Smoother |
| L | |
| labels.dendrogram | ID Numbers or Labels of the Leaves in a Dendrogram |
| labels.dist | Distance Matrix Calculation |
| lag | Create a Lagged Time Series |
| line | Robust Line Fitting |
| lm | Fit Linear Regression Model |
| lm.fit | General fitting for linear (regression) models |
| lm.influence | Regression Diagnostics |
| lm.object | Linear Least Squares Model Object |
| lm.wfit | General fitting for linear (regression) models |
| loadings | Extract Loadings from an Object |
| loess | Fit a Local Regression Model |
| loess.control | Computational Options for Loess Fitting |
| loess.object | Loess Model Object |
| loess.smooth | Smooth Loess Curve |
| Logistic | The Logistic Distribution |
| logLik | Extract Log-Likelihood |
| logLik.lm | Extract Log-Likelihood |
| logLik.nls | Extract Log-Likelihood |
| loglin | Contingency Table Analysis |
| Lognormal | The Lognormal Distribution |
| lowess | Scatter Plot Smoothing |
| lsfit | Linear Least-Squares Fit |
| M | |
| mad | Robust Estimates of Scale |
| mahalanobis | Mahalanobis Distance |
| make.link | Create a Link for GLM Families |
| make.tables.aovproj | Support for model.tables Functions. |
| make.tables.aovprojlist | Support for model.tables Functions. |
| makepredictcall | Utility Function for Safe Prediction |
| makepredictcall.default | Utility Function for Safe Prediction |
| makepredictcall.matrix | Utility Function for Safe Prediction |
| makepredictcall.poly | Utility Function for Safe Prediction |
| mantelhaen.test | Mantel-Haenszel Chi-Square Test for Count Data |
| maov.object | Analysis of Variance Objects |
| mcnemar.test | McNemar's Chi-Square Test for Count Data |
| median | Median |
| median.default | Median |
| medpolish | Median Polish of a Matrix |
| mlm.object | Linear Least Squares Model Object |
| model.extract | Extract Special Information from Model Frame |
| model.frame | Construct or Extract a Model Frame |
| model.frame.aovlist | Construct or Extract a Model Frame |
| model.frame.default | Construct or Extract a Model Frame |
| model.frame.lm | Construct or Extract a Model Frame |
| model.matrix | Matrix of Predictors |
| model.matrix.default | Matrix of Predictors |
| model.matrix.object | Matrix of Predictors |
| model.offset | Extract Special Information from Model Frame |
| model.response | Extract Special Information from Model Frame |
| model.tables | Compute Tables of Estimates for Model Object |
| model.tables.aov | Compute Tables of Estimates for Model Object |
| model.tables.aovlist | Compute Tables of Estimates for Model Object |
| model.weights | Extract Special Information from Model Frame |
| Multinomial | The Multinomial Distribution |
| mvfft | Fast Fourier Transform |
| N | |
| na.action | Handle Missing Values in Objects |
| na.action.default | Handle Missing Values in Objects |
| na.contiguous | Find Longest Contiguous Stretch of non-NAs |
| na.contiguous.default | Find Longest Contiguous Stretch of non-NAs |
| na.contiguous.ts | Find Longest Contiguous Stretch of non-NAs |
| na.exclude | Handle Missing Values in Objects |
| na.exclude.data.frame | Handle Missing Values in Objects |
| na.exclude.default | Handle Missing Values in Objects |
| na.fail | Handle Missing Values in Objects |
| na.fail.default | Handle Missing Values in Objects |
| na.omit | Handle Missing Values in Objects |
| na.omit.data.frame | Handle Missing Values in Objects |
| na.omit.default | Handle Missing Values in Objects |
| na.pass | Handle Missing Values in Objects |
| napredict | Adjust for Missing Values |
| napredict.default | Adjust for Missing Values |
| napredict.exclude | Adjust for Missing Values |
| napredict.NULL | Adjust for Missing Values |
| naprint | Print Missing Value Information |
| naprint.default | Print Missing Value Information |
| naprint.exclude | Print Missing Value Information |
| naprint.omit | Print Missing Value Information |
| naresid | Adjust for Missing Values |
| naresid.default | Adjust for Missing Values |
| naresid.exclude | Adjust for Missing Values |
| naresid.NULL | Adjust for Missing Values |
| NegBinomial | The Negative Binomial Distribution |
| nextn | Highly Composite Numbers |
| nlm | Non-Linear Minimization |
| nlminb | Nonlinear Minimization subject to Box Constraints |
| nls | Nonlinear Least Squares Regression |
| nls.control | Control the Iteration in nls() |
| NLSstAsymptotic | Fit the Asymptotic Regression Model |
| NLSstAsymptotic.sortedXyData | Fit the Asymptotic Regression Model |
| NLSstClosestX | Inverse Interpolation |
| NLSstClosestX.sortedXyData | Inverse Interpolation |
| NLSstLfAsymptote | Horizontal Asymptote on the Left Side |
| NLSstLfAsymptote.sortedXyData | Horizontal Asymptote on the Left Side |
| NLSstRtAsymptote | Horizontal Asymptote on the Right Side |
| NLSstRtAsymptote.sortedXyData | Horizontal Asymptote on the Right Side |
| nobs | Extract the Number of Observations from a Fit |
| nobs.default | Extract the Number of Observations from a Fit |
| nobs.glm | Extract the Number of Observations from a Fit |
| nobs.lm | Extract the Number of Observations from a Fit |
| nobs.nls | Extract the Number of Observations from a Fit |
| Normal | The Normal Distribution |
| numericDeriv | Evaluate derivatives numerically |
| O | |
| offset | Set an Offset Value in a Modelling Formula |
| oneway.test | Test for Equal Means in a One-Way Layout |
| optim | General-purpose Optimization |
| optimHess | Numerically Estimate Hessian Matrix |
| optimise | Univariate Optimization of a Function |
| optimize | Univariate Optimization of a Function |
| order.dendrogram | ID Numbers or Labels of the Leaves in a Dendrogram |
| P | |
| p.adjust | Adjust P-values for Multiple Comparisons |
| p.adjust.methods | Adjust P-values for Multiple Comparisons |
| pacf | Auto- and Cross- Covariance or Correlation Estimation |
| pbeta | The Beta Distribution |
| pbinom | The Binomial Distribution |
| pcauchy | The Cauchy Distribution |
| pchisq | The Chi-square Distribution |
| pexp | The Exponential Distribution |
| pf | The F Distribution |
| pgamma | The Gamma Distribution |
| pgeom | The Geometric Distribution |
| phyper | The Hypergeometric Distribution |
| plnorm | The Lognormal Distribution |
| plogis | The Logistic Distribution |
| plot.dendrogram | General Tree Structures |
| plot.ecdf | Empirical Cumulative Distribution Function |
| pnbinom | The Negative Binomial Distribution |
| pnorm | The Normal Distribution |
| Poisson | Generate a Family Object |
| poly | Compute Orthogonal Polynomials |
| polym | Compute Orthogonal Polynomials |
| power | Generate a Power Link Object |
| ppoints | Plotting Points for Quantile-Quantile Plots |
| ppois | The Poisson Distribution |
| prcomp | UNKNOWN |
| prcomp.default | UNKNOWN |
| prcomp.formula | UNKNOWN |
| predict | Make Predictions from a Fitted Model Object |
| predict.ar | Fit Autoregressive Models to Time Series |
| predict.glm | Predict Method for a Generalized Linear Model |
| predict.HoltWinters | Holt-Winters Filtering |
| predict.lm | Predict Method for a Linear Model |
| predict.loess | Evaluation of Local Regression Surfaces |
| predict.mlm | Predict Method for a Linear Model |
| predict.nls | Predicting from Nonlinear Least Squares Fits |
| predict.poly | Compute Orthogonal Polynomials |
| predict.prcomp | UNKNOWN |
| predict.princomp | Principal Component Scores |
| predict.smooth.spline | Smoothing Spline at New Data |
| princomp | Principal Components Analysis |
| princomp.default | Principal Components Analysis |
| princomp.formula | Principal Components Analysis |
| print.anova | Print an anova Object |
| print.aov | Fit an Analysis of Variance Model |
| print.aovlist | Fit an Analysis of Variance Model |
| print.ar | Fit Autoregressive Models to Time Series |
| print.Arima2 | ARIMA Modelling of Time Series |
| print.dendrogram | General Tree Structures |
| print.dist | Distance Matrix Calculation |
| print.dummy_coef | Extract Original Coefficients from a Linear Model |
| print.dummy_coef_list | Extract Original Coefficients from a Linear Model |
| print.ecdf | Empirical Cumulative Distribution Function |
| print.factanal | Estimate a Factor Analysis Model |
| print.family | Use print() on a family Object |
| print.formula | Use print() on a formula Object |
| print.ftable | Flat Contingency Tables |
| print.hclust | Hierarchical Clustering |
| print.HoltWinters | Holt-Winters Filtering |
| print.infl | Regression Deletion Diagnostics |
| print.isoreg | Isotonic / Monotone Regression |
| print.kmeans | K-Means Clustering |
| print.lm | Use print() on an lm Object |
| print.loadings | Print a Loadings Matrix |
| print.logLik | Extract Log-Likelihood |
| print.medpolish | Median Polish of a Matrix |
| print.mtable | Compute Tables of Estimates for Model Object |
| print.prcomp | UNKNOWN |
| print.princomp | Print a Principal Components Object |
| print.stepfun | Compute a Step Function |
| print.summary.aov | Summary of an Analysis of Variance Object |
| print.summary.aovlist | Summary of an Analysis of Variance Object |
| print.summary.lm | Summary Method for Linear Models |
| print.summary.mlm | Summary Method for Linear Models |
| print.summary.prcomp | UNKNOWN |
| print.summary.princomp | Print a Principal Component Summary |
| print.tables_aov | Compute Tables of Estimates for Model Object |
| print.ts | Print a Time Series |
| print.tukeyline | Robust Line Fitting |
| printCoefmat | Print Coefficient Matrices |
| proj | Projection Matrix |
| proj.aov | Projection Matrix |
| proj.aovlist | Projection Matrix |
| proj.default | Projection Matrix |
| proj.lm | Projection Matrix |
| prop.test | Proportions Tests |
| psignrank | Distribution of the Wilcoxon Signed Rank Statistic |
| pt | The Student's t Distribution |
| ptukey | The Studentized Range Distribution |
| punif | The Uniform Distribution |
| pweibull | The Weibull Distribution |
| pwilcox | The Distribution of the Wilcoxon Rank Sum Statistic |
| Q | |
| qbeta | The Beta Distribution |
| qbinom | The Binomial Distribution |
| qcauchy | The Cauchy Distribution |
| qchisq | The Chi-square Distribution |
| qexp | The Exponential Distribution |
| qf | The F Distribution |
| qgamma | The Gamma Distribution |
| qgeom | The Geometric Distribution |
| qhyper | The Hypergeometric Distribution |
| qlnorm | The Lognormal Distribution |
| qlogis | The Logistic Distribution |
| qnbinom | The Negative Binomial Distribution |
| qnorm | The Normal Distribution |
| qpois | The Poisson Distribution |
| qqnorm | Normal Quantile-Quantile Plots |
| qqnorm.default | Normal Quantile-Quantile Plots |
| qsignrank | Distribution of the Wilcoxon Signed Rank Statistic |
| qt | The Student's t Distribution |
| qtukey | The Studentized Range Distribution |
| quantile | Empirical Quantiles |
| quantile.ecdf | Empirical Cumulative Distribution Function |
| quasi | Generate a Family Object |
| quasibinomial | Generate a Family Object |
| quasipoisson | Generate a Family Object |
| qunif | The Uniform Distribution |
| qweibull | The Weibull Distribution |
| qwilcox | The Distribution of the Wilcoxon Rank Sum Statistic |
| R | |
| r2dtable | Random Two-way Tables with Given Marginals |
| rbeta | The Beta Distribution |
| rbinom | The Binomial Distribution |
| rcauchy | The Cauchy Distribution |
| rchisq | The Chi-square Distribution |
| reformulate | Modify Terms Objects |
| relevel | Reorder Levels of Factor |
| relevel.default | Reorder Levels of Factor |
| relevel.factor | Reorder Levels of Factor |
| relevel.ordered | Reorder Levels of Factor |
| reorder | Reorder Levels of a Factor |
| reorder.default | Reorder Levels of a Factor |
| reorder.dendrogram | Reorder a Dendrogram |
| replications | Number of Replications of Terms |
| reshape | Reshape Grouped Data |
| resid | Extract Information from a Model |
| residuals | Extract Information from a Model |
| residuals.default | Extract Information from a Model |
| residuals.glm | Compute Residuals for glm Objects |
| residuals.HoltWinters | Holt-Winters Filtering |
| residuals.isoreg | Isotonic / Monotone Regression |
| rexp | The Exponential Distribution |
| rf | The F Distribution |
| rgamma | The Gamma Distribution |
| rgeom | The Geometric Distribution |
| rhyper | The Hypergeometric Distribution |
| rlnorm | The Lognormal Distribution |
| rlogis | The Logistic Distribution |
| rmultinom | The Multinomial Distribution |
| rnbinom | The Negative Binomial Distribution |
| rnorm | The Normal Distribution |
| rpois | The Poisson Distribution |
| rsignrank | Distribution of the Wilcoxon Signed Rank Statistic |
| rstandard | Regression Deletion Diagnostics |
| rstandard.glm | Regression Deletion Diagnostics |
| rstandard.lm | Regression Deletion Diagnostics |
| rstudent | Regression Deletion Diagnostics |
| rstudent.glm | Regression Deletion Diagnostics |
| rstudent.lm | Regression Deletion Diagnostics |
| rt | The Student's t Distribution |
| runif | The Uniform Distribution |
| rweibull | The Weibull Distribution |
| rwilcox | The Distribution of the Wilcoxon Rank Sum Statistic |
| S | |
| sd | Compute Standard Deviation |
| se.aov | Standard Error of AOV Objects |
| se.aovlist | Standard Error of AOV Objects |
| se.contrast.aov | Standard Errors for Contrasts between Means |
| se.contrast.aovlist | Standard Errors for Contrasts between Means |
| selfStart | Construct Self-starting Nonlinear Models |
| selfStart.default | Construct Self-starting Nonlinear Models |
| selfStart.formula | Construct Self-starting Nonlinear Models |
| setNames | Attach a names attribute to an object |
| shapiro.test | Shapiro-Wilk Test for Normality |
| sigma | Extract Residual Standard Deviation |
| sigma.default | Extract Residual Standard Deviation |
| SignRank | Distribution of the Wilcoxon Signed Rank Statistic |
| smooth | Nonlinear Smoothing Using Running Medians |
| smooth.spline | Fit a Smoothing Spline |
| spline | Interpolating Splines |
| splinefun | Interpolating Splines |
| splinefunH | Interpolating Splines |
| SSasymp | Asymptotic Regression Model |
| SSasympOff | Asymptotic Regression Model |
| SSasympOrig | Asymptotic Regression Model |
| SSbiexp | Biexponential Model: The Sum of Two Exponentials |
| SSD | SSD Matrix and Estimated Variance Matrix in Multivariate Models |
| SSD.mlm | SSD Matrix and Estimated Variance Matrix in Multivariate Models |
| SSfol | First-order Compartment Model |
| SSfpl | Four-parameter Logistic Model |
| SSgompertz | Self-Starting Nls Gompertz Growth Model |
| SSlogis | Fitting a Logistic Curve |
| SSmicmen | Michaelis-Menten Model |
| SSweibull | Self-Starting Nls Weibull Growth Curve Model |
| stat.anova | Add Statistics Columns to an Anova Table |
| stepfun | Compute a Step Function |
| str.dendrogram | General Tree Structures |
| str.logLik | Extract Log-Likelihood |
| summary.aov | Summary of an Analysis of Variance Object |
| summary.aovlist | Summary of an Analysis of Variance Object |
| summary.ecdf | Empirical Cumulative Distribution Function |
| summary.glm | Summary Method for Fitted Generalized Linear Models |
| summary.infl | Regression Deletion Diagnostics |
| summary.lm | Summary Method for Linear Models |
| summary.mlm | Summary Method for Linear Models |
| summary.nls | Summary of an nls Model Object |
| summary.prcomp | UNKNOWN |
| summary.princomp | Summary of a Principal Components Object |
| summary.stepfun | Compute a Step Function |
| supsmu | Scatter Plot Smoothing Using Super Smoother |
| symnum | Symbolic Number Coding |
| T | |
| t.test | Student's t-test |
| t.test.default | Student's t-test |
| t.test.formula | Student's t-test |
| TDist | The Student's t Distribution |
| terms | Create or Extract a Terms Object |
| terms.aovlist | Create or Extract a Terms Object |
| terms.default | Create or Extract a Terms Object |
| terms.formula | Create or Extract a Terms Object |
| terms.object | Class of Objects for Terms in a Model |
| terms.terms | Create or Extract a Terms Object |
| time | Create Time Vector or Index of Frequency |
| time.default | Create Time Vector or Index of Frequency |
| toeplitz | Form Symmetric Toeplitz Matrix |
| ts | Time Series Objects |
| ts.intersect | Union and Intersection of Time Series |
| ts.union | Union and Intersection of Time Series |
| Tukey | The Studentized Range Distribution |
| U | |
| Uniform | The Uniform Distribution |
| uniroot | Find a Root of a Univariate Function |
| update | Update a Fitted Model Object |
| update.default | Update a Fitted Model Object |
| update.formula | Update a Fitted Model Object |
| V | |
| var | Correlation, Variance, and Covariance (Matrices) |
| var.test | F Test to Compare Two Variances |
| var.test.default | F Test to Compare Two Variances |
| var.test.formula | F Test to Compare Two Variances |
| vcov | Variance-Covariance Matrix of the Estimated Coefficients |
| vcov.Arima2 | ARIMA Modelling of Time Series |
| vcov.glm | Variance-Covariance Matrix of the Estimated Coefficients |
| vcov.lm | Variance-Covariance Matrix of the Estimated Coefficients |
| vcov.mlm | Variance-Covariance Matrix of the Estimated Coefficients |
| vcov.nls | Variance-Covariance Matrix of the Estimated Coefficients |
| vcov.summary.glm | Variance-Covariance Matrix of the Estimated Coefficients |
| vcov.summary.lm | Variance-Covariance Matrix of the Estimated Coefficients |
| W | |
| Weibull | The Weibull Distribution |
| weighted.mean | Compute Weighted Mean |
| weighted.mean.Date | Compute Weighted Mean |
| weighted.mean.default | Compute Weighted Mean |
| weighted.mean.difftime | Compute Weighted Mean |
| weighted.mean.POSIXct | Compute Weighted Mean |
| weighted.mean.POSIXlt | Compute Weighted Mean |
| weights | Extract Model Weights |
| weights.default | Extract Model Weights |
| weights.glm | Extract Model Weights |
| wilcox.test | Wilcoxon Rank Sum and Signed Rank Tests |
| wilcox.test.default | Wilcoxon Rank Sum and Signed Rank Tests |
| wilcox.test.formula | Wilcoxon Rank Sum and Signed Rank Tests |
| Wilcoxon | The Distribution of the Wilcoxon Rank Sum Statistic |
| window | Window a Time Series |
| window.default | Window a Time Series |
| window.ts | Window a Time Series |
| window<- | Window a Time Series |
| window<-.ts | Window a Time Series |
| X | |
| xtabs | Cross Tabulation |