Package index • rockchalk

All functions

addLines(): Superimpose regression lines on a plotted plane

centerNumerics(): Find numeric columns, center them, re-name them, and join them with the original data.

centralValues(): Central Tendency estimates for variables

cheating: Cheating and Looting in Japanese Electoral Politics

checkIntFormat(): A way of checking if a string is a valid file name.

checkPosDef(): Check a matrix for positive definitness

combineLevels(): recode a factor by "combining" levels

cutByQuantile(): Calculates the "center" quantiles, always including the median, when n is odd.

cutBySD(): Returns center values of x, the mean, mean-std.dev, mean+std.dev

cutByTable(): Select most frequently occurring values from numeric or categorical variables.

cutFancy(): Create an ordinal variable by grouping numeric data input.

descriptiveTable(): Summary stats table-maker for regression users

dir.create.unique(): Create a uniquely named directory. Appends number & optionally date to directory name.

drawnorm(): draw a normal distribution with beautiful illustrations

focalVals(): Create a focal value vector.

formatSummarizedFactors(): Prints out the contents of an object created by summarizeFactors in the style of base::summary

formatSummarizedNumerics(): Reformat numeric summarize output as one column per variable, similar to R summary

genCorrelatedData(): Generates a data frame for regression analysis

genCorrelatedData2(): Generates a data frame for regression analysis.

genCorrelatedData3(): Generate correlated data for simulations (third edition)

genX(): Generate correlated data (predictors) for one unit

getAuxRsq(): retrieves estimates of the coefficient of determination from a list of regressions

getDeltaRsquare(): Calculates the delta R-squares, also known as squared semi-partial correlation coefficients.

getFocal(): Select focal values from an observed variable.

getPartialCor(): Calculates partial correlation coefficients after retrieving data matrix froma fitted regression model

getVIF(): Converts the R-square to the variance inflation factor

gmc(): Group Mean Center: Generate group summaries and individual deviations within groups

kurtosis(): Calculate excess kurtosis

lazyCor(): Create correlation matrices.

lazyCov(): Create covariance matrix from correlation and standard deviation information

lmAuxiliary(): Estimate leave-one-variable-out regressions

magRange(): magRange Magnify the range of a variable.

makeSymmetric(): Create Symmetric Matrices, possibly covariance or correlation matrices, or check a matrix for symmetry and serviceability.

makeVec(): makeVec for checking or creating vectors

mcDiagnose(): Multi-collinearity diagnostics

mcGraph1() mcGraph2() mcGraph3(): Illustrate multicollinearity in regression, part 1.

meanCenter(): meanCenter

model.data(): Create a "raw" (UNTRANSFORMED) data frame equivalent to the input data that would be required to fit the given model.

model.data(<default>): Create a data frame suitable for estimating a model

mvrnorm(): Minor revision of mvrnorm (from MASS) to facilitate replication

newdata(): Create a newdata frame for usage in predict methods

outreg(): Creates a publication quality result table for regression models. Works with models fitted with lm, glm, as well as lme4.

outreg2HTML(): Convert LaTeX output from outreg to HTML markup

padW0(): Pad with 0's.

pctable(): Creates a cross tabulation with counts and percentages

perspEmpty(): perspEmpty

plot(<testSlopes>): Plot testSlopes objects

plotCurves(): Assists creation of predicted value curves for regression models.

plotFancy(): Regression plots with predicted value lines, confidence intervals, color coded interactions

plotFancyCategories(): Draw display for discrete predictor in plotSlopes

plotPlane(): Draw a 3-D regression plot for two predictors from any linear or nonlinear lm or glm object

plotSeq(): Create sequences for plotting

plotSlopes(): Generic function for plotting regressions and interaction effects

predictCI(): Calculate a predicted value matrix (fit, lwr, upr) for a regression, either lm or glm, on either link or response scale.

predictOMatic(): Create predicted values after choosing values of predictors. Can demonstrate marginal effects of the predictor variables.

print(<pctable>): Display pctable objects

print(<summarize>): print method for output from summarize

print(<summary.pctable>): print method for summary.pctable objects

rbindFill(): Stack together data frames

religioncrime: Religious beliefs and crime rates

removeNULL(): Remove NULL values variables from a list

residualCenter() predict(<rcreg>): Calculates a "residual-centered" interaction regression.

rockchalk-package rockchalk: rockchalk: regression functions

se.bars(): Draw standard error bar for discrete variables

skewness(): Calculate skewness

standardize(): Estimate standardized regression coefficients for all variables

summarize(): Sorts numeric from discrete variables and returns separate summaries for those types of variables.

summarizeFactors(): Extracts non-numeric variables, calculates summary information, including entropy as a diversity indicator.

summarizeNumerics(): Extracts numeric variables and presents an summary in a workable format.

summary(<factor>): Tabulates observed values and calculates entropy

summary(<pctable>): Extract presentation from a pctable object

testSlopes(): Hypothesis tests for Simple Slopes Objects

vech2Corr(): Convert the vech (column of strictly lower trianglar values from a matrix) into a correlation matrix.

vech2mat(): Convert a half-vector (vech) into a matrix.

waldt(): T-test for the difference in 2 regression parameters