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rockchalk (version 1.8.111)

Regression Estimation and Presentation

Description

A collection of functions for interpretation and presentation of regression analysis. These functions are used to produce the statistics lectures in . Includes regression diagnostics, regression tables, and plots of interactions and "moderator" variables. The emphasis is on "mean-centered" and "residual-centered" predictors. The vignette 'rockchalk' offers a fairly comprehensive overview. The vignette 'Rstyle' has advice about coding in R. The package title 'rockchalk' refers to our school motto, 'Rock Chalk Jayhawk, Go K.U.'.

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Install

install.packages('rockchalk')

Monthly Downloads

14,388

Version

1.8.111

License

GPL (>= 3.0)

Maintainer

Paul Johnson

Last Published

May 14th, 2018

Functions in rockchalk (1.8.111)

Minor revision of mvrnorm (from MASS) to facilitate replication

Calculates partial correlation coefficients after retrieving data matrix froma fitted regression model

genCorrelatedData2

Generates a data frame for regression analysis.

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

Generate correlated data (predictors) for one unit

magRange Magnify the range of a variable.

summary.pctable

Extract presentation from a pctable object

print.summary.pctable

print method for summary.pctable objects

formatNumericSummaries

Numeric output data.frame from summarize is reformatted as one column per variable with summary statistics in the rows

genCorrelatedData

Generates a data frame for regression analysis

Estimate leave-one-variable-out regressions

print.factorSummaries

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

Multi-collinearity diagnostics

retrieves estimates of the coefficient of determination from a list of regressions

Create predicted values after choosing values of predictors. Can demonstrate marginal effects of the predictor variables.

Calculate skewness

getDeltaRsquare

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

Create correlation matrices.

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

Illustrate multicollinearity in regression, part 1.

model.data.default

Create a data frame suitable for estimating a model

Create covariance matrix from correlation and standard deviation information

Generic function for plotting regressions and interaction effects

Creates a publication quality result table for regression models. outreg0 is the last version in the last development stream.

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

Display pctable objects

makeVec for checking or creating vectors

dir.create.unique

Create a uniquely named directory. Appends number & optionally date to directory name.

Convert LaTeX output from outreg to HTML markup

print.summarize

print method for output from summarize

Create sequences for plotting

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

Create a focal value vector.

Calculates a "residual-centered" interaction regression.

Create a newdata frame for usage in predict methods

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

Estimate standardized regression coefficients for all variables

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

Converts the R-square to the variance inflation factor

Tabulates observed values and calculates entropy

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

Calculate excess kurtosis

plot.testSlopes

Plot testSlopes objects

Hypothesis tests for Simple Slopes Objects

Creates a cross tabulation with counts and percentages

Assists creation of predicted value curves for regression models.

Religious beliefs and crime rates

rockchalk-package

rockchalk: regression functions

summarizeNumerics

Extracts numeric variables and presents an summary in a workable format.

Regression plots with predicted value lines, confidence intervals, color coded interactions

summarizeFactors

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

Sorts numeric from discrete variables and returns separate summaries for those types of variables.

Returns center values of x, the mean, mean-std.dev, mean+std.dev

Central Tendency estimates for variables

recode a factor by "combining" levels

A way of checking if a string is a valid file name.

Superimpose regression lines on a plotted plane

Select most frequently occurring values from numeric or categorical variables.

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

Cheating and Looting in Japanese Electoral Politics

Calculates the "center" quantiles, always including the median, when n is odd.

Check a matrix for positive definitness

Select focal values from an observed variable.