An object with class "factorSummaries" is the input. Such an object should be created with the function rockchalk::summarizeFactors. Each element in that list is then organized for printing in a tabular summary. This should look almost like R's own summary function, except for the additional information that these factor summaries include.
plotSlopes
(Package: rockchalk) :
Generic function for plotting regressions and interaction effects
This is a generic function for plotting regression objects. So far, there is an implementation for lm() objects. This allows interaction effects, but not nonlinearities like log(x1). For that, please see plotCurves.
outreg0
(Package: rockchalk) :
Creates a publication quality result table for regression models.
outreg0 writes its output directly to the terminal, but does not create an output object. The new version of this function–which I wish you would try instead–does the same work, but it also creates an output object that can be transformed for other purposes.
summarize
(Package: rockchalk) :
Sorts numeric from factor variables and returns separate
The work is done by the functions summarizeNumerics and summarizeFactors. Please see the help pages for those functions for complete details. Named argumes used here must be spelled fully so they can be sorted and passed to those 2 funcitons.
Includes an ever-growing collection of functions that assist in the presentation of regression models. The initial function was outreg, which produces LaTeX tables that summarize one or many fitted regression models. It also offers plotting conveniences like plotPlane and plotSlopes, which illustrate some of the variables from a fitted regression model. For a detailed check on multicollinearity, see mcDiagnose. The user should be aware of this fact: Not all of these functions lead to models or types of analysis that we endorese. Rather, they all lead to analysis that is endorsed by some scholars, and we feel it is important to facilitate the comparison of competing methods. For example, the function standardize will calculate standardized regression coefficients for all predictors in a regression model's design matrix in order to replicate results from other statistical frameworks, no matter how unwise the use of such coefficients might be. The function meanCenter will allow the user to more selectively choose variables for centering (and possibly standardization) before they are entered into the design matrix. Because of the importance of interaction variables in regression analysis, the residualCenter and meanCenter functions are offered. While mean centering does not actually help with multicollinearity of interactive terms, many scholars have argued that it does. The meanCenter function can be compared with the "residual centering" of interaction terms.
Use can supply either a single value (the common correlation among all variables), a column of the lower triangular values for a correlation matrix, or a candidate matrix. The function will check X and do the right thing. If X is a matrix, check that it is a valid correlation matrix. If its a single value, use that to fill up a matrix. If itis a vector, try to use it as a vech to fill the lower triangle..
getPartialCor
(Package: rockchalk) :
Calculates partial correlation coefficients after retrieving data matrix froma fitted regression model
The input is a fitted regression model, from which the design matrix is retrieved, along with the dependent variable. The partial correlation is calculated using matrix algebra that has not been closely inspected for numerical precision. That is to say, it is in the stats book style, rather than the numerically optimized calculating format that functions like lm() have adopted.
plotPlane
(Package: rockchalk) :
Draw a 3-D regression plot for two predictors from any linear or nonlinear lm or glm object
This allows user to fit a regression model with many variables and then plot 2 of its predictors and the output plane for those predictors with other variables set at mean or mode (numeric or factor). This is a front-end (wrapper) for R's persp function. Persp does all of the hard work, this function reorganizes the information for the user in a more readily understood way. It intended as a convenience for students (or others) who do not want to fight their way through the details needed to use persp to plot a regression plane. The fitted model can have any number of input variables, this will display only two of them. And, at least for the moment, I insist these predictors must be numeric variables. They can be transformed in any of the usual ways, such as poly, log, and so forth.
providing 
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