“regrrr”: One-stop R-toolkit for Compiling Regression Results

2019/03/06

Categories: R Package visualization Tags: R visualization

Today, I released an R package “regrrr” on Cran. In strategy/management research, we always need to compile the regression results into the publishable format and sometimes plot the moderating effects. This package does the job.

Here is the quickstart guide.

Rdoc

Installation

To install from CRAN:

install.packages("regrrr")
library(regrrr)

You can also use devtools to install the latest development version:

devtools::install_github("raykyang/regrrr")
library(regrrr)

Examples

compile the correlation table

library(regrrr)
data(mtcars)
m0 <- lm(mpg ~ vs + carb + hp + wt, data = mtcars)
m1 <- update(m0, . ~ . + wt * hp)
m2 <- update(m1, . ~ . + wt * vs)
cor.table(data = m2$model)
##          Mean  S.D.     1     2    3    4    5
## 1.mpg   20.09  6.03  1.00                     
## 2.vs     0.44  0.50  0.66  1.00               
## 3.carb   2.81  1.62 -0.55 -0.57 1.00          
## 4.hp   146.69 68.56 -0.78 -0.72 0.75 1.00     
## 5.wt     3.22  0.98 -0.87 -0.55 0.43 0.66 1.00

compile the regression table

regression_table <- rbind(
combine_long_tab(to_long_tab(summary(m0)$coef),
                 to_long_tab(summary(m1)$coef),
                 to_long_tab(summary(m2)$coef)),
compare_models(m0, m1, m2))
rownames(regression_table) <- NULL
print(regression_table)
##        Variables   Model 0   Model 1   Model 2
## 1    (Intercept) 35.435*** 48.157*** 46.698***
## 2                  (2.503)   (4.097)   (9.272)
## 3             vs     1.353     1.077     2.171
## 4                  (1.382)   (1.152)   (6.320)
## 5           carb    -0.057    -0.043    -0.009
## 6                  (0.449)   (0.374)   (0.426)
## 7             hp   -0.024† -0.113***   -0.107*
## 8                  (0.014)   (0.027)   (0.044)
## 9             wt -3.792*** -8.071***   -7.594*
## 10                 (0.658)   (1.307)   (3.016)
## 11         hp:wt             0.027**    0.025†
## 12                           (0.008)   (0.015)
## 13         vs:wt                        -0.367
## 14                                     (2.081)
## 15     R_squared     0.833     0.889     0.889
## 16 Adj_R_squared     0.808     0.867     0.862
## 17       Delta_F            12.517**     0.031

plot the moderating effect

plot_effect(reg.coef = summary(m2)$coefficients, data = mtcars, model = m2,
            x_var.name = "wt", y_var.name = "mpg", moderator.name = "hp",
            confidence_interval = TRUE,  CI_Ribbon = FALSE, 
            xlab = "Weight", ylab = "MPG", moderator.lab = "Horsepower") +
ggplot2::theme(text=ggplot2::element_text(family="Times New Roman", size = 16))

As you can see from the last line of code, the plot is customizable using “ggplot2”. There are a couple of other functions. Please see the reference manual on R documentation for details.

I’m also aiming to expand the package’s usage around its core functions. If you have any ideas or want to report a bug, please contact me or suggest on the GitHub page.


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