compile the correlation table

library(regrrr)

## Warning: package 'regrrr' was built under R version 3.5.2

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.

This is an R package “regrrr” on Cran I coauthored with Rui Yang.

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.

compile the correlation table

library(regrrr)

## Warning: package 'regrrr' was built under R version 3.5.2

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.