Regression tables with huxreg

From version 0.2, huxtable includes the function huxreg to build a table of regressions.

huxreg can be called with a list of models. These models can be of any class which has a tidy method defined in the broom package. The method should return a list of regression coefficients with names term, estimate, std.error and p.value. That covers most standard regression packages.

Let’s start by running some regressions to predict a diamond’s price.

data(diamonds, package = 'ggplot2')

lm1 <- lm(price ~ carat + depth, diamonds)
lm2 <- lm(price ~ depth + factor(color, ordered = FALSE), diamonds)
lm3 <- lm(log(price) ~ carat + depth, diamonds)

Now, we call huxreg to display the regression output side by side.

huxreg(lm1, lm2, lm3)
(1) (2) (3)
(Intercept) 4045.333 *** 6491.466 *** 7.313 ***
(286.205)    (730.537)    (0.074)   
carat 7765.141 ***          1.971 ***
(14.009)             (0.004)   
depth -102.165 *** -53.835 *** -0.018 ***
(4.635)    (11.815)    (0.001)   
factor(color, ordered = FALSE)E          -95.142             
         (62.037)            
factor(color, ordered = FALSE)F          554.742 ***         
         (62.374)            
factor(color, ordered = FALSE)G          832.357 ***         
         (60.338)            
factor(color, ordered = FALSE)H          1324.183 ***         
         (64.296)            
factor(color, ordered = FALSE)I          1929.902 ***         
         (71.561)            
factor(color, ordered = FALSE)J          2164.044 ***         
         (88.144)            
N 53940         53940         53940        
R2 0.851     0.032     0.847    
logLik -472488.441     -522908.139     -26617.649    
AIC 944984.882     1045834.277     53243.298    
*** p < 0.001; ** p < 0.01; * p < 0.05.

The basic output includes estimates, standard errors and summary statistics.

Some of those variable names are hard to read. We can change them by specifying a named list of variables in the coefs argument, like this:

color_names <- paste0('factor(color, ordered = FALSE)', LETTERS[5:10])
names(color_names) <- paste('Color:', LETTERS[5:10])

huxreg(lm1, lm2, lm3, coefs = c('Carat' = 'carat', 'Depth' = 'depth', color_names))
(1) (2) (3)
Carat 7765.141 ***          1.971 ***
(14.009)             (0.004)   
Depth -102.165 *** -53.835 *** -0.018 ***
(4.635)    (11.815)    (0.001)   
Color: E          -95.142             
         (62.037)            
Color: F          554.742 ***         
         (62.374)            
Color: G          832.357 ***         
         (60.338)            
Color: H          1324.183 ***         
         (64.296)            
Color: I          1929.902 ***         
         (71.561)            
Color: J          2164.044 ***         
         (88.144)            
N 53940         53940         53940        
R2 0.851     0.032     0.847    
logLik -472488.441     -522908.139     -26617.649    
AIC 944984.882     1045834.277     53243.298    
*** p < 0.001; ** p < 0.01; * p < 0.05.

Alternatively, since the output from huxreg is just a huxtable, we could just edit its contents directly before we print it:

diamond_regs <- huxreg(lm1, lm2, lm3)
diamond_regs[seq(8, 18, 2), 1] <- paste('Color:', LETTERS[5:10])
diamond_regs
(1) (2) (3)
(Intercept) 4045.333 *** 6491.466 *** 7.313 ***
(286.205)    (730.537)    (0.074)   
carat 7765.141 ***          1.971 ***
(14.009)             (0.004)   
depth -102.165 *** -53.835 *** -0.018 ***
(4.635)    (11.815)    (0.001)   
Color: E          -95.142             
         (62.037)            
Color: F          554.742 ***         
         (62.374)            
Color: G          832.357 ***         
         (60.338)            
Color: H          1324.183 ***         
         (64.296)            
Color: I          1929.902 ***         
         (71.561)            
Color: J          2164.044 ***         
         (88.144)            
N 53940         53940         53940        
R2 0.851     0.032     0.847    
logLik -472488.441     -522908.139     -26617.649    
AIC 944984.882     1045834.277     53243.298    
*** p < 0.001; ** p < 0.01; * p < 0.05.

Of course, we aren’t limited to just changing names. We can also make our table prettier. Let’s add the “article” theme, and a vertical stripe for background colour, tweak a few details like font size, and add a caption. All of these are just standard huxtable commands.

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following object is masked from 'package:huxtable':
## 
##     add_rownames
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
diamond_regs                                                         %>% 
      theme_article                                                  %>% 
      set_background_color(1:nrow(diamond_regs), evens, grey(.95)) %>% 
      set_font_size(final(), 1, 9)                                   %>% 
      set_bold(final(), 1, FALSE)                                    %>%
      set_top_border(final(), 1, 1)                                  %>%
      set_caption('Linear regressions of diamond prices')
Linear regressions of diamond prices
(1) (2) (3)
(Intercept) 4045.333 *** 6491.466 *** 7.313 ***
(286.205)    (730.537)    (0.074)   
carat 7765.141 ***          1.971 ***
(14.009)             (0.004)   
depth -102.165 *** -53.835 *** -0.018 ***
(4.635)    (11.815)    (0.001)   
Color: E          -95.142             
         (62.037)            
Color: F          554.742 ***         
         (62.374)            
Color: G          832.357 ***         
         (60.338)            
Color: H          1324.183 ***         
         (64.296)            
Color: I          1929.902 ***         
         (71.561)            
Color: J          2164.044 ***         
         (88.144)            
N 53940         53940         53940        
R2 0.851     0.032     0.847    
logLik -472488.441     -522908.139     -26617.649    
AIC 944984.882     1045834.277     53243.298    
*** p < 0.001; ** p < 0.01; * p < 0.05.

We could do more, like changing the number_format of N to not display decimals. But let’s explore what else huxreg itself can do.

By default, standard errors are shown below coefficient estimates. To display them in a column to the right, use error_pos = 'right':

huxreg(lm1, lm3, error_pos = 'right')
(1) (2)
(Intercept) 4045.333 *** (286.205) 7.313 *** (0.074)
carat 7765.141 *** (14.009) 1.971 *** (0.004)
depth -102.165 *** (4.635) -0.018 *** (0.001)
N 53940               53940              
R2 0.851           0.847          
logLik -472488.441           -26617.649          
AIC 944984.882           53243.298          
*** p < 0.001; ** p < 0.01; * p < 0.05.

This will give column headings a column span of 2.

To display standard errors in the same cell as estimates, use error_pos = 'same':

huxreg(lm1, lm3, error_pos = 'same')
(1) (2)
(Intercept) 4045.333 *** (286.205) 7.313 *** (0.074)
carat 7765.141 *** (14.009) 1.971 *** (0.004)
depth -102.165 *** (4.635) -0.018 *** (0.001)
N 53940      53940     
R2 0.851  0.847 
logLik -472488.441  -26617.649 
AIC 944984.882  53243.298 
*** p < 0.001; ** p < 0.01; * p < 0.05.

You can change the default column headings by giving names to your models:

huxreg('Price' = lm1, 'Log price' = lm3)
Price Log price
(Intercept) 4045.333 *** 7.313 ***
(286.205)    (0.074)   
carat 7765.141 *** 1.971 ***
(14.009)    (0.004)   
depth -102.165 *** -0.018 ***
(4.635)    (0.001)   
N 53940         53940        
R2 0.851     0.847    
logLik -472488.441     -26617.649    
AIC 944984.882     53243.298    
*** p < 0.001; ** p < 0.01; * p < 0.05.

To display a particular row of summary statistics, use the statistics parameter. This should be a character vector. Valid values are anything returned from your models by broom::glance. Another valid value is "nobs", which returns the number of observations from the regression. If the statistics vector has names, these will be used for row headings:

broom::glance(lm1)
##   r.squared adj.r.squared    sigma statistic p.value df    logLik      AIC
## 1 0.8506755     0.8506699 1541.649  153634.8       0  3 -472488.4 944984.9
##        BIC     deviance df.residual
## 1 945020.5 128191108498       53937
huxreg(lm1, lm3, statistics = c('# observations' = 'nobs', 'R squared' = 'r.squared', 'F statistic' = 'statistic',
  'P value' = 'p.value'))
(1) (2)
(Intercept) 4045.333 *** 7.313 ***
(286.205)    (0.074)   
carat 7765.141 *** 1.971 ***
(14.009)    (0.004)   
depth -102.165 *** -0.018 ***
(4.635)    (0.001)   
# observations 53940         53940        
R squared 0.851     0.847    
F statistic 153634.765     149771.327    
P value 0.000     0.000    
*** p < 0.001; ** p < 0.01; * p < 0.05.

You aren’t limited to displaying standard errors of the estimates. If you prefer, you can display t statistics or p values, using the error_style option:

huxreg(lm1, lm3, error_style = 'statistic')
(1) (2)
(Intercept) 4045.333 *** 7.313 ***
(14.134)    (99.383)   
carat 7765.141 *** 1.971 ***
(554.282)    (547.305)   
depth -102.165 *** -0.018 ***
(-22.041)    (-14.936)   
N 53940         53940        
R2 0.851     0.847    
logLik -472488.441     -26617.649    
AIC 944984.882     53243.298    
*** p < 0.001; ** p < 0.01; * p < 0.05. 
huxreg(lm1, lm3, error_style = 'pvalue')
(1) (2)
(Intercept) 4045.333 *** 7.313 ***
(0.000)    (0.000)   
carat 7765.141 *** 1.971 ***
(0.000)    (0.000)   
depth -102.165 *** -0.018 ***
(0.000)    (0.000)   
N 53940         53940        
R2 0.851     0.847    
logLik -472488.441     -26617.649    
AIC 944984.882     53243.298    
*** p < 0.001; ** p < 0.01; * p < 0.05.

Or you can display confidence intervals using ‘ci’. Use ci_level to set the confidence level for the interval:

huxreg(lm1, lm3, error_style = 'ci') # default is .95
(1) (2)
(Intercept) 4045.333 *** 7.313 ***
(3484.381 -- 4606.285)    (7.169 -- 7.457)   
carat 7765.141 *** 1.971 ***
(7737.683 -- 7792.599)    (1.964 -- 1.978)   
depth -102.165 *** -0.018 ***
(-111.250 -- -93.080)    (-0.020 -- -0.015)   
N 53940         53940        
R2 0.851     0.847    
logLik -472488.441     -26617.649    
AIC 944984.882     53243.298    
*** p < 0.001; ** p < 0.01; * p < 0.05. 
huxreg(lm1, lm3, error_style = 'ci', ci_level = .99)
(1) (2)
(Intercept) 4045.333 *** 7.313 ***
(3308.117 -- 4782.549)    (7.123 -- 7.502)   
carat 7765.141 *** 1.971 ***
(7729.055 -- 7801.226)    (1.962 -- 1.981)   
depth -102.165 *** -0.018 ***
(-114.105 -- -90.226)    (-0.021 -- -0.015)   
N 53940         53940        
R2 0.851     0.847    
logLik -472488.441     -26617.649    
AIC 944984.882     53243.298    
*** p < 0.001; ** p < 0.01; * p < 0.05.

If you choose more than one error_style option, the second one will be shown in square brackets:

huxreg(lm1, lm3, error_style = c('stderr', 'ci'))
(1) (2)
(Intercept) 4045.333 *** 7.313 ***
(286.205) [3484.381 -- 4606.285]    (0.074) [7.169 -- 7.457]   
carat 7765.141 *** 1.971 ***
(14.009) [7737.683 -- 7792.599]    (0.004) [1.964 -- 1.978]   
depth -102.165 *** -0.018 ***
(4.635) [-111.250 -- -93.080]    (0.001) [-0.020 -- -0.015]   
N 53940         53940        
R2 0.851     0.847    
logLik -472488.441     -26617.649    
AIC 944984.882     53243.298    
*** p < 0.001; ** p < 0.01; * p < 0.05.

To change the footnote, use note. If note contains the string "%stars%" it will be replaced by a description of the significance stars used. If you don’t want a footnote, just set note = NULL.

huxreg(lm1, lm3, note = 'Linear regressions on diamond price. %stars%.')
(1) (2)
(Intercept) 4045.333 *** 7.313 ***
(286.205)    (0.074)   
carat 7765.141 *** 1.971 ***
(14.009)    (0.004)   
depth -102.165 *** -0.018 ***
(4.635)    (0.001)   
N 53940         53940        
R2 0.851     0.847    
logLik -472488.441     -26617.649    
AIC 944984.882     53243.298    
Linear regressions on diamond price. *** p < 0.001; ** p < 0.01; * p < 0.05.

To change number formatting, set the number_format parameter. This works the same as the number_format property for a huxtable - if it is numeric, numbers will be rounded to that many decimal places; if it is character, it will be taken as a format to the base R sprintf function; if it is a function, the function will be called to format the number. huxreg tries to be smart and to format summary statistics like nobs as integers.

huxreg(lm1, lm3, number_format = 2)
(1) (2)
(Intercept) 4045.33 *** 7.31 ***
(286.21)    (0.07)   
carat 7765.14 *** 1.97 ***
(14.01)    (0.00)   
depth -102.17 *** -0.02 ***
(4.64)    (0.00)   
N 53940        53940       
R2 0.85     0.85    
logLik -472488.44     -26617.65    
AIC 944984.88     53243.30    
*** p < 0.001; ** p < 0.01; * p < 0.05.

Lastly, if you want to bold all significant coefficients, set the parameter bold_signif to a maximum significance level:

huxreg(lm1, lm3, bold_signif = 0.05)
(1) (2)
(Intercept) 4045.333 *** 7.313 ***
(286.205)    (0.074)   
carat 7765.141 *** 1.971 ***
(14.009)    (0.004)   
depth -102.165 *** -0.018 ***
(4.635)    (0.001)   
N 53940         53940        
R2 0.851     0.847    
logLik -472488.441     -26617.649    
AIC 944984.882     53243.298    
*** p < 0.001; ** p < 0.01; * p < 0.05.