Dummy variable coefficients for ordered factors comes out wonky in regression tables

[rudeboybert]

Might need to unconvert all ordered = TRUE factors to unordered.

suppressPackageStartupMessages(library(tidyverse))
library(fivethirtyeight)
library(moderndive)

# clean_test is ordered factor
bechdel$clean_test[1:5]
#> [1] notalk ok     notalk notalk men   
#> Levels: nowomen < notalk < men < dubious < ok

# weird output for dummy variables in regression table
lm(domgross~clean_test, data = bechdel) %>% 
  get_regression_table()
#> Warning: package 'bindrcpp' was built under R version 3.4.4
#> # A tibble: 5 x 7
#>   term           estimate std_error statistic p_value   conf_low conf_high
#>   <chr>             <dbl>     <dbl>     <dbl>   <dbl>      <dbl>     <dbl>
#> 1 intercept     70491451.  2412903.    29.2    0.      65759017. 75223886.
#> 2 clean_test.L  -3804412.  5244171.    -0.725  0.468  -14089823.  6480999.
#> 3 clean_test.Q  -9916737.  5398245.    -1.84   0.0660 -20504334.   670861.
#> 4 clean_test.C    750590.  5353012.     0.140  0.889   -9748292. 11249471.
#> 5 clean_test^4  -9507099.  5580759.    -1.70   0.0890 -20452662.  1438464.
lm(domgross~clean_test, data = bechdel) %>% 
  summary()
#> 
#> Call:
#> lm(formula = domgross ~ clean_test, data = bechdel)
#> 
#> Residuals:
#>       Min        1Q    Median        3Q       Max 
#> -79345264 -52417225 -25031559  22438057 691533349 
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)  70491451    2412903  29.214   <2e-16 ***
#> clean_test.L -3804412    5244171  -0.725   0.4683    
#> clean_test.Q -9916736    5398245  -1.837   0.0664 .  
#> clean_test.C   750590    5353012   0.140   0.8885    
#> clean_test^4 -9507099    5580759  -1.704   0.0886 .  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 80100000 on 1772 degrees of freedom
#>   (17 observations deleted due to missingness)
#> Multiple R-squared:  0.008974,   Adjusted R-squared:  0.006737 
#> F-statistic: 4.012 on 4 and 1772 DF,  p-value: 0.003042

# should look like
bechdel %>% 
  mutate(clean_test = factor(clean_test, ordered = FALSE)) %>% 
  lm(domgross~clean_test, data = .) %>% 
  get_regression_table()
#> # A tibble: 5 x 7
#>   term            estimate std_error statistic p_value  conf_low conf_high
#>   <chr>              <dbl>     <dbl>     <dbl>   <dbl>     <dbl>     <dbl>
#> 1 intercept         6.62e7  6793664.     9.75   0.        5.29e7 79547620.
#> 2 clean_testnot…    1.31e7  7663750.     1.72   0.0870   -1.89e6 28172609.
#> 3 clean_testmen     2.75e6  8910344.     0.309  0.758    -1.47e7 20226986.
#> 4 clean_testdub…    9.79e6  9573562.     1.02   0.307    -8.99e6 28562779.
#> 5 clean_testok     -4.34e6  7364354.    -0.589  0.556    -1.88e7 10106207.
bechdel %>% 
  mutate(clean_test = factor(clean_test, ordered = FALSE)) %>% 
  lm(domgross~clean_test, data = .) %>% 
  summary()
#> 
#> Call:
#> lm(formula = domgross ~ clean_test, data = .)
#> 
#> Residuals:
#>       Min        1Q    Median        3Q       Max 
#> -79345264 -52417225 -25031559  22438057 691533349 
#> 
#> Coefficients:
#>                   Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)       66223181    6793664   9.748   <2e-16 ***
#> clean_testnotalk  13141668    7663750   1.715   0.0866 .  
#> clean_testmen      2751095    8910344   0.309   0.7575    
#> clean_testdubious  9786117    9573562   1.022   0.3068    
#> clean_testok      -4337528    7364354  -0.589   0.5559    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 80100000 on 1772 degrees of freedom
#>   (17 observations deleted due to missingness)
#> Multiple R-squared:  0.008974,   Adjusted R-squared:  0.006737 
#> F-statistic: 4.012 on 4 and 1772 DF,  p-value: 0.003042

Created on 2018-04-04 by the reprex package (v0.2.0).