summarize_models() error

[Deleetdk]

Some kind of issue with interactions

> #unstandardized outcome
> list(
+   ols(ideo7 ~ P, data = d_subset),
+   ols(ideo7 ~ rcs(age) * gender + race + educ5 + P, data = d_subset),
+   ols(ideo7 ~ rcs(age) * gender + race + educ5 + rcs(P), data = d_subset)
+ ) ->
+   ideo_models
> ideo_models
[[1]]
Frequencies of Missing Values Due to Each Variable
ideo7     P 
   25     0 

Linear Regression Model
 
 ols(formula = ideo7 ~ P, data = d_subset)
 
 
                 Model Likelihood    Discrimination    
                       Ratio Test           Indexes    
 Obs    2262    LR chi2     19.24    R2       0.008    
 sigma1.5422    d.f.            1    R2 adj   0.008    
 d.f.   2260    Pr(> chi2) 0.0000    g        0.160    
 
 Residuals
 
       Min        1Q    Median        3Q       Max 
 -3.448074 -1.046157 -0.007705  1.058740  3.367328 
 
 
           Coef    S.E.   t      Pr(>|t|)
 Intercept  4.0306 0.0324 124.30 <0.0001 
 P         -0.1426 0.0325  -4.39 <0.0001 
 

[[2]]
Frequencies of Missing Values Due to Each Variable
 ideo7    age gender   race  educ5      P 
    25      0      0      0      0      0 

Linear Regression Model
 
 ols(formula = ideo7 ~ rcs(age) * gender + race + educ5 + P, data = d_subset)
 
 
                 Model Likelihood    Discrimination    
                       Ratio Test           Indexes    
 Obs    2262    LR chi2    165.76    R2       0.071    
 sigma1.4977    d.f.           15    R2 adj   0.064    
 d.f.   2246    Pr(> chi2) 0.0000    g        0.466    
 
 Residuals
 
       Min        1Q    Median        3Q       Max 
 -3.753105 -1.035751  0.001144  1.146863  4.020785 
 
 
                             Coef    S.E.   t     Pr(>|t|)
 Intercept                    3.7084 0.4913  7.55 <0.0001 
 age                          0.0255 0.0172  1.49 0.1370  
 age'                        -0.0391 0.0872 -0.45 0.6534  
 age''                        0.0494 0.2576  0.19 0.8480  
 age'''                       0.0222 0.4880  0.05 0.9638  
 gender=Male                  1.1343 0.6837  1.66 0.0972  
 race=2+ Races, Non-Hispanic -0.4156 0.2280 -1.82 0.0684  
 race=Black, Non-Hispanic    -0.7000 0.0909 -7.70 <0.0001 
 race=Hispanic               -0.4378 0.0934 -4.69 <0.0001 
 race=Other, Non-Hispanic    -0.2276 0.1133 -2.01 0.0447  
 educ5                       -0.1628 0.0279 -5.84 <0.0001 
 P                           -0.1464 0.0328 -4.46 <0.0001 
 age * gender=Male           -0.0406 0.0239 -1.70 0.0899  
 age' * gender=Male           0.2676 0.1218  2.20 0.0281  
 age'' * gender=Male         -0.8105 0.3606 -2.25 0.0247  
 age''' * gender=Male         1.4606 0.6859  2.13 0.0333  
 

[[3]]
Frequencies of Missing Values Due to Each Variable
 ideo7    age gender   race  educ5      P 
    25      0      0      0      0      0 

Linear Regression Model
 
 ols(formula = ideo7 ~ rcs(age) * gender + race + educ5 + rcs(P), 
     data = d_subset)
 
 
                 Model Likelihood    Discrimination    
                       Ratio Test           Indexes    
 Obs    2262    LR chi2    175.86    R2       0.075    
 sigma1.4953    d.f.           18    R2 adj   0.067    
 d.f.   2243    Pr(> chi2) 0.0000    g        0.479    
 
 Residuals
 
       Min        1Q    Median        3Q       Max 
 -3.869394 -1.022600 -0.001861  1.144102  3.950326 
 
 
                             Coef     S.E.   t     Pr(>|t|)
 Intercept                     4.0697 0.5239  7.77 <0.0001 
 age                           0.0289 0.0172  1.68 0.0928  
 age'                         -0.0516 0.0871 -0.59 0.5540  
 age''                         0.0749 0.2573  0.29 0.7710  
 age'''                        0.0196 0.4873  0.04 0.9679  
 gender=Male                   1.2574 0.6843  1.84 0.0663  
 race=2+ Races, Non-Hispanic  -0.4050 0.2277 -1.78 0.0754  
 race=Black, Non-Hispanic     -0.6970 0.0909 -7.67 <0.0001 
 race=Hispanic                -0.4504 0.0934 -4.82 <0.0001 
 race=Other, Non-Hispanic     -0.2417 0.1133 -2.13 0.0330  
 educ5                        -0.1623 0.0279 -5.81 <0.0001 
 P                             0.1247 0.1324  0.94 0.3465  
 P'                           -1.5757 0.5681 -2.77 0.0056  
 P''                          14.0475 4.5772  3.07 0.0022  
 P'''                        -24.0181 7.6831 -3.13 0.0018  
 age * gender=Male            -0.0448 0.0240 -1.87 0.0619  
 age' * gender=Male            0.2843 0.1218  2.34 0.0196  
 age'' * gender=Male          -0.8495 0.3604 -2.36 0.0185  
 age''' * gender=Male          1.4916 0.6851  2.18 0.0296  
 

> ideo_models %>% 
+   summarize_models()
Error in `dplyr::mutate()`:
! Problem while computing `term = factor(term, levels = term_order)`.
Caused by error in `levels<-`:
! factor level [21] is duplicated
Run `rlang::last_error()` to see where the error occurred.