We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
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.
The text was updated successfully, but these errors were encountered:
6195f0d
Deleetdk
No branches or pull requests
Some kind of issue with interactions
The text was updated successfully, but these errors were encountered: