This is a minor release for bug fixes and other enhancements.
Enhancements
- force variables to be included or excluded from the model at all stages of variable selection
- variable selection methods allow use of the following metrics:
- p value
- akaike information criterion (aic)
- schwarz bayesian criterion (sbc)
- sawa bayesian criterion (sbic)
- r-square
- adjusted r-square
- hierarchical selection can be enabled when using
pvalues as variable selection metric - choose threshold for determining influential observations in
ols_plot_dffits()
Bug Fixes
- allow users to specify threshold for detecting outliers (#178)
- if
ols_test_outlier()does not find any outliers, it returns largest positive residual instead of largest absolute residual (#177) - using
ols_step_all_possible()with Model created from dynamic function leads to"Error in eval(model$call$data) . . . not found"(#176) ols_step_both_p(): Error in if (pvals[minp] <= pent) {: argument is of length zero(#175)- handle extremely significant variables (#173)
ols_correlations()returns error for models with 2 predictors (#168)ols_step_both_aic()doesn't return model (#167)ols_regress()returned residual standard error instead of RMSE (@jens-daniel-mueller, #165)- extracting model data (#159)
- ols_plot_resid_stud() fails to plot outliers due to y-axis range (#155)
- ols_correlations error (#191)
- mallow's Cp behaves inconsistently depending on model specification (#196)
- ols_step_forward_p(...) problem using the funtion ols_step_forward_p (#200)
- output of the command "ols_step_both_aic" doesn't contain final model (#201)
Contributors
jens-daniel-mueller