Computes linear regression for all independent variables on the specified dependent variable. Linear modeling of multiple independent variables uses stepwise regression modeling. If specified, preconditions for (multi-)collinearity and for homoscedasticity are checked.
Usage
regress(
data,
dependent_var,
...,
check_independenterrors = FALSE,
check_multicollinearity = FALSE,
check_homoscedasticity = FALSE
)Arguments
- data
- dependent_var
The dependent variable on which the linear model is fitted. Specify as column name.
- ...
Independent variables to take into account as (one or many) predictors for the dependent variable. Specify as column names. At least one has to be specified.
- check_independenterrors
if set, the independence of errors among any two cases is being checked using a Durbin-Watson test
- check_multicollinearity
if set, multicollinearity among all specified independent variables is being checked using the variance inflation factor (VIF) and the tolerance (1/VIF); this check can only be performed if at least two independent variables are provided, and all provided variables need to be numeric
- check_homoscedasticity
if set, homoscedasticity is being checked using a Breusch-Pagan test
Value
a tdcmm model
Examples
WoJ %>% regress(autonomy_selection, ethics_1)
#> # A tibble: 2 × 6
#> Variable B StdErr beta t p
#> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 3.99 0.0481 NA 82.9 0
#> 2 ethics_1 -0.0689 0.0259 -0.0766 -2.66 0.00798
#> # F(1, 1195) = 7.061023, p = 0.007983, R-square = 0.005874
WoJ %>% regress(autonomy_selection, work_experience, trust_government)
#> # A tibble: 3 × 6
#> Variable B StdErr beta t p
#> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 3.52 0.0906 NA 38.8 3.02e-213
#> 2 work_experience 0.0121 0.00211 0.164 5.72 1.35e- 8
#> 3 trust_government 0.0501 0.0271 0.0531 1.85 6.49e- 2
#> # F(2, 1181) = 17.400584, p = 0.000000, R-square = 0.028624