Summary method for linear regression results
Source:R/linear_regression.R
summary.linear_regression.RdCreates a summary object that produces detailed output when printed, including model summary, ANOVA table, and coefficient table.
Usage
# S3 method for class 'linear_regression'
summary(
object,
model_summary = TRUE,
anova_table = TRUE,
coefficients = TRUE,
conf_int = TRUE,
collinearity = TRUE,
descriptives = TRUE,
digits = 3,
...
)Arguments
- object
A
linear_regressionresult object.- model_summary
Logical. Show model summary (R, R-squared)? (Default: TRUE)
- anova_table
Logical. Show ANOVA table? (Default: TRUE)
- coefficients
Logical. Show coefficients table? (Default: TRUE)
- conf_int
Logical. Show the confidence-interval columns for B in the coefficients table (SPSS
/STATISTICS CI)? The interval level is theconf.levelpassed tolinear_regression. (Default: TRUE)- collinearity
Logical. Show collinearity diagnostics (Tolerance, VIF per model term)? (Default: TRUE)
- descriptives
Logical. Show the Descriptive Statistics table (Mean, SD, N for the dependent and predictor variables)? (Default: TRUE)
- digits
Number of decimal places for formatting (Default: 3).
- ...
Additional arguments (not used).
See also
linear_regression for the main analysis function.
Examples
result <- linear_regression(survey_data, life_satisfaction ~ age + trust_government)
summary(result)
#>
#> Linear Regression Results
#> -------------------------
#> - Formula: life_satisfaction ~ age + trust_government
#> - Method: ENTER (all predictors)
#> - N: 2280
#>
#> Descriptive Statistics
#> -----------------------------------------------
#> Variable Mean Std. Deviation N
#> -----------------------------------------------
#> life_satisfaction 3.641 1.151 2280
#> age 50.653 17.013 2280
#> trust_government 2.625 1.166 2280
#> -----------------------------------------------
#>
#> Model Summary
#> ----------------------------------
#> R 0.026
#> R Square 0.001
#> Adjusted R Square 0.000
#> Std. Error of the Estimate 1.151
#> ----------------------------------
#>
#> ANOVA
#> -------------------------------------------------------------
#> Source Sum of Squares df Mean Square F Sig.
#> -------------------------------------------------------------
#> Regression 2.058 2 1.029 0.776 .460
#> Residual 3018.749 2277 1.326
#> Total 3020.807 2279
#> -------------------------------------------------------------
#>
#> Coefficients
#> --------------------------------------------------------------------------------------------
#> Term B Std. Error Beta t Sig. 95% CI Lower 95% CI Upper
#> --------------------------------------------------------------------------------------------
#> (Intercept) 3.711 0.093 39.828 <.001 3.528 3.894 ***
#> age -0.002 0.001 -0.025 -1.208 .227 -0.004 0.001
#> trust_government 0.006 0.021 0.006 0.305 .760 -0.034 0.047
#> --------------------------------------------------------------------------------------------
#>
#> Collinearity Statistics
#> ----------------------------------
#> Term Tolerance VIF
#> ----------------------------------
#> age 1.000 1.000
#> trust_government 1.000 1.000
#> ----------------------------------
#> VIF > 10 (Tolerance < 0.1) indicates problematic collinearity.
#>
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05
summary(result, descriptives = FALSE)
#>
#> Linear Regression Results
#> -------------------------
#> - Formula: life_satisfaction ~ age + trust_government
#> - Method: ENTER (all predictors)
#> - N: 2280
#>
#> Model Summary
#> ----------------------------------
#> R 0.026
#> R Square 0.001
#> Adjusted R Square 0.000
#> Std. Error of the Estimate 1.151
#> ----------------------------------
#>
#> ANOVA
#> -------------------------------------------------------------
#> Source Sum of Squares df Mean Square F Sig.
#> -------------------------------------------------------------
#> Regression 2.058 2 1.029 0.776 .460
#> Residual 3018.749 2277 1.326
#> Total 3020.807 2279
#> -------------------------------------------------------------
#>
#> Coefficients
#> --------------------------------------------------------------------------------------------
#> Term B Std. Error Beta t Sig. 95% CI Lower 95% CI Upper
#> --------------------------------------------------------------------------------------------
#> (Intercept) 3.711 0.093 39.828 <.001 3.528 3.894 ***
#> age -0.002 0.001 -0.025 -1.208 .227 -0.004 0.001
#> trust_government 0.006 0.021 0.006 0.305 .760 -0.034 0.047
#> --------------------------------------------------------------------------------------------
#>
#> Collinearity Statistics
#> ----------------------------------
#> Term Tolerance VIF
#> ----------------------------------
#> age 1.000 1.000
#> trust_government 1.000 1.000
#> ----------------------------------
#> VIF > 10 (Tolerance < 0.1) indicates problematic collinearity.
#>
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05
summary(result, conf_int = FALSE) # hide the CI columns
#>
#> Linear Regression Results
#> -------------------------
#> - Formula: life_satisfaction ~ age + trust_government
#> - Method: ENTER (all predictors)
#> - N: 2280
#>
#> Descriptive Statistics
#> -----------------------------------------------
#> Variable Mean Std. Deviation N
#> -----------------------------------------------
#> life_satisfaction 3.641 1.151 2280
#> age 50.653 17.013 2280
#> trust_government 2.625 1.166 2280
#> -----------------------------------------------
#>
#> Model Summary
#> ----------------------------------
#> R 0.026
#> R Square 0.001
#> Adjusted R Square 0.000
#> Std. Error of the Estimate 1.151
#> ----------------------------------
#>
#> ANOVA
#> -------------------------------------------------------------
#> Source Sum of Squares df Mean Square F Sig.
#> -------------------------------------------------------------
#> Regression 2.058 2 1.029 0.776 .460
#> Residual 3018.749 2277 1.326
#> Total 3020.807 2279
#> -------------------------------------------------------------
#>
#> Coefficients
#> ----------------------------------------------------------------
#> Term B Std. Error Beta t Sig.
#> ----------------------------------------------------------------
#> (Intercept) 3.711 0.093 39.828 <.001 ***
#> age -0.002 0.001 -0.025 -1.208 .227
#> trust_government 0.006 0.021 0.006 0.305 .760
#> ----------------------------------------------------------------
#>
#> Collinearity Statistics
#> ----------------------------------
#> Term Tolerance VIF
#> ----------------------------------
#> age 1.000 1.000
#> trust_government 1.000 1.000
#> ----------------------------------
#> VIF > 10 (Tolerance < 0.1) indicates problematic collinearity.
#>
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05