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Summary Method for Linear Model (lm) Objects

Source: R/summary.lm.R
summary.lm.Rd

Computes a comprehensive summary for an object of class lm, including performance statistics, ANOVA, coefficients with VIFs, and correlation/covariance tables. Handles factor variable recoding and collinearity/singularity warnings.

Usage

# S3 method for class 'lm'
summary(object, ...)

Arguments

object

An object of class lm.

...

Additional arguments (currently unused).

Value

An object of class summary.lm containing tables and statistics described above.

Details

The returned summary object includes:

  • stats: Performance statistics (F-statistic, R-squared, RMSE, etc.)

  • anova: Simplified ANOVA table (Sum of squares, mean squares, F-statistic, p-value)

  • coefficients: Table of regression coefficients with standard errors, t-stats, p-values, and VIFs

  • cov.unscaled, correlation: Covariance and correlation matrices for coefficients

  • v.correlation: Variable correlation matrix (for models with interaction terms)

  • fits: Observed, fitted, and residual values

  • aliased: Logical vector indicating aliased coefficients

  • df: Degrees of freedom

  • sigma: Estimated standard deviation of residuals

  • r.squared, adj.r.squared: R-squared and adjusted R-squared

  • fstatistic, f.pval: F-statistic and p-value for overall regression

  • notes: Warnings, singularity, and collinearity notes (attached as attribute)

Factor variable names are recoded for clarity, and coefficients for aliased or singular variables are omitted with notes produced as attributes.

See also

print.summary.lm, lm

Examples

mdl <- lm(Sepal.Length ~ Sepal.Width + Petal.Length, data = iris)
sumry <- summary(mdl)
sumry
#> 
#> Summary Statistics:
#>                  Value      Performance    Measure  Err(Resids)    Metric
#> Observations =     150      R-Squared =    0.84018       MAPE =  0.045172
#> F-Statistic =   386.39      Adj-R2 =        0.8380       MAD  =   0.26563
#> Pr(b's=0),% =   <2e-16 ***  Std.Err.Est =  0.33329       RMSE =   0.32994
#> 
#> Analysis of Variance:
#>                Deg.Frdm  Sum.of.Sqs  Mean.Sum.Sqs  F.statistic  p-value(F)    
#> Regression            2      85.840      42.91978       386.39      <2e-16 ***
#> Error(Resids)       147      16.329       0.11108                             
#> Total               149     102.168                                           
#> 
#> Coefficients:
#>               Coefficient  Std.Error   t-stat   p-value         VIF
#> (Intercept)       2.24914   0.247970   9.0702  7.04e-16 ***        
#> Sepal.Width       0.59552   0.069328   8.5899  1.16e-14 ***  1.2248
#> Petal.Length      0.47192   0.017118  27.5692   < 2e-16 ***  1.2248
#>                                                                      
#> Signif.Levels:  0 ‘***’ 0.001 ‘** ’ 0.01 ‘*  ’ 0.05 ‘.  ’ 0.1 ‘   ’ 1
#>                                                                       
#> Summary of    Min       1Q       Mean     Median      3Q        Max   
#> Residuals:  -0.9616   -0.2349   <3e-14   0.0007718  0.2145    0.7856  
#>                                                                            
#> Call:  lm(formula = Sepal.Length ~ Sepal.Width + Petal.Length, data = iris)