Print a Summary for Linear Model Objects

Prints a comprehensive summary for objects of class summary.lm or lm, including model statistics, ANOVA table, coefficients, and optional tables (correlations, covariance, fits), followed by a five-number summary of residuals and the model call.

Usage

# S3 method for class 'summary.lm'
print(
  x,
  digits = max(5, getOption("digits") - 2),
  symbolic.cor = NULL,
  signif.stars = getOption("show.signif.stars"),
  options = NULL,
  na.print = "",
  eps = .Machine$double.eps,
  ...
)

Arguments

x

An object of class summary.lm or lm.

digits

Minimal number of significant digits. Defaults to max(5, getOption("digits") - 2).

symbolic.cor

Not implemented. Defaults to NULL.

signif.stars

Logical; whether to show significance stars in the coefficients table. Defaults to getOption("show.signif.stars").

options

A character vector of optional summary tables to print (e.g., "v.correlation", "cov.unscaled", "correlation", "fits"). Printed in the given order if present.

na.print

String to use for NA values in the tables.

eps

Smallest positive floating-point value, used for formatting near-zero residuals. Defaults to .Machine$double.eps.

...

Additional arguments (not currently used).

Details

The function prints summary statistics, ANOVA, and coefficients tables for a linear model in order, along with specified optional tables if provided. It concludes with a five-number-plus-mean summary of residuals and the model call. For objects not of class summary.lm, a default print method is used.

See also

summary.lm, print.table.summary.lm

Examples

mdl <- lm(Sepal.Length ~ Sepal.Width, data = iris)
summary(mdl)
#> 
#> Summary Statistics:
#>                  Value      Performance      Measure  Err(Resids)   Metric
#> Observations =     150      R-Squared =     0.013823       MAPE =  0.11751
#> F-Statistic =   2.0744      Adj-R2 =       0.0071593       MAD  =   0.6749
#> Pr(b's=0),% =    0.152      Std.Err.Est =     0.8251       RMSE =  0.81958
#> 
#> Analysis of Variance:
#>                Deg.Frdm  Sum.of.Sqs  Mean.Sum.Sqs  F.statistic  p-value(F)    
#> Regression            1      1.4122       1.41224       2.0744       0.152    
#> Error(Resids)       148    100.7561       0.68078                             
#> Total               149    102.1683                                           
#> 
#> Coefficients:
#>              Coefficient  Std.Error   t-stat  p-value      VIF
#> (Intercept)      6.52622    0.47890  13.6276   <2e-16 ***     
#> Sepal.Width     -0.22336    0.15508  -1.4403    0.152         
#>                                                                      
#> Signif.Levels:  0 ‘***’ 0.001 ‘** ’ 0.01 ‘*  ’ 0.05 ‘.  ’ 0.1 ‘   ’ 1
#>                                                           
#> Summary of   Min     1Q    Median   Mean     3Q      Max  
#> Residuals: -1.556  -0.6333 -0.112  <3e-14  0.5579   2.223 
#>                                                             
#> Call:  lm(formula = Sepal.Length ~ Sepal.Width, data = iris)
summary(mdl, options = c("correlation", "fits"))
#> 
#> Summary Statistics:
#>                  Value      Performance      Measure  Err(Resids)   Metric
#> Observations =     150      R-Squared =     0.013823       MAPE =  0.11751
#> F-Statistic =   2.0744      Adj-R2 =       0.0071593       MAD  =   0.6749
#> Pr(b's=0),% =    0.152      Std.Err.Est =     0.8251       RMSE =  0.81958
#> 
#> Analysis of Variance:
#>                Deg.Frdm  Sum.of.Sqs  Mean.Sum.Sqs  F.statistic  p-value(F)    
#> Regression            1      1.4122       1.41224       2.0744       0.152    
#> Error(Resids)       148    100.7561       0.68078                             
#> Total               149    102.1683                                           
#> 
#> Coefficients:
#>              Coefficient  Std.Error   t-stat  p-value      VIF
#> (Intercept)      6.52622    0.47890  13.6276   <2e-16 ***     
#> Sepal.Width     -0.22336    0.15508  -1.4403    0.152         
#>                                                                      
#> Signif.Levels:  0 ‘***’ 0.001 ‘** ’ 0.01 ‘*  ’ 0.05 ‘.  ’ 0.1 ‘   ’ 1
#>                                                           
#> Summary of   Min     1Q    Median   Mean     3Q      Max  
#> Residuals: -1.556  -0.6333 -0.112  <3e-14  0.5579   2.223 
#>                                                             
#> Call:  lm(formula = Sepal.Length ~ Sepal.Width, data = iris)