Extended Model Summary

Extended summary information for a model fit. Currently linear and logistic regression models are supported.

info(x)

Arguments

x

an object of type lm or glm. If glm, then family must be binomial.

Value

A list produced by info.lm or info.glm.

Details

info is a generic S3 function providing detailed model information. The goal is to provide more extensive information than currently produced by summary. See the related methods for details.

See also

info.lm, info.glm.

Examples

#######################
# multiple regression #
#######################
fit <- lm(mpg ~ hp + wt + accel + origin, data = auto_mpg)
info(fit)
#> MULTIPLE REGRESSION SUMMARY 
#> Model: mpg ~ hp + wt + accel + origin
#> Data : auto_mpg
#> N    : 388
#> 
#> Fit Indices 
#>  R.Squared Adj.R.Squared  AIC RMSE  MAE
#>      0.718         0.715 2217 4.14 3.15
#> 
#> Omnibus Test 
#> F(5,382) = 194.921, p < <2e-16  *** 
#> 
#> Anova Table (type III tests) 
#>               Sum Sq  DF F value Pr(>F)    
#> (Intercept) 5363.242   1 308.296 <0.001 ***
#> hp           217.649   1  12.511 <0.001 ***
#> wt          1007.306   1  57.903 <0.001 ***
#> accel          0.952   1   0.055  0.815    
#> origin       307.094   2   8.826 <0.001 ***
#> Residuals   6645.428 382                   
#> 
#> Regression Coefficients 
#>                    B      B*       SE      t Pr(>|t|)    
#> (Intercept) 43.26563  0.0000 2.464104 17.558 5.04e-51 ***
#> hp          -0.05632 -0.2783 0.015922 -3.537 4.54e-04 ***
#> wt          -0.00477 -0.5180 0.000627 -7.609 2.17e-13 ***
#> accel       -0.02862 -0.0101 0.122391 -0.234 8.15e-01    
#> origin2      0.96892  0.0472 0.646763  1.498 1.35e-01    
#> origin3      2.76148  0.1419 0.659001  4.190 3.46e-05 ***

#######################
# logistic regression #
#######################
fit2 <- glm(caesarian ~ age + bp + delivery.time, family = binomial, data = caesarian)
info(fit2)
#> LOGISTIC REGRESSION SUMMARY 
#> Formula: caesarian ~ age + bp + delivery.time
#> Data   : caesarian
#> N      : 80
#> 
#> Predicted category: yes
#> 
#> Omnibus Test 
#> Chi-square(5) = 15.2455, p = 0.009363 **
#> 
#> Fit Measures 
#> Stukel's GOF Test: Chi-square(2) = 0.0663, p < 0.9674
#> Tjur's Psuedo-R.squared: 0.1769
#> AIC: 105.8512 
#> 
#> Anova Table (type III tests) 
#>               LR Chisq DF Pr(>Chisq)   
#> age             0.6465  1   0.421353   
#> bp             12.1255  2   0.002328 **
#> delivery.time   6.9476  2   0.030999  *
#> 
#> Logistic Regression Coefficients 
#>                               B      SE       z Pr(>|z|)   
#> (Intercept)             1.02083 1.49393  0.6833 0.494407   
#> age                     0.03944 0.04928  0.8003 0.423531   
#> bpnormal               -2.05065 0.71483 -2.8687 0.004122 **
#> bphigh                 -0.46110 0.75158 -0.6135 0.539542   
#> delivery.timepremature -1.29707 0.67785 -1.9135 0.055685   
#> delivery.timelatecomer -1.53961 0.70396 -2.1871 0.028737  *
#> 
#> Odds Ratios (with 95% Confidence Intervals) 
#>                        Odds Ratio    2.5%  97.5%
#> age                        1.0402 0.94482 1.1489
#> bpnormal                   0.1287 0.02790 0.4775
#> bphigh                     0.6306 0.13739 2.7408
#> delivery.timepremature     0.2733 0.06659 0.9889
#> delivery.timelatecomer     0.2145 0.04907 0.8064