## Linear Regression Model
##
## ols(formula = mpg ~ cyl + vs, data = mtcars, model = TRUE, x = TRUE,
## y = TRUE)
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 32 LR chi2 41.70 R2 0.728
## sigma3.2481 d.f. 2 R2 adj 0.710
## d.f. 29 Pr(> chi2) 0.0000 g 5.641
##
## Residuals
##
## Min 1Q Median 3Q Max
## -4.92324 -1.95291 -0.08097 1.31867 7.57676
##
##
## Coef S.E. t Pr(>|t|)
## Intercept 39.6250 4.2246 9.38 <0.0001
## cyl -3.0907 0.5581 -5.54 <0.0001
## vs -0.9391 1.9775 -0.47 0.6384
##
## Logistic Regression Model
##
## lrm(formula = vs ~ mpg, data = mtcars, model = TRUE, x = TRUE,
## y = TRUE)
##
## Model Likelihood Discrimination Rank Discrim.
## Ratio Test Indexes Indexes
## Obs 32 LR chi2 18.33 R2 0.584 C 0.911
## 0 18 d.f. 1 g 2.925 Dxy 0.821
## 1 14 Pr(> chi2) <0.0001 gr 18.637 gamma 0.825
## max |deriv| 2e-05 gp 0.399 tau-a 0.417
## Brier 0.130
##
## Coef S.E. Wald Z Pr(>|Z|)
## Intercept -8.8331 3.1623 -2.79 0.0052
## mpg 0.4304 0.1584 2.72 0.0066
##
##
## Call: glm(formula = vs ~ mpg, family = binomial(link = "logit"), data = mtcars,
## model = TRUE, x = TRUE, y = TRUE)
##
## Coefficients:
## (Intercept) mpg
## -8.8331 0.4304
##
## Degrees of Freedom: 31 Total (i.e. Null); 30 Residual
## Null Deviance: 43.86
## Residual Deviance: 25.53 AIC: 29.53
## Cox Proportional Hazards Model
##
## cph(formula = Surv(mpg, vs) ~ am + gear, data = mtcars, model = TRUE,
## x = TRUE, y = TRUE)
##
## Model Tests Discrimination
## Indexes
## Obs 32 LR chi2 9.35 R2 0.311
## Events 14 d.f. 2 Dxy 0.514
## Center -2.0911 Pr(> chi2) 0.0093 g 1.275
## Score chi2 10.16 gr 3.579
## Pr(> chi2) 0.0062
##
## Coef S.E. Wald Z Pr(>|Z|)
## am -2.0159 0.9041 -2.23 0.0258
## gear -0.3450 0.6999 -0.49 0.6221
##
## Call:
## coxph(formula = Surv(mpg, vs) ~ am + gear, data = mtcars, model = TRUE,
## x = TRUE, y = TRUE)
##
## coef exp(coef) se(coef) z p
## am -2.0159 0.1332 0.9041 -2.230 0.0258
## gear -0.3450 0.7082 0.6999 -0.493 0.6221
##
## Likelihood ratio test=9.35 on 2 df, p=0.009328
## n= 32, number of events= 14