Main page: https://www.rdocumentation.org/packages/datasets/versions/3.6.2/topics/infert
head(infert)## education age parity induced case spontaneous stratum pooled.stratum
## 1 0-5yrs 26 6 1 1 2 1 3
## 2 0-5yrs 42 1 1 1 0 2 1
## 3 0-5yrs 39 6 2 1 0 3 4
## 4 0-5yrs 34 4 2 1 0 4 2
## 5 6-11yrs 35 3 1 1 1 5 32
## 6 6-11yrs 36 4 2 1 1 6 36require(stats)
model1 <- glm(case ~ spontaneous+induced, data = infert, family = binomial())
summary(model1)##
## Call:
## glm(formula = case ~ spontaneous + induced, family = binomial(),
## data = infert)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6678 -0.8360 -0.5772 0.9030 1.9362
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7079 0.2677 -6.380 1.78e-10 ***
## spontaneous 1.1972 0.2116 5.657 1.54e-08 ***
## induced 0.4181 0.2056 2.033 0.042 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 316.17 on 247 degrees of freedom
## Residual deviance: 279.61 on 245 degrees of freedom
## AIC: 285.61
##
## Number of Fisher Scoring iterations: 4## adjusted for other potential confounders:
summary(model2 <- glm(case ~ age+parity+education+spontaneous+induced,
data = infert, family = binomial()))##
## Call:
## glm(formula = case ~ age + parity + education + spontaneous +
## induced, family = binomial(), data = infert)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7603 -0.8162 -0.4956 0.8349 2.6536
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.14924 1.41220 -0.814 0.4158
## age 0.03958 0.03120 1.269 0.2046
## parity -0.82828 0.19649 -4.215 2.49e-05 ***
## education6-11yrs -1.04424 0.79255 -1.318 0.1876
## education12+ yrs -1.40321 0.83416 -1.682 0.0925 .
## spontaneous 2.04591 0.31016 6.596 4.21e-11 ***
## induced 1.28876 0.30146 4.275 1.91e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 316.17 on 247 degrees of freedom
## Residual deviance: 257.80 on 241 degrees of freedom
## AIC: 271.8
##
## Number of Fisher Scoring iterations: 4## Really should be analysed by conditional logistic regression
## which is in the survival package
if(require(survival)){
model3 <- clogit(case ~ spontaneous+induced+strata(stratum), data = infert)
print(summary(model3))
detach() # survival (conflicts)
}## Loading required package: survival## Call:
## coxph(formula = Surv(rep(1, 248L), case) ~ spontaneous + induced +
## strata(stratum), data = infert, method = "exact")
##
## n= 248, number of events= 83
##
## coef exp(coef) se(coef) z Pr(>|z|)
## spontaneous 1.9859 7.2854 0.3524 5.635 1.75e-08 ***
## induced 1.4090 4.0919 0.3607 3.906 9.38e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## spontaneous 7.285 0.1373 3.651 14.536
## induced 4.092 0.2444 2.018 8.298
##
## Concordance= 0.776 (se = 0.044 )
## Likelihood ratio test= 53.15 on 2 df, p=3e-12
## Wald test = 31.84 on 2 df, p=1e-07
## Score (logrank) test = 48.44 on 2 df, p=3e-11