> fit <- glm(count ~ group + MI, family= poisson, data= phs) > summary(fit) Call: glm(formula = count ~ group + MI, family = poisson, data = phs) Deviance Residuals: 1 2 3 4 3.3610 -3.7072 -0.4078 0.4072 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 4.9868895 0.0588072 84.80 <2e-16 groupAspirin 0.0002718 0.0134623 0.02 0.984 MINo 4.3084830 0.0588123 73.26 <2e-16 (Dispersion parameter for poisson family taken to be 1) Null deviance: 27507.580 on 3 degrees of freedom Residual deviance: 25.372 on 1 degrees of freedom AIC: 67.203 Number of Fisher Scoring iterations: 4 > anova(fit) Analysis of Deviance Table Model: poisson, link: log Response: count Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev NULL 3 27507.6 group 1 0 2 27507.6 MI 1 27482 1 25.4 > confint(fit) Waiting for profiling to be done... 2.5 % 97.5 % (Intercept) 4.86942613 5.10002996 groupAspirin -0.02611413 0.02665786 MINo 4.19533052 4.42595448 > fitF <- glm(count ~ group * MI, family= poisson, data= phs) > summary(fitF) Call: glm(formula = count ~ group * MI, family = poisson, data = phs) Deviance Residuals: [1] 0 0 0 0 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 5.24175 0.07274 72.062 < 2e-16 groupAspirin -0.59736 0.12209 -4.893 9.95e-07 MINo 4.04971 0.07337 55.195 < 2e-16 groupAspirin:MINo 0.60544 0.12284 4.929 8.28e-07 (Dispersion parameter for poisson family taken to be 1) Null deviance: 2.7508e+04 on 3 degrees of freedom Residual deviance: 3.5376e-12 on 0 degrees of freedom AIC: 43.831 Number of Fisher Scoring iterations: 2 > anova(fitF) Analysis of Deviance Table Model: poisson, link: log Response: count Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev NULL 3 27507.6 group 1 0.0 2 27507.6 MI 1 27482.2 1 25.4 group:MI 1 25.4 0 0.0 > confint(fitF) Waiting for profiling to be done... 2.5 % 97.5 % (Intercept) 5.0957066 5.3810003 groupAspirin -0.8397232 -0.3605429 MINo 3.9091624 4.1969315 groupAspirin:MINo 0.3671257 0.8492364