Note 2 - Stats for Clinical Trials Math 150 Jo Hardin Model...

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Stats for Clinical Trials, Math 150 Jo Hardin Model building Consider the HERS data described in your book (page 30); variable description also given on the book website http://www.epibiostat.ucsf.edu/biostat/vgsm/data/hersdata.codebook. txt For now, we will try to predict whether the individuals had a medical condition, medcond . We will use the variables age , weight , diabetes and drinkany > HERS <- read.table("HERS.txt", sep="\t", header=T, na.strings=".") > attach(HERS) Forward > summary(glm(medcond ~ age, family="binomial"))$coef Estimate Std. Error z value Pr(>|z|) (Intercept) -1.60404454 0.400644718 -4.003658 6.237044e-05 age 0.01619155 0.005965348 2.714267 6.642259e-03 Null deviance: 3647.4 on 2762 degrees of freedom Residual deviance: 3640.0 on 2761 degrees of freedom AIC: 3644 > summary(glm(medcond ~ age + weight, family="binomial"))$coef Estimate Std. Error z value Pr(>|z|) (Intercept) -2.169846602 0.496466231 -4.370582 1.239155e-05 age 0.018926204 0.006132171 3.086379 2.026105e-03 weight 0.005279148 0.002742218 1.925138 5.421212e-02 Null deviance: 3645.5 on 2760 degrees of freedom Residual deviance: 3634.3 on 2758 degrees of freedom AIC: 3640.3 > summary(glm(medcond ~ age+ diabetes, family="binomial"))$coef Estimate Std. Error z value Pr(>|z|) (Intercept) -1.89085342 0.407555871 -4.639495 3.492616e-06 age 0.01848156 0.006027953 3.065977 2.169602e-03
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