Logistisk regression

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u n i v e r s i t y o f c o p e n h a g e n Simpel logistisk regression i R d e p a r t m e n t o f b i o s t a t i s t i c s Logistisk regression i R laves vha funktionen glm: Estimation > glm1 summary(glm1) Call: glm(formula = chdI ~ mand, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max -0.7674 -0.7674 -0.5586 -0.5586 1.9672 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.7789 0.1060 -16.780 < 2e-16 *** mand 0.7070 0.1394 5.073 3.92e-07 *** -- (Dispersion parameter for binomial family taken to be 1) Null deviance: 1351.2 on 1362 degrees of freedom Residual deviance: 1324.9 on 1361 degrees of freedom AIC: 1328.9 Number of Fisher Scoring iterations: 4 16 / 42
u n i v e r s i t y o f c o p e n h a g e n Test af kønseffekten d e p a r t m e n t o f b i o s t a t i s t i c s Nulhypotesen er at sandsynligheden for CHD ikke afhænger af køn: Dette kan testes med et Wald-test: z = estimat SE H 0 : b = 0. ∼ N (0, 1). Her er b = 0.7070, SE = 0.1394, z=5.073, p < .0001. Bemærk at z 2 = 5.073 2 = 25.74 ∼ χ 2 (df = 1) stemmer overens med standard χ 2 -testet i 2×2-tabellen: 25.62 (df = 1). Der er en stærkt signifikant forskel på kønnene mht risikoen for CHD. 17 / 42
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Logistisk regression

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