1 개요[ | ]
- R glm()
- "Generalized Linear Model"
R
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library("MASS")
str(menarche)
summary(glm(cbind(Menarche, Total-Menarche) ~ Age, family=binomial, data=menarche))
'data.frame': 25 obs. of 3 variables: $ Age : num 9.21 10.21 10.58 10.83 11.08 ... $ Total : num 376 200 93 120 90 88 105 111 100 93 ... $ Menarche: num 0 0 0 2 2 5 10 17 16 29 ...
Call: glm(formula = cbind(Menarche, Total - Menarche) ~ Age, family = binomial, data = menarche) Deviance Residuals: Min 1Q Median 3Q Max -2.0363 -0.9953 -0.4900 0.7780 1.3675 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -21.22639 0.77068 -27.54 <2e-16 *** Age 1.63197 0.05895 27.68 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 3693.884 on 24 degrees of freedom Residual deviance: 26.703 on 23 degrees of freedom AIC: 114.76 Number of Fisher Scoring iterations: 4
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counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
z <- glm(counts ~ outcome + treatment, family = poisson())
summary(z)
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anova(z)
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2 같이 보기[ | ]
- R anova.glm()
- R summary.glm()
- R lm()
- R polr()
- R loglin()
- R loglm()
- R esoph()
- R infert()
- R predict.glm()
- R 로지스틱 회귀분석
3 참고[ | ]
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