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--- logistic_regression [2024/12/11 10:46] – [Odds ratio] hkimscil
+++ logistic_regression [2024/12/11 11:57] (current) – [exercise: binary IV] hkimscil
@@ Line 557: / Line 557: @@
 </code>
 마리화나의 사용경험에서 남성이 여성보다 큰 승산이 있다고 판단되었다 (Odds ratio (OR) = 1.44; 95% CI = 1.13, 1.86; p = .004). 남성은 여성보다 약 44% 더 사용경험을 할 승산을 보였다 (OR = 1.44).
+====== exercise: binary IV ======
+<code>
+########################################
+# exercise
+head(df)
+table(df)
+# base 바꾸기
+df.norm <- df %>% mutate(gene = relevel(gene, ref = "norm"))
+df.mut <- df %>% mutate(gene = relevel(gene, ref = "mutant"))
+logm.cancer.gene.1 <- glm(cancer ~ gene, family = binomial, data = df.norm)
+summary(logm.cancer.gene.1)
+a <- logm.cancer.gene.1$coefficients[1]
+b <- logm.cancer.gene.1$coefficients[2]
+a
+b
+a+b
+# when b = 0; 즉, mutant = 0 일 때
+# log(odds.norm) = a 이므로
+# odds.norm = e^a
+exp(a)
+# 확인
+odds(p.can.norm)
+# odds.mut = e^(a+b)
+exp(a+b)
+odds(p.can.mut)
+# odds.ratio = e^(b)
+exp(b)
+odds.ratio(p.can.mut, p.can.norm)
+logm.cancer.gene.2 <- glm(cancer ~ gene, family = binomial, data = df.mut)
+summary(logm.cancer.gene.2)
+a <- logm.cancer.gene.2$coefficients[1]
+b <- logm.cancer.gene.2$coefficients[2]
+a
+b
+a+b
+# when b = 0; 즉, mutant = 0 일 때
+# log(odds.norm) = a 이므로
+# odds.norm = e^a
+exp(a)
+# 확인
+odds(p.can.mut)
+# odds.mut = e^(a+b)
+exp(a+b)
+odds(p.can.norm)
+# odds.ratio = e^(b)
+exp(b)
+odds.ratio(p.can.norm, p.can.mut)
+</code>
 ====== X: numeric variable ======
 <code>