我try 使用相对风险回归来计算风险比，但我不知道如何执行它. 我使用的logbin函数与glm二项式，但我只获得系数
fit.glm <- glm(Reparto_OUT ~ t0_IOT , data = db,family=binomial())
install.packages("logbin")
library(logbin)
fit.logbin <- logbin(formula(fit.glm), data = db)
summary(fit.logbin)
从这个输出中，我不知道如何继续获得95%可信区间的RR
> summary(fit.logbin)
Call:
logbin(formula = formula(fit.glm), data = db)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8761 -0.8388 -0.8388 0.8729 1.5591
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.488334 0.122824 -12.118 <2e-16 ***
t0_FIO1 0.012995 0.001363 9.531 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Null deviance: 529.80 on 382 degrees of freedom
Residual deviance: 434.77 on 381 degrees of freedom
(1 osservazione eliminata a causa di un valore mancante)
AIC: 438.77
AIC_c: 438.80
Number of iterations: 82 (best: 61)
我的数据:
db$Reparto_OUT
[1] 1 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 0 0 0
[49] 0 0 0 0 1 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 1 1 0 1 1 1 0 1 0 1 0 0 1 1
[97] 0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 1 0 1 0 0 1 1 1 0 1 1 1 0 1 0 0 1 0 0 1 0 0 0 0 1 1 1 1 0 1 0 0 1
[145] 1 1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 1 1 1 1 1 0 0 1 1 0 1 0 0 1 1 1 1 1 1 1 0 1 1 0 0 0
[193] 0 1 0 1 1 1 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 1 1 0 0 1 1 1 0 1 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0
[241] 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 0 1 1 1 1 0 1 1 1 1 0 0 0 1 1 1 0 0 0 0 1
[289] 0 1 0 1 1 1 0 1 1 1 1 1 0 1 0 1 1 0 1 0 1 0 1 0 1 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0
[337] 0 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 1 1 0 0 0 1 1 1 1 1
db$t0_FIO1
[1] 100 21 21 21 21 100 21 21 21 21 21 100 100 100 100 21 21 21 21 21 21 21 21 21
[25] 21 21 21 21 21 21 21 21 21 100 21 21 100 100 21 21 21 100 21 21 21 21 21 21
[49] 21 21 21 21 100 21 21 60 21 21 40 21 21 100 21 21 21 21 21 21 21 21 100 21
[73] 21 21 21 21 100 21 21 21 21 21 100 40 21 21 100 40 21 100 21 21 100 21 100 21
[97] 100 21 21 100 21 21 40 21 21 40 21 40 100 21 21 21 100 40 100 21 40 100 100 21
[121] 40 100 60 21 100 21 100 100 21 100 60 21 21 40 60 40 100 100 100 40 100 21 21 100
[145] 100 100 100 100 21 21 50 21 21 21 21 21 21 60 80 21 21 21 21 100 100 100 100 100
[169] 21 21 21 21 21 21 100 21 100 21 21 80 100 100 100 100 100 50 21 100 100 21 21 21
[193] 21 21 21 21 21 100 21 21 21 NA 21 21 100 100 70 24 21 60 21 40 100 100 80 28
[217] 100 100 21 21 50 21 100 100 100 31 21 21 21 21 21 21 100 24 21 21 21 100 21 21
[241] 50 21 100 100 100 21 100 100 50 21 100 100 40 100 50 21 100 21 21 21 100 31 21 40
[265] 21 80 100 50 21 100 100 100 40 21 40 60 21 21 21 21 21 100 21 85 21 100 100 60
[289] 21 100 100 100 100 100 21 50 40 50 50 21 40 60 21 50 40 21 21 21 100 21 100 100
[313] 50 21 100 21 21 100 21 100 21 21 100 21 21 21 21 21 21 21 100 21 60 21 21 21
[337] 21 100 21 21 35 21 21 21 70 100 100 80 100 80 21 21 28 21 40 21 60 21 40 21
[361] 21 21 21 21 21 21 80 21 80 50 21 21 21 21 90 80 100 21 21 100 100 21 35 100
