[英]How to generate bootstrapped confidence intervals for categorical data in R?
我正在嘗試為 R 中的分類數據的正態分布數據構建簡單的 95% 引導置信區間。 R boot.ci
中的常規Bootstrap 置信區間似乎不適用於分類變量
df <- data.frame(
dose = rep(c("10","20","30","40","50"),times= 10),
count = rnorm(50, 1, 0.1)
)
df$dose <- as.factor(df$dose)
df <- data.frame(
dose = rep(c(10,20,30,40,50),times= 10)
)
df$count <- rpois(50, df$dose)
df$dose <- factor(df$dose)
#I would consider if I can keep dose as numeric
fit <- glm(count ~ dose, data = df, family = "poisson")
summary(fit)
#bootstrap predictions
library(boot)
set.seed(42)
myboot <- boot(df, function(df, i) {
fit <- glm(count ~ dose, data = df[i,])
predict(fit, newdata = data.frame(dose = unique(df$dose)), type = "response")
}, 1e3)
lapply(seq_along(myboot$t0), function(i) boot.ci(myboot, index = i))
# [[1]]
# BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
# Based on 1000 bootstrap replicates
#
# CALL :
# boot.ci(boot.out = myboot, index = i)
#
# Intervals :
# Level Normal Basic
# 95% ( 9.57, 13.93 ) ( 9.40, 13.90 )
#
# Level Percentile BCa
# 95% ( 9.50, 14.00 ) ( 9.54, 14.00 )
# Calculations and Intervals on Original Scale
#
# ...
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