R 帮助我改进我的引导

考虑以下代码：

require(Hmisc)
num.boots <- 10
data <- rchisq(500, df = 5) #generate fake data

#create bins
binx <- cut(data, breaks = 10)
binx <- levels(binx)
binx <- sub("^.*\\,", "", binx)
binx <- as.numeric(substr(binx, 1, nchar(binx) - 1))

#pre-allocate a matrix to be filled with samples
output <- matrix(NA, nrow = num.boots, ncol = length(binx)) 

#do random sampling from the vector and calculate percent
# of values equal or smaller to the bin number (i)
for (i in 1:num.boots) {
    walk.pair.sample <- sample(data, size = length(data), replace = TRUE)
    data.cut <- cut2(x = walk.pair.sample, cuts = binx)
    data.cut <- table(data.cut)/sum(table(data.cut))
    output[i, ] <- data.cut
}

#do some plotting
plot(1:10, seq(0, max(output), length.out = nrow(output)), type = "n", xlab = "", ylab = "")

for (i in 1:nrow(output)) {
    lines(1:10, output[i, 1:nrow(output)])
}

#mean values by columns
output.mean <- apply(output, 2, mean)
lines(output.mean, col="red", lwd = 3)
legend(x = 8, y = 0.25, legend = "mean", col = "red", lty = "solid", lwd = 3)

然而，我设法产生了一个错误，我仍在努力如何删除它

> x <- boot(data = data, statistic = bootstrapDistances2, R = 100, binx = binx)
Error in t.star[r, ] <- statistic(data, i[r, ], ...) : 
  number of items to replace is not a multiple of replacement length

---

>x来自
引导功能的帮助文件：

在所有其他情况下，统计必须至少包含两个参数。传递的第一个参数始终是原始数据。第二个是定义引导样本的指数、频率或权重向量

因此，您需要在
bootstrapdistance
函数中添加第二个参数，告诉它选择了哪些数据元素：

bootstrapDistances2 <- function(data, idx, binx) { 
  data.cut <- cut2(x = data[idx], cuts = binx) 
  data.cut <- table(data.cut)/sum(table(data.cut)) 
  return(data.cut) 
} 

bootstrapDistances2回答得好，Aniko

另外，“boot”的帮助页面声明bootstrap statistic函数可能返回一个向量，而不仅仅是一个标量。
很感谢您的尝试，但是我得到了一个错误：x在重新启动R会话后，一切正常。啊？谢谢你的合作。现在我看到了，上面写着“统计数据”！
> x <- boot(data = data, statistic = bootstrapDistances2, R = 100, binx = binx)
Error in t.star[r, ] <- statistic(data, i[r, ], ...) : 
  number of items to replace is not a multiple of replacement length

bootstrapDistances2 <- function(data, idx, binx) { 
  data.cut <- cut2(x = data[idx], cuts = binx) 
  data.cut <- table(data.cut)/sum(table(data.cut)) 
  return(data.cut) 
} 

x <- boot(data = data, statistic = bootstrapDistances2, R = 100, binx=binx)
x

ORDINARY NONPARAMETRIC BOOTSTRAP


Call:
boot(data = data, statistic = bootstrapDistances2, R = 100, binx = binx)


Bootstrap Statistics :
     original   bias    std. error
t1*     0.208  0.00134 0.017342783
t2*     0.322  0.00062 0.021700803
t3*     0.190 -0.00034 0.018873433
t4*     0.136 -0.00116 0.016206197
t5*     0.078 -0.00120 0.011413265
t6*     0.036  0.00070 0.008510837
t7*     0.016  0.00074 0.005816417
t8*     0.006  0.00024 0.003654581
t9*     0.000  0.00000 0.000000000
t10*    0.008 -0.00094 0.003368961