如何解决R代码中‘object 'LB' not found’错误？

Hey there! Let's break down what's causing your error and fix up your code properly:

Key Issues in Your Original Code #

• Out-of-order variable definition: You tried to assign LowerBound <- LB before you actually calculated LB—R has no idea what LB is at that point, hence the error.
• Variable name conflict: You used sample to store your total number of iterations, then reused the same name inside the loop to hold your beta sample. This overwrites your original sample = 20000 value, which will break your loop.
• Redundant calculations: LB and UB are fixed values based on your initial mean—no need to recalculate them inside every loop iteration.
• Unused counting variables: You defined yes and no but never updated them in the loop, so they won't track anything useful.

Corrected Code #

# Initialize counting variables
yes <- 0
no <- 0

# Total number of iterations (renamed to avoid conflict)
total_iterations <- 20000

# Population mean (renamed to avoid conflict with R's built-in mean() function)
pop_mean <- 10/12

# Calculate fixed bounds FIRST, before assigning to Lower/UpperBound
LB <- (pop_mean - 2) * sqrt(20/1872)
UB <- (pop_mean + 2) * sqrt(20/1872)
LowerBound <- LB
UpperBound <- UB

# Run the loop
for(i in 1:total_iterations){
  # Use a unique variable name for the beta sample
  beta_sample <- rbeta(10, 10, 2)
  # Calculate the sample mean (assuming you want to check if this falls in bounds)
  sample_mean <- mean(beta_sample)
  
  # Update counting variables
  if(sample_mean >= LowerBound && sample_mean <= UpperBound){
    yes <- yes + 1
  } else {
    no <- no + 1
  }
}

# Optional: Print results to verify
cat("Number of samples within bounds:", yes, "\n")
cat("Number of samples outside bounds:", no, "\n")

What Changed? #

1. Fixed variable order: Now LB and UB are calculated first, then assigned to LowerBound and UpperBound—no more "object not found" error.
2. Renamed conflicting variables: Used total_iterations instead of sample for the loop count, and pop_mean instead of mean to avoid clashing with R's built-in mean() function.
3. Moved bound calculations outside the loop: Saves unnecessary computation since these values don't change across iterations.
4. Added logic to update yes/no: Assumes you want to check if the sample mean falls within your bounds—adjust this condition if your actual goal is different!

内容的提问来源于stack exchange，提问作者Nisha