所以我有一個資料框列，看起來像：
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依此類推，分位數看起來像
0% 25% 50% 75% 100%
-4.268446 -3.845898 -3.703559 -3.471152 -1.423046
并且意思是
[1] -3.624417
而標準差是
[1] 0.317673
我想標準化這個數字，平均值為 100，最佳值（最大值）在 100 以上。任何值都應該比平均值好/差 100-x%。
因此，如果值為 110，則應比均值大 10%，如果為 90，則應比均值低 10%。
我已經嘗試了我通常做的方式來標準化這種方式
值/平均值 * 100
但這使得更差的值被估價更高。
uj5u.com熱心網友回復：
嘗試使用正態分布。
# your data
head(vec)
[1] -3.630924 -3.465714 -3.239579 -3.089863 -3.842719 -3.636238
mean(rnorm( 1000, 100-(mean(vec)), 5) vec)
[1] 100.0151
uj5u.com熱心網友回復：
new_vec = (vec - mean(vec) * 100 100)
uj5u.com熱心網友回復：
首先創建 z-scores (mean=0, sd=1)：
z <- scale(x)
然后轉換為mean=100, sd=100
xmod <- z * 100 100
mean(xmod)
# [1] 100
sd(xmod)
# [1] 100
summary(xmod)
# V1
# Min. :-99.46
# 1st Qu.: 32.31
# Median : 83.33
# Mean :100.00
# 3rd Qu.:150.84
# Max. :630.09
轉載請註明出處，本文鏈接：https://www.uj5u.com/qukuanlian/378682.html
標籤：r
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