Winsorize函数：`[.data.frame`（x，顺序（x，na.last=na.last，递减=递减））中出错：选择了未定义的列

我想winsorize我的数据，如下所示（总共134个观察值）：

---

为了使用

DescTools

软件包中的

winsorize

函数，我创建了一个变量

rev

的单一数值向量，只需使用

select

函数：

rev_vector您就快到了，只是您为分位数选择了限制性probs。您的向量已经有相当多的相等的变量它的边缘值。它以前可能已经被winsorized了吗

library(DescTools)

x <-  c(0.66, 2.8, 87.75, 6.89, 134.73, 0.09, 22.78, 1.36, 
        5.48, 0.7, 0.79, 0.35, 31.37, 0.55, 0.94, 0.06, 12.36, 13.58, 
        7.95, 0.29, 7.8, 0.39, 73.55, 0.09, 23.07, 0.27, 0.32, 0.08, 
        0.05, 0.41, 29.47, 0.66, 20.91, 0.67, 0.05, 1.39, 0.17, 0.14, 
        1.79, 0.05, 2.52, 3.68, 0.24, 0.09, 109.65, 8.43, 0.2, 0.17, 
        35.93, 3.05, 0.07, 0.05, 0.82, 0.57, 26.21, 0.28, 0.05, 5.72, 
        6.12, 4.09, 0.05, 0.22, 134.73, 94.43, 41.35, 0.2, 17.32, 5.63, 
        3.25, 0.12, 0.05, 0.07, 10.89, 3.79, 1.89, 134.73, 9.98, 10.58, 
        54.98, 134.73, 15.55, 15.21, 5.93, 42.65, 1.59, 3, 11.19, 6.1, 
        0.08, 134.73, 31.37, 17.74, 20.92, 6.46, 3.18, 0.05, 0.81, 9.15, 
        29.47, 0.05, 1.34, 7.97, 109.65, 28.45, 35.93, 0.38, 0.65, 134.73, 
        9.44, 8.66, 5.3, 11.83, 20.06, 29.55, 1.15, 2.32, 46.14, 134.73, 
        9.98, 10.58, 11.05, 54.98, 134.73, 15.55, 15.21, 5.93, 1.59, 
        1.03, 3, 11.19, 6.1)

使用
Desc（）

Desc(Winsorize(x))

# -----------------------------------------------------    
# Winsorize(x) (numeric)
#
#  length       n    NAs  unique     0s   mean  meanCI
#     131     131      0      95      0  19.73   13.53
#          100.0%   0.0%           0.0%          25.92
#                                                     
#     .05     .10    .25  median    .75    .90     .95
#    0.05    0.08   0.48    5.48  17.53  54.98  134.73
#                                                     
#   range      sd  vcoef     mad    IQR   skew    kurt
#  134.68   35.84   1.82    7.87  17.05   2.35    4.42
#                                                     
# lowest : 0.05 (9), 0.06, 0.07 (2), 0.08 (2), 0.09 (3)
# highest: 73.55, 87.75, 94.43, 109.65 (2), 134.73 (8)

你看，你有9倍于0.05的值和8倍于134.73的值。因此，probs为0.05和0.95的分位数与极值相同，winsorized向量与原始向量相同

quantile(x=x, probs=c(0.05, 0.95))
#    5%    95% 
#  0.05 134.73 

只需将probs增加到c（0.1,0.9），您就会看到效果

PS:
Winsorize（）
需要一个向量作为参数，并且无法处理data.frames。（帮助文件中也介绍了这一点…）

PPS：一个可复制的示例将有助于…；-）
谢谢！我现在可以winsorize我的向量了！按照您的建议：rev_wins1如果答案令人满意地涵盖了您问题的所有方面，您可以接受它…；-）
library(DescTools)

x <-  c(0.66, 2.8, 87.75, 6.89, 134.73, 0.09, 22.78, 1.36, 
        5.48, 0.7, 0.79, 0.35, 31.37, 0.55, 0.94, 0.06, 12.36, 13.58, 
        7.95, 0.29, 7.8, 0.39, 73.55, 0.09, 23.07, 0.27, 0.32, 0.08, 
        0.05, 0.41, 29.47, 0.66, 20.91, 0.67, 0.05, 1.39, 0.17, 0.14, 
        1.79, 0.05, 2.52, 3.68, 0.24, 0.09, 109.65, 8.43, 0.2, 0.17, 
        35.93, 3.05, 0.07, 0.05, 0.82, 0.57, 26.21, 0.28, 0.05, 5.72, 
        6.12, 4.09, 0.05, 0.22, 134.73, 94.43, 41.35, 0.2, 17.32, 5.63, 
        3.25, 0.12, 0.05, 0.07, 10.89, 3.79, 1.89, 134.73, 9.98, 10.58, 
        54.98, 134.73, 15.55, 15.21, 5.93, 42.65, 1.59, 3, 11.19, 6.1, 
        0.08, 134.73, 31.37, 17.74, 20.92, 6.46, 3.18, 0.05, 0.81, 9.15, 
        29.47, 0.05, 1.34, 7.97, 109.65, 28.45, 35.93, 0.38, 0.65, 134.73, 
        9.44, 8.66, 5.3, 11.83, 20.06, 29.55, 1.15, 2.32, 46.14, 134.73, 
        9.98, 10.58, 11.05, 54.98, 134.73, 15.55, 15.21, 5.93, 1.59, 
        1.03, 3, 11.19, 6.1)

summary(Winsorize(x))
# Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
# 0.05    0.48    5.48   19.73   17.53  134.73 

Desc(Winsorize(x))

# -----------------------------------------------------    
# Winsorize(x) (numeric)
#
#  length       n    NAs  unique     0s   mean  meanCI
#     131     131      0      95      0  19.73   13.53
#          100.0%   0.0%           0.0%          25.92
#                                                     
#     .05     .10    .25  median    .75    .90     .95
#    0.05    0.08   0.48    5.48  17.53  54.98  134.73
#                                                     
#   range      sd  vcoef     mad    IQR   skew    kurt
#  134.68   35.84   1.82    7.87  17.05   2.35    4.42
#                                                     
# lowest : 0.05 (9), 0.06, 0.07 (2), 0.08 (2), 0.09 (3)
# highest: 73.55, 87.75, 94.43, 109.65 (2), 134.73 (8)

quantile(x=x, probs=c(0.05, 0.95))
#    5%    95% 
#  0.05 134.73