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你好 Stack Overflow 的人，我花了一段时间寻找解决我的问题的方法，但没有找到任何东西，所以我想我会发布。

基本上我有一个按字母顺序列出的 196 个国家的数据集。其中一个变量根据国家所在的地区分配一个 1-10 的数字。例如东欧 = 1、西欧 = 2、中东 = 3、南美 = 4 等等。

这是数据集的可视化表示：

国家名称-----国家地区------婴儿死亡率

阿富汗------------3------------180

阿根廷---------------4------------65

法国-------------------2------------------------12

德国---------------2-------------10

波兰-----------------1------------------------16

我需要做的是将这 10 个区域分成各自的虚拟变量，以便我通过多元回归对它们进行运行，以确定它们对婴儿死亡率的个体影响。

我想知道创建虚拟变量（1 = 东欧，0 = 其他等）的必要代码是什么，然后如何在单独和多元回归中测试它们的效果。

抱歉，如果这看起来很简单或愚蠢的问题，我对使用 R 相当陌生。

我在这里先向您的帮助表示感谢。

编辑：这是要求的 dput 输出：

structure(list(Country.Name = structure(c(1L, 2L, 3L, 4L, 5L, 
6L, 11L, 7L, 9L, 10L, 12L, 13L, 14L, 8L, 15L, 17L, 20L, 21L, 
22L, 23L, 24L, 18L, 156L, 25L, 26L, 120L, 28L, 16L, 29L, 30L, 
31L, 32L, 33L, 160L, 34L, 35L, 36L, 170L, 37L, 38L, 39L, 40L, 
41L, 43L, 44L, 45L, 46L, 19L, 47L, 49L, 50L, 51L, 53L, 54L, 57L,
55L, 56L, 58L, 59L, 60L, 48L, 61L, 63L, 62L, 64L, 65L, 88L, 66L,
67L, 68L, 69L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L,
81L, 82L, 42L, 83L, 84L, 86L, 85L, 87L, 89L, 90L, 91L, 92L, 93L,
95L, 96L, 94L, 97L, 98L, 99L, 100L, 101L, 103L, 104L, 105L, 106L, 
107L, 108L, 110L, 111L, 112L, 115L, 116L, 114L, 117L, 118L, 119L, 
130L, 121L, 122L, 123L, 124L, 189L, 125L, 126L, 127L, 128L, 129L, 
113L, 109L, 132L, 131L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 
70L, 174L, 140L, 141L, 142L, 143L, 161L, 162L, 163L, 145L, 146L, 
147L, 148L, 149L, 151L, 152L, 153L, 154L, 191L, 155L, 157L, 158L, 
194L, 159L, 164L, 165L, 166L, 167L, 168L, 169L, 171L, 173L, 175L, 
176L, 177L, 184L, 178L, 179L, 180L, 181L, 182L, 183L, 102L, 52L, 
185L, 172L, 186L, 27L, 187L, 188L, 190L, 144L, 192L, 150L, 193L
), .Label = c("Afghanistan", "Albania", "Algeria", "Andorra", 
"Angola", "Antigua and Barbuda", "Argentina", "Armenia", "Australia", 
"Austria", "Azerbaijan", "Bahamas", "Bahrain", "Bangladesh", 
"Barbados", "Belarus", "Belgium", "Belize", "Benin", "Bhutan", 
"Bolivia", "Bosnia and Herzegovina", "Botswana", "Brazil", "Brunei", 
"Bulgaria", "Burkina Faso", "Burundi", "Cambodia", "Cameroon", 
"Canada", "Cape Verde", "Central African Republic", "Chad", "Chile", 
"China", "Colombia", "Comoros", "Congo", "Congo, Democratic Republic", 
"Costa Rica", "Cote d'Ivoire", "Croatia", "Cuba", "Cyprus", "Czech Republic",  
"Denmark", "Djibouti", "Dominica", "Dominican Republic", "Ecuador", 
"Egypt", "El Salvador", "Equatorial Guinea", "Eritrea", "Estonia", 
"Ethiopia", "Fiji", "Finland", "France", "Gabon", "Gambia", "Georgia", 
"Germany", "Ghana", "Greece", "Grenada", "Guatemala", "Guinea",
"Guinea-Bissau", "Guyana", "Haiti", "Honduras", "Hungary", "Iceland", 
"India", "Indonesia", "Iran", "Iraq", "Ireland", "Israel", "Italy", 
"Jamaica", "Japan", "Jordan", "Kazakhstan", "Kenya", "Kiribati", 
"Korea, North", "Korea, South", "Kuwait", "Kyrgyzstan", "Laos", 
"Latvia", "Lebanon", "Lesotho", "Liberia", "Libya", "Liechtenstein", 
"Lithuania", "Luxembourg", "Macedonia", "Madagascar", "Malawi", 
"Malaysia", "Maldives", "Mali", "Malta", "Marshall Islands", 
"Mauritania", "Mauritius", "Mexico", "Micronesia", "Moldova", 
"Monaco", "Mongolia", "Montenegro", "Morocco", "Mozambique",
"Myanmar", "Namibia", "Nauru", "Nepal", "Netherlands", "New Zealand", 
"Nicaragua", "Niger", "Nigeria", "Norway", "Oman", "Pakistan", 
"Palau", "Panama", "Papua New Guinea", "Paraguay", "Peru", "Philippines", 
"Poland", "Portugal", "Qatar", "Romania", "Russia", "Rwanda", 
"Samoa", "San Marino", "Sao Tome and Principe", "Saudi Arabia", 
"Senegal", "Serbia", "Serbia and Montenegro", "Seychelles", "Sierra Leone", 
"Singapore", "Slovakia", "Slovenia", "Solomon Islands", "Somalia", 
"South Africa", "Spain", "Sri Lanka", "St Kitts and Nevis", "St Lucia", 
"St Vincent and the Grenadines", "Sudan", "Suriname", "Swaziland", 
"Sweden", "Switzerland", "Syria", "Taiwan", "Tajikistan", "Tanzania", 
"Thailand", "Timor-Leste", "Togo", "Tonga", "Trinidad and Tobago", 
"Tunisia", "Turkey", "Turkmenistan", "Tuvalu", "Uganda", "Ukraine", 
"United Arab Emirates", "United Kingdom", "United States", "Uruguay", 
"Uzbekistan", "Vanuatu", "Venezuela", "Vietnam", "Yemen", "Zambia", 
"Zimbabwe"), class = "factor"), Country.Region = c(8L, 1L, 3L, 
5L, 4L, 10L, 1L, 2L, 5L, 5L, 10L, 3L, 8L, 1L, 10L, 5L, 8L, 2L, 
1L, 4L, 2L, 10L, 9L, 7L, 1L, 7L, 4L, 1L, 7L, 4L, 5L, 4L, 4L, 
8L, 4L, 2L, 6L, 6L, 2L, 4L, 4L, 4L, 2L, 1L, 2L, 3L, 1L, 4L, 5L, 
10L, 2L, 2L, 2L, 4L, 4L, 4L, 1L, 9L, 5L, 5L, 4L, 4L, 1L, 4L, 
5L, 4L, 9L, 5L, 10L, 2L, 4L, 10L, 2L, 2L, 1L, 5L, 8L, 7L, 3L, 
3L, 5L, 3L, 5L, 4L, 10L, 6L, 1L, 3L, 4L, 6L, 6L, 3L, 1L, 7L, 
3L, 4L, 1L, 4L, 3L, 5L, 1L, 5L, 4L, 4L, 7L, 8L, 4L, 5L, 4L, 4L, 
2L, 5L, 6L, 1L, 1L, 3L, 4L, 3L, 4L, 9L, 8L, 5L, 9L, 5L, 2L, 4L, 
4L, 5L, 9L, 9L, 9L, 8L, 2L, 9L, 2L, 2L, 7L, 1L, 5L, 4L, 7L, 3L, 
1L, 1L, 4L, 10L, 10L, 10L, 5L, 4L, 3L, 4L, 1L, 4L, 4L, 7L, 1L, 
7L, 1L, 4L, 4L, 4L, 5L, 4L, 10L, 4L, 5L, 5L, 3L, 1L, 7L, 4L, 
9L, 10L, 3L, 3L, 3L, 1L, 9L, 4L, 1L, 1L, 3L, 5L, 4L, 5L, 4L, 
2L, 1L, 2L, 9L, 3L, 1L, 4L), Under.5.Mortality.Rate = c(137.3500061, 
20.40999985, 30.80999947, 6.579999924, 178.6000061, 22.02000046, 
51.13999939, 20.05999947, 6.059999943, 5.46999979, 19.12000084, 
11.18999958, 79.55999756, 28.54000092, 19.89999962, 5.639999866, 
79.80999756, 56.77999878, 9.569999695, 58.18000031, 28.07999992, 
29.54999924, 34.72999954, 9.199999809, 15.46000004, 72.59999847, 
145.4600067, 14.72000027, 85.63999939, 132.8600006, 6.480000019, 
42.68000031, 150.5, 15.02999973, 185.2100067, 10.13000011, 27.06999969, 
7.619999886, 22.79000092, 78.52999878, 113.0199966, 165.1199951, 
13.39999962, 7.949999809, 7.730000019, 5.590000153, 6.460000038, 
128.3200073, 5.489999771, 20.05999947, 35.97000122, 31.18000031, 
30.44000053, 180.1799927, 126.4899979, 95.69000244, 9.210000038, 
30.03000069, 4.010000229, 4.949999809, 83.83000183, 80.19999695, 
31.62000084, 110.2300034, 4.889999866, 93.91000366, 56.91999817, 
6.400000095, 20.76000023, 45.81999969, 163.9900055, 44.61000061, 
90.98000336, 33.29999924, 9.079999924, 3.730000019, 79.45999908, 
46.09999847, 43.70999908, 39.90000153, 6.769999981, 6.690000057, 
5.730000019, 123.8099976, 23.86000061, 4.079999924, 39.5, 21.85000038, 
96.69000244, 44.25, 8.93999958, 11.47000027, 49.27000046, 91.37999725, 
12.98999977, 105.8600006, 12.56000042, 151.6100006, 19.94000053, 
NA, 9.520000458, 4.71999979, 94.59999847, 129.8999939, 8.5, 28.04000092, 
199.6399994, 6.849999905, 97.87999725, 16.95999908, 23.54999924, 
NA, 52.43000031, 19.60000038, 12.39999962, 46.31000137, 150.2299957, 
14.13000011, 61.52000046, NA, 69.44999695, 6.139999866, 32.08000183, 
7.300000191, 36.22999954, 205.1699982, 172.3699951, 4.639999866, 
34.79000092, 45.15000153, NA, 90.91000366, 22.34000015, 90.08000183, 
26.45000076, 36.41999817, 36.63000107, 8.770000458, 6.639999866, 
183.9799957, 79.04000092, 12.32999992, 19.40999985, 19.03000069, 
142.6300049, NA, 16.13999939, 23.86000061, NA, 67.91999817, 20.27000046, 
115.0800018, 7.21999979, 17.17000008, 184.1300049, 3.680000067, 
9.020000458, 17.10000038, 4.900000095, 137.2100067, 42.95999908, 
74.22000122, 5.260000229, 101.0999985, 42.54000092, 106.4199982, 
4.489999771, 5.639999866, 16.70999908, 73.68000031, 12.68999958, 
109.0500031, 21.54000092, 32.61999893, 5.940000057, 24.13999939, 
37.29999924, 61.74000168, NA, 134.8099976, 18.44000053, 17.59000015, 
40.22000122, 6.190000057, 115.1299973, 8.050000191, 161.7100067, 
15.60999966, 54.25, 21.29999924, 23.29999924, 85.33999634, NA, 
133.4700012)), .Names = c("Country.Name", "Country.Region", "Under.5.Mortality.Rate"
), class = "data.frame", row.names = c(NA, -194L))
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2 回答 2

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由于您的目标是为每个区域生成模型，因此我认为您想要的是plyr函数，您需要将其安装为一个包。对于新的 R 用户来说，这听起来可能令人生畏，但绝对值得学习，因为它很容易让您对数据子集进行分析。

在您的情况下，您的代码如下所示：

require(plyr)
models = ddply(df, .(CountryRegion), lm, formula = y ~ x1 + x2)

其中 df 是您的数据框， CountryRegion 是您用于子集数据的分类变量， lm （或替代方法）是建模函数， x1、x2 等是您的自变量。因此，您将拥有每个 CountryRegion 的单独模型。

希望这会有所帮助，并确保您查看plyr！

于 2013-04-19T16:11:05.910 回答
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据我所知，您希望为您的 10 类因素设置对比。设置对比的方法之一确实是虚拟化。如果单个名义变量有 10 个类别，则可以创建 9 个虚拟变量来表示多元回归中的相同信息。如果是这种情况，您正在寻找的是contrasts()：

x <- factor(1:10,ordered=FALSE)
contrasts(x) <- contr.treatment(10)
print(x)

然后您只需x在回归模型中使用，R 将识别对比矩阵并相应地应用它。在输出中，您将看到您设置的级别。

base=您可以使用 中的选项指定将哪个类别用作参考contr.treatment。然后，回归系数的解释是将每个虚拟变量（相应类别的平均值）与参考类别（基本水平的平均值）进行比较。

还有其他设置对比的方法。对于国家而言，将每个国家与总体平均值进行比较可能有用，而不是与一个特定国家的平均值进行比较。这可以通过使用 的偏差编码来实现contr.sum(10)。您对效果的解释发生了变化，截距的含义也发生了变化。一般来说，了解不同的对比方法非常有用，因为它们使回归等简单工具更加灵活和强大。可以在此处找到有关 R 中各种编码系统的其他信息。祝你分析顺利。

PS你也可以手动指定一个对比矩阵，这对于偏差编码很有用，因为它没有base选项。

于 2013-04-20T15:14:03.100 回答