Hello Stack Overflow人，我花了一段时间寻找解决我问题的方法，但没找到任何东西，所以我想发帖。
基本上我有按字母顺序列出的196个国家/地区的数据集。其中一个变量根据该国家所在的地区指定1-10的数字。例如，东欧= 1，西欧= 2，中东= 3，南美= 4，依此类推。
以下是数据集的直观表示：
国名------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10个区域分成他们自己的虚拟变量，以便我通过多元回归来运行它们，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，然后如何在单个和多元回归中测试它们的效果。
对不起，如果这看似简单或愚蠢的问题，我对使用R来说相当新。
感谢您的帮助。
编辑：这是请求的输出输出：
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))
答案 0 :(得分：0)
由于您的目标是为每个区域生成模型，我认为您需要的是 plyr 功能，您需要将其作为包安装。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，因为它可以轻松地对子集数据进行分析。
在您的情况下，您的代码将如下所示：
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）是建模函数，x1，x2等是您的自变量。因此，每个CountryRegion都有单独的模型。
希望这会有所帮助，并确保您查看 plyr ！
答案 1 :(得分：0)
据我所知，您希望为您的10类因素设置对比度。设置对比的方法之一确实是虚假化。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变量回归中的相同信息。如果是这种情况，您要找的是contrasts()：
x <- factor(1:10,ordered=FALSE)
contrasts(x) <- contr.treatment(10)
print(x)
然后，您只需在回归模型中使用x，R将识别对比矩阵并相应地应用它。在输出中，您将看到已设置的级别。
您可以使用base=中的contr.treatment选项指定将哪个类别用作参考。然后，回归系数的解释是每个虚拟变量（相应类别的平均值）与参考类别（基准水平的平均值）的比较。
还有其他设置对比的方法。对于国家而言，将每个国家与平均值进行比较可能是有用的，而不是一个特定国家的平均值。这将通过使用contr.sum(10)的偏差编码来实现。你的效果的解释会发生变化，拦截的意义也会发生变化。一般来说，了解不同的对比方法非常有用，因为它们使得像回归这样的简单工具更加灵活和强大。有关R中各种编码系统的更多信息，请参见here。祝你的分析好运。
P.S。您也可以手动指定对比矩阵，这对偏差编码很有用，因为它没有base选项。