使用map()在lm中无法识别的因素 [英] Factors not being recognised in a lm using map()
本文介绍了使用map()在lm中无法识别的因素的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
目标:我想使用map()函数来建立一个涉及分类变量的线性模型.
Aim: I want to use the map() function to do a linear model that involves categorical variables.
问题:我遇到以下错误，但我知道包含的类别变量，borrower_genders具有五个级别.
Problem: I am getting the following error but I know the categorical variable included, borrower_genders has five levels.
Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels
我所做的事情:删除borrower_genders时，以下代码可以很好地工作，而不是这样做，为什么?
What I've done: The following code works perfectly fine when removing borrower_genders, not with it, why?
kiva_country%>%
dplyr::group_by(country_code)%>%
tidyr::nest(-country_code) %>%
mutate(model=map(data, ~lm(loan_usd~
lender_count+
borrower_genders,
data=.)))
数据:
kiva_country<- structure(list(loan_usd = c(0.913621225, 0.085394175, 6.079311875,
12.626263125, 0.024824985, 6.54695125, 0.1793277675, 0.21334368,
0.12996942, 6.079311875, 14.496820625, 0.10343936, 650, 87.632824,
6.079311875, 0.0597759225, 4.208754375, 0.9948269, 2.48706725,
1.9896538, 4.208754375, 1.8705575, 2.338196875, 0.0939335925,
5.144033125, 6.54695125, 0.1337112975, 3.741115, 6.079311875,
4.47672105, 0.2228521625, 1.9896538, 0.224215244, 231.40113,
0.6284916, 0.04964997, 0.3119930275, 0.029483707725, 2.48706725,
14.42499005, 2.80583625, 4.208754375, 0.489196896, 200, 6.079311875,
0.2793296, 0.36544849, 0.65066387, 2.80583625, 41.07788625, 1300,
173.5508475, 0.02585984, 0.04149558865, 1000, 0.7576973525, 4000,
0.308370055, 6.54695125, 137.4675435, 800, 9.948269, 0.13576416,
0.2418007625, 112.27955575, 4.67639375, 30.12378325, 2.80583625,
0.9948269, 0.10990432, 4.208754375, 6.079311875, 2.238360525,
4.725427775, 20.108493125, 4.208754375, 6.217668125, 3.741115,
0.13663068, 3.48189415, 11.690984375, 8.885148125, 1.9896538,
10.28806625, 2500, 9.699562275, 4.9741345, 7.48223, 0.04964997,
8.41750875, 3.2932654, 139.66481325, 7.014590625, 1800, 5.144033125,
4.208754375, 7.014590625, 7.014590625, 35.60083475, 3.741115,
1000, 54.770515, 1.6486415625, 0.04344372375, 0.108106928325,
3.1130664, 2.338196875, 6.54695125, 4.208754375, 3.74391633,
3.704923575, 1000, 3.704923575, 1.9896538, 1500, 9.699562275,
5.144033125, 2.46994905, 89.12655875, 2.48706725, 4.67639375,
4.208754375, 0.53484519, 8.41750875, 8.885148125, 3.48189415,
19.16968025, 4.9741345, 500, 1.9896538, 0.1117124325, 7.949869375,
4.208754375, 2.338196875, 2.058290875, 1100, 4.11658175, 6.54695125,
8.41750875, 7.48223, 0.1004630041, 19.16968025, 2.238360525,
0.445704325, 4.208754375, 0.33617792, 6.54695125, 424.235405,
2.338196875, 3.741115, 0.2094972, 0.817726215, 0.61674011, 5.6116725,
0.24824985, 9.820426875, 1.9896538, 0.2134854375, 0.0646496,
0.3119930275, 19.173214375, 4.208754375, 0.024824985, 7.014590625,
4.9741345, 0.11575085255, 1147.95914625, 0.9715994275, 1.243533625,
2.222982255, 2.80583625, 3.667749, 1.6466327, 2.62710048, 3.741115,
11.223345, 0.51762116375, 0.36544849, 1500, 2.5674243, 4.67639375,
500, 5.96049055, 6.54695125, 0.038219621125, 2.9862189775, 0.024824985,
139.875587, 0.294315025, 4.1507552, 6.9637883, 4.9741345, 2.34777983,
0.1337112975, 2.48706725, 0.1538732125, 2.238360525, 4.3224108375,
0.0646496, 15.2741715, 65.724618, 3.741115, 1175, 5.6116725,
0.17078835, 0.2134854375, 250.6845575, 3.273475625, 575, 7.014590625,
4.208754375, 183.290058, 3.48189415, 1000, 1.76407096, 2.238360525,
3.273475625, 0.20494602, 5.6116725, 0.03878976, 500, 9.820426875,
440.96208475, 1351.76417775, 4.208754375, 1500, 0.93527875, 328.39468725,
0.802267785, 800, 4.67639375, 10.28806625, 2.48706725, 0.5379833025,
0.4189944, 26.0410179, 0.10247301, 0.43963775, 5.6116725, 0.0620624625,
0.29738816, 91.645029, 420.03971625, 106.80250425, 3.002586925,
6.079311875, 5.5573853625, 800, 1.0376888, 0.153709515, 0.06826870875,
4.208754375, 1.02990029, 190.92714375, 4.208754375, 4.208754375,
4.9741345, 1.2008166, 2.48706725, 1657.24760775, 4.208754375,
5.13693565, 0.6828194075, 5.222841225, 83.88382, 1.196990445,
4.208754375, 1.243533625, 4.208754375, 4.208754375, 1.8705575,
0.15515904, 2.80583625, 2.46994905, 6.54695125, 6.079311875,
1.243533625, 43.816412, 16.5048705, 500, 0.21721861875, 7.705403475,
4.208754375, 0.01861873875, 800, 0.2094972, 0.746120175, 5200,
2.338196875, 7.48223, 1000, 0.119551845, 20.108493125, 0.124124925,
0.1337112975, 5.720254675, 2.39398089, 0.256182525, 0.05171968,
0.09050944, 1.02422911125, 0.548172735, 670.4304375, 1.243533625,
0.10990432, 54.66472125, 4.11658175, 4.208754375, 1.8159554,
4.9741345, 39.2449143, 8.207321925, 0.0388692568, 0.6828194075,
0.47840704, 104.0639785, 0.5379833025, 5.9689614, 110.09751375,
12.15862375, 2.80583625, 2.058290875, 49.2934635, 0.04699248,
0.1626659675, 6.54695125, 2500, 160.37880075, 4.4012988, 1500,
0.1793277675, 2.9844807, 8.885148125, 5.144033125, 5.144033125,
0.35656346, 10.28806625, 0.035335688, 2.46994905, 106.9192005,
0.26431719, 4.228014325, 5.144033125, 4.208754375, 3.233187425,
0.9948269, 1400, 0.5583399425, 7.9586152, 6.079311875, 0.9948269,
0.119551845, 2500, 1.158831245, 511.01082875, 1.515394705, 0.17078835,
0.16808896, 4.208754375, 0.17828173, 6.079311875, 4.67639375,
4.9741345, 3.48189415, 336.031773, 1800, 2.735773975, 1200, 4.228014325,
72.666295, 1.9896538, 0.07111456, 2.338196875, 4.208754375, 1000,
0.1861873875, 625, 0.35656346, 600, 19.64085375, 2.338196875,
1145.5628625, 1.243533625, 11.9379228, 3.741115, 0.1861873875,
0.051236505, 0.24824985, 8.5129526075, 0.024824985, 5.5573853625,
0.17078835, 5.144033125, 500, 3000, 0.91724418, 8.41750875, 3.741115,
0.1451700975, 0.9364284075, 2.338196875, 0.325331935, 3.741115,
8.207321925, 3.2932654, 10.755705625, 0.5055834125, 7.949869375,
0.51762116375, 3.741115, 4.208754375, 0.21980864, 0.0372374775,
0.93527875, 800, 21.28159055, 1.36798908, 2000, 4.208754375,
676.6696025, 0.04964997, 4.208754375, 1.8705575, 6.715081575,
3.48189415, 3.741115, 4.3224108375, 1.2925425425, 0.02585984,
2.058290875, 16.367378125, 2.80583625, 0.18101888, 1.297111,
11.00771925, 0.37573437, 3.741115, 8.41750875, 4.67639375, 0.2793296,
1.196990445, 0.75617693, 2.48706725, 6.079311875, 4.208754375,
0.1793277675, 2.338196875, 0.09174312, 6.9637883, 600, 5.6116725,
0.256182525, 32.14086125, 1061.55491925, 3.233187425, 1750, 3.741115,
0.04964997, 8.940735825, 1160.837034, 2.884287245, 147.8803905,
21.51141125, 8.41750875, 800, 0.04344372375, 154.26742, 5.6116725,
462.80226, 0.49741345, 2875, 7.014590625, 7.48223, 99.28211475,
0.5794156225, 0.128446112, 68.46314375, 4.67639375, 2900, 0.21721861875,
0.9948269, 0.903179175, 3.273475625, 4.67639375, 1175, 11.689216075,
0.623986055, 17.245426825, 8.41750875, 5.1457271875, 11.223345,
800, 11.690984375, 300, 1000, 0.0764526, 2.9844807, 7.949869375,
137.4675435, 10.755705625, 0.04964997, 3.273475625, 2.9844807,
4.9741345, 6.54695125, 4.208754375, 2.48706725, 2.338196875,
3.233187425, 6.079311875, 1.880984985, 3.9793076, 699.708432,
0.5892198075, 0.034461152, 24.64673175, 1100, 0.71761056, 221.75941625,
878.339758, 7.949869375, 1000, 0.31678304, 0.029483707725, 1.9896538,
3000, 0.119551845, 0.05585621625, 2.80583625, 0.0597759225, 2.80583625
), lender_count = c(49L, 10L, 8L, 8L, 4L, 11L, 21L, 27L, 17L,
13L, 30L, 9L, 13L, 13L, 10L, 7L, 9L, 3L, 27L, 6L, 9L, 1L, 4L,
6L, 8L, 13L, 2L, 8L, 10L, 6L, 5L, 8L, 16L, 24L, 5L, 8L, 7L, 11L,
4L, 52L, 6L, 5L, 42L, 7L, 11L, 4L, 22L, 32L, 4L, 15L, 41L, 11L,
4L, 36L, 31L, 11L, 138L, 27L, 2L, 18L, 23L, 34L, 20L, 24L, 28L,
10L, 10L, 5L, 4L, 8L, 2L, 6L, 8L, 1L, 25L, 8L, 25L, 7L, 16L,
14L, 24L, 1L, 8L, 18L, 93L, 36L, 12L, 16L, 8L, 11L, 16L, 34L,
14L, 69L, 6L, 8L, 15L, 12L, 11L, 8L, 32L, 16L, 274L, 4L, 86L,
57L, 1L, 14L, 4L, 46L, 18L, 32L, 11L, 3L, 44L, 25L, 6L, 10L,
12L, 12L, 10L, 8L, 6L, 12L, 16L, 14L, 7L, 11L, 18L, 6L, 18L,
13L, 1L, 4L, 6L, 38L, 17L, 8L, 13L, 15L, 64L, 6L, 4L, 1L, 9L,
31L, 12L, 41L, 5L, 1L, 3L, 27L, 50L, 12L, 19L, 18L, 8L, 25L,
8L, 6L, 38L, 9L, 4L, 14L, 15L, 53L, 159L, 12L, 3L, 13L, 6L, 10L,
8L, 167L, 7L, 1L, 14L, 18L, 52L, 7L, 5L, 17L, 14L, 7L, 31L, 57L,
4L, 67L, 10L, 73L, 24L, 19L, 62L, 2L, 14L, 31L, 8L, 9L, 9L, 2L,
12L, 8L, 28L, 12L, 14L, 9L, 22L, 4L, 23L, 6L, 8L, 18L, 14L, 22L,
26L, 1L, 6L, 15L, 11L, 6L, 12L, 17L, 79L, 59L, 8L, 55L, 2L, 2L,
17L, 28L, 10L, 15L, 6L, 2L, 5L, 55L, 12L, 5L, 8L, 6L, 40L, 2L,
12L, 17L, 8L, 3L, 27L, 28L, 18L, 16L, 10L, 6L, 53L, 1L, 9L, 1L,
13L, 10L, 19L, 20L, 8L, 14L, 36L, 19L, 16L, 2L, 1L, 10L, 9L,
8L, 1L, 4L, 1L, 7L, 11L, 10L, 7L, 12L, 40L, 19L, 28L, 2L, 8L,
3L, 20L, 1L, 3L, 43L, 4L, 14L, 22L, 14L, 12L, 19L, 3L, 20L, 9L,
27L, 8L, 10L, 86L, 18L, 16L, 4L, 10L, 15L, 11L, 9L, 27L, 19L,
103L, 13L, 20L, 53L, 58L, 12L, 9L, 16L, 14L, 14L, 4L, 7L, 18L,
15L, 36L, 1L, 50L, 20L, 12L, 44L, 13L, 9L, 19L, 1L, 4L, 7L, 15L,
19L, 9L, 13L, 17L, 12L, 9L, 1L, 7L, 1L, 39L, 8L, 25L, 7L, 4L,
7L, 85L, 24L, 50L, 25L, 20L, 22L, 2L, 4L, 13L, 9L, 18L, 9L, 21L,
22L, 4L, 41L, 12L, 50L, 8L, 11L, 3L, 9L, 25L, 27L, 23L, 8L, 24L,
19L, 1L, 64L, 5L, 45L, 2L, 22L, 6L, 27L, 96L, 3L, 23L, 16L, 1L,
20L, 77L, 83L, 16L, 1L, 16L, 1L, 5L, 72L, 8L, 17L, 12L, 16L,
33L, 5L, 44L, 6L, 6L, 28L, 5L, 2L, 20L, 50L, 7L, 73L, 9L, 26L,
2L, 9L, 4L, 27L, 28L, 8L, 21L, 15L, 4L, 9L, 14L, 6L, 23L, 24L,
26L, 31L, 8L, 13L, 10L, 1L, 6L, 14L, 6L, 11L, 9L, 21L, 1L, 11L,
27L, 21L, 12L, 30L, 19L, 63L, 13L, 66L, 7L, 8L, 21L, 43L, 92L,
44L, 29L, 18L, 13L, 7L, 10L, 11L, 41L, 2L, 73L, 14L, 15L, 13L,
12L, 33L, 24L, 7L, 66L, 17L, 4L, 31L, 1L, 8L, 17L, 40L, 13L,
44L, 12L, 19L, 13L, 23L, 2L, 12L, 34L, 10L, 4L, 11L, 2L, 10L,
8L, 1L, 12L, 19L, 8L, 1L, 10L, 5L, 12L, 11L, 10L, 12L, 82L, 47L,
9L, 7L, 14L, 74L, 19L, 54L, 11L, 17L, 16L, 11L, 7L, 59L, 12L,
7L, 6L, 7L, 6L), borrower_genders = structure(c(4L, 4L, 4L, 4L,
2L, 4L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L,
5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 4L, 5L, 4L, 4L, 2L,
4L, 4L, 4L, 1L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 2L, 4L, 5L, 5L, 4L,
4L, 4L, 4L, 2L, 1L, 4L, 5L, 5L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L,
4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 1L, 4L, 4L, 4L, 4L, 1L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 5L, 1L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 4L, 4L, 4L, 4L, 4L, 1L,
4L, 4L, 2L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 2L, 5L, 4L, 4L, 4L, 1L, 4L, 4L,
4L, 4L, 5L, 2L, 3L, 4L, 2L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 4L,
4L, 1L, 1L, 2L, 4L, 4L, 4L, 4L, 4L, 1L, 4L, 4L, 1L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 2L, 1L, 4L, 4L, 4L, 1L, 4L, 5L, 4L, 4L, 4L,
5L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 1L, 1L, 4L, 4L, 4L, 4L,
5L, 4L, 4L, 4L, 5L, 4L, 5L, 2L, 4L, 2L, 4L, 1L, 5L, 4L, 4L, 5L,
4L, 4L, 4L, 5L, 4L, 4L, 2L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 4L, 1L,
4L, 5L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L,
4L, 4L, 2L, 5L, 2L, 4L, 4L, 1L, 4L, 5L, 4L, 1L, 4L, 4L, 4L, 4L,
4L, 2L, 4L, 1L, 4L, 5L, 4L, 4L, 2L, 4L, 4L, 4L, 4L, 5L, 4L, 4L,
5L, 4L, 2L, 5L, 4L, 2L, 1L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 2L, 4L, 4L, 4L, 5L,
4L, 5L, 4L, 4L, 5L, 4L, 4L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L,
4L, 5L, 4L, 4L, 4L, 4L, 4L, 5L, 2L, 2L, 5L, 2L, 4L, 5L, 4L, 5L,
4L, 4L, 5L, 2L, 5L, 4L, 5L, 4L, 4L, 2L, 4L, 4L, 4L, 5L, 4L, 1L,
2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 5L, 2L, 4L, 4L, 4L, 1L,
4L, 5L, 2L, 4L, 2L, 4L, 4L, 5L, 1L, 4L, 5L, 5L, 4L, 4L, 4L, 5L,
1L, 4L, 4L, 5L, 4L, 4L, 4L, 2L, 4L, 4L, 4L, 5L, 4L, 5L, 4L, 5L,
4L, 4L, 4L, 4L, 4L, 2L, 5L, 4L, 5L, 4L, 4L, 4L, 1L, 5L, 4L, 5L,
4L, 1L, 4L, 1L, 4L, 1L, 5L, 1L, 4L, 4L, 4L, 4L, 5L, 1L, 5L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 5L, 4L, 2L, 2L, 4L, 4L, 4L, 4L,
4L, 4L, 2L, 5L, 4L, 4L, 4L, 5L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L,
1L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 2L, 4L, 4L, 4L, 1L, 5L, 4L, 4L,
4L, 1L, 4L, 5L, 4L, 5L, 4L, 4L, 5L, 4L, 4L, 1L, 4L, 4L, 5L), .Label = c("mixed_genders",
"mult_females", "mult_males", "single_female", "single_male"), class = "factor"),
country_code = c("LB", "CO", "PH", "PH", "KH", "PH", "CO",
"UG", "MG", "PH", "PH", "UG", "KH", "TJ", "PH", "CO", "PH",
"KE", "KE", "KE", "PH", "PH", "PH", "CO", "PH", "PH", "TG",
"PH", "PH", "KE", "ML", "KE", "MN", "WS", "NG", "KH", "TG",
"VN", "KE", "KE", "PH", "PH", "MN", "TJ", "PH", "NG", "LB",
"PY", "PH", "TJ", "EC", "WS", "UG", "VN", "KH", "ML", "CD",
"TZ", "PH", "PE", "PS", "KE", "UG", "PY", "TJ", "PH", "TJ",
"PH", "KE", "UG", "PH", "PH", "KE", "KE", "PH", "PH", "KE",
"PH", "CO", "KE", "PH", "PH", "KE", "PH", "AM", "KE", "KE",
"PH", "KH", "PH", "PK", "TJ", "PH", "KE", "PH", "PH", "PH",
"PH", "TJ", "PH", "EC", "TJ", "PY", "KH", "VN", "AM", "PH",
"PH", "PH", "SN", "PK", "EC", "PK", "KE", "EC", "KE", "PH",
"PK", "GH", "KE", "PH", "PH", "SN", "PH", "PH", "KE", "TJ",
"KE", "ZW", "KE", "KH", "PH", "PH", "PH", "PK", "EC", "PK",
"PH", "PH", "PH", "VN", "TJ", "KE", "TG", "PH", "UG", "PH",
"WS", "PH", "PH", "NG", "PY", "TZ", "PH", "KH", "PH", "KE",
"CO", "UG", "CM", "PH", "PH", "KH", "PH", "KE", "VN", "BO",
"PY", "KE", "LR", "PH", "IN", "PK", "BI", "PH", "PH", "TZ",
"LB", "TL", "IN", "PH", "EC", "MZ", "PH", "VN", "ML", "KH",
"ZA", "RW", "AM", "KE", "KE", "MW", "TG", "KE", "PY", "KE",
"PK", "UG", "PE", "TJ", "PH", "EC", "PH", "CO", "CO", "WS",
"PH", "LB", "PH", "PH", "PE", "KE", "EC", "AM", "KE", "PH",
"CO", "PH", "UG", "ZW", "PH", "BO", "PE", "PH", "BO", "PH",
"PE", "CM", "TL", "PH", "PH", "KE", "CO", "NG", "IN", "CO",
"PY", "PH", "KH", "UG", "PE", "PE", "TJ", "NP", "PH", "PK",
"PS", "AM", "CO", "KH", "PH", "LB", "PE", "PH", "PH", "KE",
"YE", "KE", "PE", "PH", "KG", "TZ", "KE", "GH", "LR", "PH",
"KE", "PH", "PH", "PH", "UG", "PH", "PK", "PH", "PH", "KE",
"TJ", "IN", "SO", "KH", "KG", "PH", "KH", "TL", "NG", "KE",
"CD", "PH", "PH", "PS", "CO", "PH", "KH", "TG", "KE", "LR",
"CO", "UG", "UG", "TZ", "LB", "JO", "KE", "UG", "BO", "PK",
"PH", "AM", "KE", "IN", "KE", "ID", "TZ", "UG", "TJ", "CO",
"KE", "GH", "PH", "PH", "PK", "TJ", "SL", "PY", "PH", "LB",
"PE", "IN", "EC", "CO", "KE", "PH", "PH", "PH", "ML", "PH",
"ID", "PK", "PE", "TZ", "KE", "PH", "PH", "KE", "KE", "LB",
"PY", "KE", "PH", "KE", "CO", "PS", "TG", "WS", "ML", "CO",
"UG", "PH", "TG", "PH", "PH", "KE", "KE", "PE", "LB", "KE",
"ZW", "KE", "EG", "KE", "UG", "PH", "PH", "PS", "KH", "PS",
"TG", "KH", "PH", "PH", "PE", "KE", "KE", "PH", "KH", "CO",
"KH", "BF", "KH", "PK", "CO", "PH", "ZW", "AM", "MN", "PH",
"PH", "CO", "PY", "PH", "PY", "PH", "KE", "PK", "PH", "PY",
"PH", "TZ", "PH", "PH", "UG", "KH", "PH", "EC", "KG", "LR",
"PS", "PH", "AZ", "KH", "PH", "PH", "KE", "KE", "PH", "PK",
"ML", "UG", "PK", "PH", "PH", "UG", "AM", "KG", "CO", "PH",
"PH", "PH", "NG", "LR", "PY", "KE", "PH", "PH", "CO", "PH",
"MG", "KE", "KH", "PH", "CO", "EG", "PE", "KE", "CD", "PH",
"KH", "MZ", "PE", "RW", "TJ", "PH", "PH", "EC", "KH", "WS",
"PH", "WS", "KE", "KH", "PH", "PH", "PE", "SN", "SL", "TJ",
"PH", "LB", "KH", "KE", "MM", "PH", "PH", "EC", "KE", "ML",
"KG", "PH", "PK", "PH", "PS", "PH", "KH", "TL", "MG", "KE",
"PH", "PE", "PH", "KH", "PH", "KE", "KE", "PH", "PH", "KE",
"PH", "KE", "PH", "LR", "KE", "BO", "CO", "SL", "TJ", "EC",
"UG", "WS", "GE", "PH", "EC", "UG", "VN", "KE", "GE", "CO",
"KH", "PH", "CO", "PH")), row.names = c(NA, -531L), class = c("tbl_df",
"tbl", "data.frame"), na.action = structure(c(`9` = 9L, `30` = 30L,
`32` = 32L, `102` = 102L, `104` = 104L, `118` = 118L, `119` = 119L,
`164` = 164L, `166` = 166L, `169` = 169L, `196` = 196L, `224` = 224L,
`231` = 231L, `255` = 255L, `264` = 264L, `268` = 268L, `274` = 274L,
`280` = 280L, `285` = 285L, `327` = 327L, `338` = 338L, `377` = 377L,
`385` = 385L, `386` = 386L, `390` = 390L, `402` = 402L, `420` = 420L,
`441` = 441L, `445` = 445L, `446` = 446L, `473` = 473L, `528` = 528L,
`533` = 533L, `541` = 541L, `544` = 544L, `546` = 546L, `555` = 555L
), class = "omit"))
推荐答案
错误消息指出，至少一个模型中的borrower_genders级别少于2个.首先要做的是梳理数据以查看发生这种情况的地方.
The error message says that there are fewer than 2 levels of borrower_genders in at least one of the models. The first thing to do is comb through the data to see where this happens.
如果您对每个国家/地区的不同borrower_genders级别进行计数，然后对每个计数所在的国家/地区县计数(获取元数据)，则会看到47个不同的国家/地区中的19个仅存在一个级别，而没有一个具有全部5个级别.
If you count up the number of distinct borrower_genders levels for each country, then county the number of countries with each of those counts (it gets meta), you'll see that of your 47 distinct countries, 19 of them have only one level present, and none of them have all 5 levels.
library(tidyverse)
kiva_country %>%
distinct(country_code, borrower_genders) %>%
count(country_code) %>%
count(n)
#> # A tibble: 4 x 2
#> n nn
#> <int> <int>
#> 1 1 19
#> 2 2 19
#> 3 3 5
#> 4 4 4
这是您可能需要重新评估方法的地方-仅针对某些样本量或某些性别，按洲而不是按国家/地区等进行过滤.我将把这些决定留给您，但仅举例说明，我仅针对级别超过1的国家/地区(其中有28个)进行过滤.即便如此，其中一些还是很小的样本，稍后警告消息将指出.我假设您的完整数据集中有更好的样本量.
This is a point where you might want to reevaluate your approach—filter for only certain sample sizes or certain variety of genders, group by continent instead of country, etc. I'll leave those decisions up to you, but to illustrate, I'm filtering for only the countries that have more than 1 level (there are 28 of these). Even still, some of these are very small samples, as the warning message later will point out; I'll assume there are better sample sizes in your full dataset.
select_countries <- kiva_country %>%
distinct(country_code, borrower_genders) %>%
count(country_code) %>%
filter(n > 1) %>%
pull(country_code)
现在，除了关于样本量的警告以外，您的嵌套/地图/模型工作流程还可以正常工作.
Now your nest / map / model workflow works fine, aside from the warning about sample sizes.
kiva_country %>%
filter(country_code %in% select_countries) %>%
group_by(country_code) %>%
nest(-country_code) %>%
mutate(model = map(data, ~lm(loan_usd ~ lender_count + borrower_genders, data = .))) %>%
mutate(glnc = map(model, broom::glance)) %>%
unnest(glnc)
#> Warning in stats::summary.lm(x): essentially perfect fit: summary may be
#> unreliable
#> # A tibble: 28 x 14
#> country_code data model r.squared adj.r.squared sigma statistic
#> <chr> <list> <lis> <dbl> <dbl> <dbl> <dbl>
#> 1 LB <tibble … <S3:… 0.573 0.451 847. 4.69
#> 2 CO <tibble … <S3:… 0.195 0.127 0.135 2.90
#> 3 PH <tibble … <S3:… 0.532 0.526 2.55 88.2
#> 4 KH <tibble … <S3:… 0.836 0.810 246. 32.0
#> 5 UG <tibble … <S3:… 0.903 0.887 0.0562 55.9
#> 6 TJ <tibble … <S3:… 0.324 0.234 44.6 3.59
#> 7 KE <tibble … <S3:… 0.297 0.256 180. 7.29
#> 8 MN <tibble … <S3:… 1 NaN NaN NaN
#> 9 WS <tibble … <S3:… 0.976 0.967 25.6 102.
#> 10 NG <tibble … <S3:… 0.615 0.358 0.130 2.39
#> # ... with 18 more rows, and 7 more variables: p.value <dbl>, df <int>,
#> # logLik <dbl>, AIC <dbl>, BIC <dbl>, deviance <dbl>, df.residual <int>
reprex软件包(v0.2.0)于2018-06-25创建.
Created on 2018-06-25 by the reprex package (v0.2.0).
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