回圈多個多級有序logits，整理結果，一次洗掉一個觀察結果-有解無憂

我試圖在上使用隨機截距回圈多個多級有序邏輯回歸，country一次從主資料集中洗掉一個觀察結果，同時生成一個帶有結果的大量、增強的整潔資料框。鑒于我只是在回圈方面遇到了問題，并且我可以一次生成一個資料幀的所有內容，我將首先展示我試圖用兩個資料幀做什么。然后，我將（糟糕地）嘗試通過回圈解決問題，這是我非常感謝您的幫助的部分。

首先，讓我們加載一些庫：

# load libraries
library(dplyr)
library(purrr)
library(tibble)
library(ordinal)
library(reprex)
library(magrittr)

接下來，讓我們創建一些示例資料：

# create original df in the right format
data0 = data.frame(country = rep(c("Algeria","Belgium","Canada","Denmark","England", "France"),times=10),
                     x1 = rep(0:1),times=30,
                     x2 = rnorm(n = 60, mean = 100, sd = 5),
                     y = rep(1:6),times=10)
data0$country = factor(data0$country)
data0$y = factor(data0$y)
data0 <- data0 %>% dplyr::select(country,x1,x2,y)

在這篇文章的基礎上，我將為clmm2模型創建一個自定義整理器：

tidy.clmm2 <- 
  function(fit){
    results = as.data.frame(coefficients(summary(fit)))
    colnames(results) = c("estimate","std.error","statistic","p.value")
    results %>% tibble::rownames_to_column("term")
  }

估計第一個模型，整理它，并用置信區間、優勢比和模型觀察數對其進行擴充：

# model 1: get the dataset with one less observation, removing the top row
data1 <- data0 %>% dplyr::slice(-1)

# model 1: estimate
m1 <- clmm2(y ~ x1   x2, random=country, data=data1, Hess = TRUE)
summary(m1)

# model 1: get odds ratio and renames columns for model 1
or1 <- as.data.frame(exp(coef(m1)))
or1 <- or1 %>% rename(estimate.odds =1)
or1$term <- row.names(or1)

# model 1: tidy the model, get the CIs, and store the observation/model number
tidy_m1 <- tidy.clmm2(m1)
tidy_m1$obs <- m1$nobs[[1]]
tidy_m1$conf.low <- tidy_m1$estimate - (1.96 * tidy_m1$std.error)
tidy_m1$conf.high <- tidy_m1$estimate   (1.96 * tidy_m1$std.error)

# model 1: merge over the odds ratios and make final data
tidy_m1 <- left_join(tidy_m1, or1, by=c("term"))

對模型 2 執行相同操作，并將最終輸出與模型 1 系結：

# model 2: get the dataset with one less observation, removing the top row
data2 <- data1 %>% dplyr::slice(-1)

# model 2: estimate
m2 <- clmm2(y ~ x1   x2, random=country, data=data2, Hess = TRUE)
summary(m2)

# model 2: get odds ratio and renames columns for model 1
or2 <- as.data.frame(exp(coef(m2)))
or2 <- or2 %>% rename(estimate.odds =1)
or2$term <- row.names(or2)

# model 2: tidy the model, get the CIs, and store the observation/model number
tidy_m2 <- tidy.clmm2(m2)
tidy_m2$obs <- m2$nobs[[1]]
tidy_m2$conf.low <- tidy_m2$estimate - (1.96 * tidy_m2$std.error)
tidy_m2$conf.high <- tidy_m2$estimate   (1.96 * tidy_m2$std.error)

# model 2: merge over the odds ratios and make final data
tidy_m2 <- left_join(tidy_m2, or2, by=c("term"))

## Bind everything together to get a final data frame
tidy_final <- dplyr::bind_rows(tidy_m1,tidy_m2)

這是我的（糟糕的）回圈嘗試，建立在這個優秀的帖子上：

# create vector to store observation/data frame numbers
vector <- 1:59

# start of model
df_final <- purrr::map_dfr(1:59, 
               function(i) data.frame(model = i, 
                                      tidy.clmm2(glm(as.formula(paste0('y ~ ', i)), 
                                                     random=country,
                                                     Hess = TRUE,
                                                     data = data[vector]))))

如何根據我一次做一個模型的方法來解決上述問題？不要擔心NaNs，因為我只提供了一個高度風格化的示例來促進回應。

uj5u.com熱心網友回復：

首先，我將改進您的 tidyer 功能，以完成您為每個模型所做的所有額外整理（我假設此模型類的整理器在某些包或其他包中尚不存在）：

tidy.clmm2 <- 
  function(fit){
    results = as.data.frame(coefficients(summary(fit)))
    colnames(results) = c("estimate","std.error","statistic","p.value")
    tidy_fit <- results %>% tibble::rownames_to_column("term")
    or1 <- as.data.frame(exp(coef(fit)))
    or1 <- or1 %>% rename(estimate.odds =1)
    or1$term <- row.names(or1)
    
    #tidy the model, get the CIs, and store the observation/model number
    tidy_fit$obs <- fit$nobs[[1]]
    tidy_fit$conf.low <- tidy_fit$estimate - (1.96 * tidy_fit$std.error)
    tidy_fit$conf.high <- tidy_fit$estimate   (1.96 * tidy_fit$std.error)
    
    #merge over the odds ratios and make final data
    left_join(tidy_fit, or1, by=c("term"))
    }

我不知道purrr，但是您可以使用基本函式lapply（我猜是相似的）來估計模型并在連續較小的資料集上獲取整潔的引數，然后將結果傳遞給bind_rows. 在這里，我只對前 10 個這樣做：

df_final <- lapply(1:10, function(i) {
  mod <- clmm2(y ~ x1   x2, random=country, data=data0[(i 1):60,], Hess = TRUE)
  tidy.clmm2(mod)
  }) %>% bind_rows(, .id="Dataset")

> df_final
   Dataset term      estimate    std.error     statistic       p.value obs      conf.low    conf.high estimate.odds
1        1  1|2 -6.438544e 01          NaN           NaN           NaN  59           NaN          NaN  1.090838e-28
2        1  2|3 -3.518858e 01          NaN           NaN           NaN  59           NaN          NaN  5.221475e-16
3        1  3|4  2.961620e-01          NaN           NaN           NaN  59           NaN          NaN  1.344688e 00
4        1  4|5  2.971124e 01 2.207366e 01  1.346004e 00  1.783011e-01  59  -13.55313129  72.97561932  8.006253e 12
5        1  5|6  7.273474e 01          NaN           NaN           NaN  59           NaN          NaN  3.875214e 31
6        1   x1  2.054609e 01          NaN           NaN           NaN  59           NaN          NaN  8.376306e 08
7        1   x2 -8.899366e-02 3.978817e-03 -2.236686e 01 8.275733e-111  59   -0.09679214  -0.08119518  9.148514e-01
8        2  1|2 -7.093800e 01          NaN           NaN           NaN  58           NaN          NaN  1.556034e-31
9        2  2|3 -2.937359e 01 7.293763e 00 -4.027221e 00  5.644000e-05  58  -43.66936969 -15.07781910  1.750693e-13
10       2  3|4 -4.496101e 00 5.083061e 00 -8.845263e-01  3.764122e-01  58  -14.45889969   5.46669816  1.115240e-02
11       2  4|5  2.637413e 01 1.028467e 00  2.564412e 01 4.917528e-145  58   24.35833179  28.38992198  2.845364e 11
12       2  5|6  8.163547e 01 5.497370e-02  1.484991e 03  0.000000e 00  58   81.52771782  81.74321472  2.843364e 35
13       2   x1  1.821630e 01 5.424801e 00  3.357967e 00  7.851802e-04  58    7.58369175  28.84891081  8.151530e 07
14       2   x2 -7.476304e-02          NaN           NaN           NaN  58           NaN          NaN  9.279633e-01
15       3  1|2 -1.105259e 02          NaN           NaN           NaN  57           NaN          NaN  9.981611e-49
16       3  2|3 -4.660975e 01          NaN           NaN           NaN  57           NaN          NaN  5.723279e-21
17       3  3|4 -1.713909e 00 5.880686e 00 -2.914471e-01  7.707094e-01  57  -13.24005308   9.81223521  1.801602e-01
18       3  4|5  4.382707e 01          NaN           NaN           NaN  57           NaN          NaN  1.081073e 19
19       3  5|6  1.265335e 02 3.425069e-03  3.694335e 04  0.000000e 00  57  126.52681215 126.54023843  8.970400e 54
20       3   x1  3.988252e 01 5.999608e-04  6.647520e 04  0.000000e 00  57   39.88133918  39.88369103  2.092937e 17
21       3   x2 -2.175413e-01 2.615869e-04 -8.316215e 02  0.000000e 00  57   -0.21805405  -0.21702863  8.044943e-01
22       4  1|2 -6.646351e 01          NaN           NaN           NaN  56           NaN          NaN  1.365411e-29
23       4  2|3 -3.064726e 01 1.722949e 01 -1.778768e 00  7.527783e-02  56  -64.41706724   3.12253810  4.898489e-14
24       4  3|4 -3.327152e 00          NaN           NaN           NaN  56           NaN          NaN  3.589518e-02
25       4  4|5  3.188512e 01          NaN           NaN           NaN  56           NaN          NaN  7.039323e 13
26       4  5|6  7.214246e 01          NaN           NaN           NaN  56           NaN          NaN  2.143249e 31
27       4   x1  2.524262e 01 1.420819e 01  1.776624e 00  7.563011e-02  56   -2.60544100  53.09068131  9.177632e 10
28       4   x2 -1.139253e-01 5.874164e-04 -1.939430e 02  0.000000e 00  56   -0.11507663  -0.11277396  8.923246e-01
29       5  1|2 -5.520472e 01 1.024174e-03 -5.390171e 04  0.000000e 00  55  -55.20673057 -55.20271580  1.058994e-24
30       5  2|3 -3.324923e 01          NaN           NaN           NaN  55           NaN          NaN  3.631150e-15
31       5  3|4  7.955460e-01 5.817109e 00  1.367597e-01  8.912208e-01  55  -10.60598823  12.19708026  2.215650e 00
32       5  4|5  2.816002e 01 5.061437e 00  5.563641e 00  2.642027e-08  55   18.23960201  38.08043320  1.697228e 12
33       5  5|6  6.669283e 01          NaN           NaN           NaN  55           NaN          NaN  9.211492e 28
34       5   x1  3.351064e 01 1.024452e-03  3.271080e 04  0.000000e 00  55   33.50863540  33.51265125  3.576741e 14
35       5   x2 -1.850497e-01 1.173298e-03 -1.577175e 02  0.000000e 00  55   -0.18734938  -0.18275005  8.310630e-01
36       6  1|2 -3.286871e 01          NaN           NaN           NaN  54           NaN          NaN  5.312531e-15
37       6  2|3 -1.394418e 01          NaN           NaN           NaN  54           NaN          NaN  8.792619e-07
38       6  3|4  1.402239e 01          NaN           NaN           NaN  54           NaN          NaN  1.229831e 06
39       6  4|5  4.198923e 01          NaN           NaN           NaN  54           NaN          NaN  1.720640e 18
40       6  5|6  6.091421e 01          NaN           NaN           NaN  54           NaN          NaN  2.849095e 26
41       6   x1  2.791695e 01 9.151861e 00  3.050412e 00  2.285273e-03  54    9.97930271  45.85459762  1.330998e 12
42       6   x2  6.368881e-04          NaN           NaN           NaN  54           NaN          NaN  1.000637e 00
43       7  1|2 -8.540551e 01          NaN           NaN           NaN  53           NaN          NaN  8.106962e-38
44       7  2|3 -4.981897e 01          NaN           NaN           NaN  53           NaN          NaN  2.311503e-22
45       7  3|4  1.323464e-02          NaN           NaN           NaN  53           NaN          NaN  1.013323e 00
46       7  4|5  4.199212e 01 3.930682e 00  1.068317e 01  1.220384e-26  53   34.28798714  49.69626009  1.725630e 18
47       7  5|6  9.757102e 01          NaN           NaN           NaN  53           NaN          NaN  2.368942e 42
48       7   x1  2.742401e 01          NaN           NaN           NaN  53           NaN          NaN  8.130075e 11
49       7   x2 -1.149872e-01          NaN           NaN           NaN  53           NaN          NaN  8.913776e-01
50       8  1|2 -3.428518e 01 2.418647e 01 -1.417536e 00  1.563264e-01  52  -81.69065068  13.12029594  1.288655e-15
51       8  2|3 -1.468742e 01 2.158542e 01 -6.804326e-01  4.962306e-01  52  -56.99484187  27.61999711  4.181514e-07
52       8  3|4 -2.123523e 00 2.585943e 01 -8.211793e-02  9.345529e-01  52  -52.80800668  48.56096089  1.196095e-01
53       8  4|5  1.428943e 01 2.595126e 01  5.506258e-01  5.818902e-01  52  -36.57503632  65.15390302  1.606283e 06
54       8  5|6  3.811502e 01 1.071360e 00  3.557631e 01 3.257489e-277  52   36.01515820  40.21488826  3.573915e 16
55       8   x1  8.597913e 00 2.399747e 01  3.582841e-01  7.201307e-01  52  -38.43712837  55.63295352  5.420333e 03
56       8   x2 -3.759098e-02          NaN           NaN           NaN  52           NaN          NaN  9.631068e-01
57       9  1|2 -1.381079e 02          NaN           NaN           NaN  51           NaN          NaN  1.048377e-60
58       9  2|3 -5.910938e 01          NaN           NaN           NaN  51           NaN          NaN  2.133649e-26
59       9  3|4 -9.084277e 00          NaN           NaN           NaN  51           NaN          NaN  1.134354e-04
60       9  4|5  5.875811e 01          NaN           NaN           NaN  51           NaN          NaN  3.298542e 25
61       9  5|6  1.564752e 02          NaN           NaN           NaN  51           NaN          NaN  9.043358e 67
62       9   x1  4.528686e 01          NaN           NaN           NaN  51           NaN          NaN  4.654054e 19
63       9   x2 -2.132578e-01 2.314244e-05 -9.215010e 03  0.000000e 00  51   -0.21330318  -0.21321246  8.079478e-01
64      10  1|2 -5.537366e 01 2.398432e 01 -2.308744e 00  2.095778e-02  50 -102.38292260  -8.36439034  8.943892e-25
65      10  2|3 -2.567911e 01 1.644416e 01 -1.561594e 00  1.183836e-01  50  -57.90967366   6.55145288  7.042130e-12
66      10  3|4 -3.561160e 00 1.421535e 01 -2.505152e-01  8.021890e-01  50  -31.42323652  24.30091746  2.840587e-02
67      10  4|5  2.622061e 01 1.739894e 01  1.507024e 00  1.318045e-01  50   -7.88130243  60.32253224  2.440441e 11
68      10  5|6  6.147091e 01          NaN           NaN           NaN  50           NaN          NaN  4.971402e 26
69      10   x1  1.720323e 01          NaN           NaN           NaN  50           NaN          NaN  2.959845e 07
70      10   x2 -6.799849e-02          NaN           NaN           NaN  50           NaN          NaN  9.342619e-01

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