我有一个包含患者数据的数据集。一些患者在医院中多次住院，但观察结果是独立的（我尝试了一个多水平模型，但对这些数据来说是不可能的）我创建了一个数据集，对于每个有多次住院的病人，只随机选择一次住院。我为这些患者创建了100个随机住院数据集。我的因变量是计数变量，零膨胀负二项模型拟合最佳。我已经设法对每个数据集运行了回归模型（数据集由变量“sample”标识），但我不知道如何获得所有这100个回归的合并结果，我希望获得每个预测变量的计数模型和零膨胀模型的合并结果，我使用：library(dplyr); library(tidyverse); library(pscl); library(broom); library(jtools); library(mice)pool函数来自mice。
我创建的组合数据集如下所示：
set.seed(12345)
Combined_randcase <- bind_rows(replicate(100, cohort_1 %>% group_by(patient) %>%
slice_sample(n=1, replace = TRUE), simplify=F), .id="sample")
Combined_randcase <- data.frame(as.list(Combined_randcase))我在每个数据集上运行ZINB回归模型，按“样本”分组，如下所示（使用broom包）：
regr_comb_randcase.zeroinfl = Combined_randcase %>%
nest_by(sample) %>%
mutate(model = list(zeroinfl(formula = cm_number ~ after_wm + age + gender_male + ref_mode_police + ref_lg_invol + ref_reas_selfharm + ref_reas_aggrpers + comm_limited + duration_days + diagnosis_personality + diagnosis_psychosis + diagnosis_mania + diagnosis_substance + intoxication | age + gender_male + ref_mode_police + ref_lg_invol + ref_reas_selfharm + ref_reas_aggrpers + comm_limited + duration_days + diagnosis_personality + diagnosis_psychosis + diagnosis_mania + diagnosis_substance + intoxication, data = cohort_1, na.action = na.exclude, dist = "negbin")))
%>%
summarise(tidy(model))这就是我试图获得汇总结果的方法：
models.zeroinfl <- regr_comb_randcase.zeroinfl$model
pool_results.zeroinfl <- pool(regr_comb_randcase.zeroinfl)当运行第二行时，我得到这个错误：
Error: No tidy method for objects of class character对于另一个逻辑回归模型，我成功地做到了这一点：
regr_comb_randcase.log = Combined_randcase %>%
group_by(sample) %>%
do(model = glm(cm ~ after_wm + age + gender_male +ref_mode_police + ref_lg_invol + ref_reas_selfharm + ref_reas_aggrpers + comm_limited + duration_days + diagnosis_personality + diagnosis_psychosis + diagnosis_mania + diagnosis_substance + intoxication, data = ., family = binomial()))
models <- regr_comb_randcase.log$model
pool_results <- pool(models)
summary(pool_results)dput(cohort_1_example)的输出（我的数据集的缩短版本）用于再现性：
structure(list(case = c("20001879", "20009253", "20003748", "20002321",
"20001662", "1910967", "20008058", "20010686", "20010938", "20009508",
"20002307", "20010105", "210098181", "21009818", "210100261",
"21010026", "21000865", "21002199", "1906803", "1907642", "20008274",
"21000858", "21004557", "1910669", "21004451", "21000202", "21000812",
"21001006", "21001143", "21001423", "1906820", "21000448", "21002128",
"21002666", "21003560", "1907070", "20011121", "1907614", "20002748",
"20010645", "21001363", "1908906", "1910981", "1905926", "21002429",
"21004264", "20011209", "20010442", "20009977", "1906382", "1909409",
"1908904", "1910516", "20001534", "20011201", "1907432", "1908332",
"1906356", "20011026", "20008206", "20000809", "1910664", "1908673",
"1907610", "1911046", "20008505", "20009385", "21000530", "1909620",
"1909730", "1910988", "20009899", "1907282", "1906671", "20007870",
"1910749", "20010782", "20009808", "20003311", "1910722", "1910529",
"1906638", "1906861", "1906956", "1910743", "20002057", "21000891",
"20010349", "20008503", "1906093", "1910662", "20008093", "20010683",
"20008787", "20003631", "20007796", "20008089", "21004141", "20010177",
"20001316", "1909809", "20001875", "20009552", "20001443", "21000419",
"20003106", "1909773", "21004600", "20008105", "21002070", "1908245",
"1909860", "21004209", "21003022", "20003151", "20011037", "21001966",
"20009902", "1906202", "1910009", "1910777", "20010294", "1910072"
), patient = c("10", "11", "100", "100", "101", "102", "103",
"105", "106", "107", "108", "11", "11", "11", "11", "11", "1000",
"1001", "1002", "1002", "1003", "1003", "1004", "1005", "1005",
"1006", "1008", "1009", "1009", "1009", "1011", "1012", "1013",
"1013", "1013", "1014", "1016", "1017", "1018", "1020", "1020",
"1021", "1022", "1023", "1026", "1026", "1029", "1030", "1033",
"1035", "1036", "1037", "1037", "1037", "1039", "1041", "1041",
"1042", "1042", "1043", "1044", "1045", "1046", "1047", "1048",
"1049", "1050", "1053", "1054", "1056", "1056", "1057", "1058",
"1060", "1061", "1064", "1064", "1064", "1065", "1066", "1067",
"1067", "1067", "1067", "1069", "1071", "1072", "1073", "1074",
"1075", "1075", "1076", "1077", "1078", "1079", "1080", "1081",
"1082", "1083", "1086", "1087", "1087", "1088", "1089", "1089",
"1090", "1091", "1091", "1092", "1093", "1094", "1094", "1095",
"1096", "1098", "1098", "1098", "1099", "1048", "1048", "1021",
"1018", "1011"), cm = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0), cm_number = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 3, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 5, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 2, 2, 0, 0, 1, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0), total_cm_duration = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 165.000000003492, 0, 0, 0, 0, 0, 0,
0, 0, 174.999999994179, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 259.999999998836,
720, 0, 0, 0, 0, 0, 60.0000000069849, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 815.000000005821, 0, 0, 10865.0000000023,
0, 0, 0, 0, 0, 0, 420.000000006985, 0, 0, 0, 0, 0, 200.000000002328,
0, 0, 0, 0, 0, 0, 239.999999996508, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1084.99999999534, 0, 0, 145.000000001164, 0, 0, 789.999999997672,
435.000000003492, 0, 0, 60.0000000069849, 0, 0, 0, 0, 775.000000001164,
0, 0, 0, 0, 0, 0, 0), after_wm = c(0, 1, 0, 0, 0, 0, 1, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,
0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1,
0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1,
1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1,
0, 1, 1, 1, 0, 0, 0, 1, 0), age = c(26, 27, 53, 53, 26, 28, 30,
57, 39, 50, 49, 27, 28, 28, 27, 27, 89, 18, 22, 22, 21, 21, 58,
35, 36, 63, 44, 35, 35, 35, 25, 24, 36, 36, 36, 62, 50, 21, 55,
23, 23, 44, 53, 71, 39, 39, 79, 47, 81, 43, 39, 21, 22, 22, 79,
22, 22, 33, 35, 86, 27, 42, 20, 30, 25, 22, 26, 62, 54, 46, 46,
46, 79, 39, 21, 63, 64, 64, 31, 59, 70, 70, 70, 70, 49, 37, 49,
63, 74, 38, 39, 74, 50, 72, 61, 80, 51, 45, 67, 45, 76, 76, 61,
30, 31, 35, 48, 49, 45, 30, 76, 76, 20, 18, 20, 20, 21, 51, 24,
24, 45, 55, 25), gender_male = c(1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0,
1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,
0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,
1, 1, 1, 0, 0, 0, 0, 0), ref_mode_police = c(0, 0, 0, 0, 0, 0,
1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,
1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0), ref_lg_invol = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0,
1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0), ref_reas_selfharm = c(1,
1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,
1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0,
1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0,
0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1,
1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1,
0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0), ref_reas_aggrpers = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0), comm_limited = c(0,
0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), duration_days = c(41,
1, 2, 42, 3, 46, 3, 8, 1, 1, 21, 64, 25, 2, 25, 2, 22, 26, 7,
29, 101, 119, 153, 2, 2, 51, 10, 2, 6, 49, 83, 5, 1, 8, 1, 36,
71, 1, 7, 9, 166, 41, 2, 76, 12, 1, 25, 40, 4, 0, 2, 28, 1, 3,
49, 29, 54, 95, 119, 29, 28, 26, 43, 1, 15, 121, 22, 28, 73,
13, 39, 1, 119, 14, 73, 18, 124, 32, 2, 120, 67, 2, 2, 8, 29,
27, 34, 32, 112, 6, 8, 38, 118, 24, 38, 20, 2, 1, 2, 9, 1, 21,
42, 57, 49, 1, 1, 1, 35, 2, 45, 23, 64, 29, 2, 6, 56, 0, 5, 3,
58, 51, 2), diagnosis_personality = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,
1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), diagnosis_psychosis = c(1, 1,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0,
0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1,
1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0,
1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,
1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1), diagnosis_mania = c(0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), diagnosis_substance = c(0,
0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1,
0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1), intoxication = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1)), row.names = c(NA,
-123L), class = c("tbl_df", "tbl", "data.frame"))我可以找到用线性模型和逻辑模型来做这个的信息，但是没有用零膨胀的negbin模型来做，也许这就是为什么tidy不起作用的原因吧，任何帮助都是非常感谢的。
1条答案
按热度按时间suzh9iv81#
我们可以拟合混合/多水平模型的数据与单例组;主要的约束是总体上是否有足够的信息使组间方差的估计可行。
在主流软件包中，
lme4可以拟合LMM、逻辑GLMM和负二项混合模型（尽管速度有点慢，而且不是零膨胀混合模型，需要大量工作：glmmTMB可以处理上述所有内容。brms可以做glmmTMB可以做的任何事情，加上厨房Flume，但速度较慢（因为贝叶斯MCMC），可能需要您深入了解贝叶斯/MCMC采样的杂草。下面的例子使用了你的数据子集，模型大大简化了。你的样本数据有86个病人，61个单例，123个总观察值。这似乎几乎足以拟合简化的模型（下图）;我们在ZINB拟合（零膨胀项收敛于零概率，NB离散参数收敛于泊松分布）和逻辑拟合（奇异拟合，即患者间方差收敛于零）中确实遇到了一些典型的数据不足问题。如果您的完整数据集很大，这些问题发生的可能性要小得多......
机器的第一部分（除了加载包之外）是装饰性的，以避免太多重复的代码，并使它更容易看到所有模型中包含的公共预测器集。