我使用 MICE 包创建了一个包含 7 个插补数据集的 MI 数据集
imputeddata <- mice(distress_tibmi, m=7)
我的数据结构现在为：
..$ id : num [1:342] 4 8 10 11 23 32 40 47 48 56 ...
..$ diagnosis : Factor w/ 2 levels "psychosis","bpd": 1 1 1 1 1 1 1 1 1 1 ...
..$ gender : Factor w/ 2 levels "female","male": 1 2 2 2 2 1 1 1 1 1 ...
..$ distress.time : Factor w/ 2 levels "baseline","post": 1 1 1 1 1 1 1 1 1 1 ...
..$ distress.score: num [1:342] -2.436 -1.242 0.251 -1.54 0.549 ...
..$ depression : num [1:342] 0.332 0.542 1.172 -0.298 1.172 ...
..$ anxiety : num [1:342] -1.898 -0.687 0.87 -0.687 1.043 ...
..$ choice : num [1:342] 6.73 2.18 2 6.45 3.55 ...
$ imp :List of 8
..$ id :'data.frame': 0 obs. of 7 variables:
.. ..$ 1: logi(0)
.. ..$ 2: logi(0)
.. ..$ 3: logi(0)
.. ..$ 4: logi(0)
.. ..$ 5: logi(0)
.. ..$ 6: logi(0)
.. ..$ 7: logi(0)
..$ diagnosis :'data.frame': 0 obs. of 7 variables:
.. ..$ 1: logi(0)
.. ..$ 2: logi(0)
.. ..$ 3: logi(0)
.. ..$ 4: logi(0)
.. ..$ 5: logi(0)
.. ..$ 6: logi(0)
.. ..$ 7: logi(0)
..$ gender :'data.frame': 0 obs. of 7 variables:
.. ..$ 1: logi(0)
.. ..$ 2: logi(0)
.. ..$ 3: logi(0)
.. ..$ 4: logi(0)
.. ..$ 5: logi(0)
.. ..$ 6: logi(0)
.. ..$ 7: logi(0)
..$ distress.time :'data.frame': 0 obs. of 7 variables:
.. ..$ 1: logi(0)
.. ..$ 2: logi(0)
.. ..$ 3: logi(0)
.. ..$ 4: logi(0)
.. ..$ 5: logi(0)
.. ..$ 6: logi(0)
.. ..$ 7: logi(0)
..$ distress.score:'data.frame': 59 obs. of 7 variables:
.. ..$ 1: num [1:59] -0.6808 -0.6448 -1.658 -0.0293 -0.3463 ...
.. ..$ 2: num [1:59] 1.2736 0.2507 -0.0478 -0.6448 1.2736 ...
.. ..$ 3: num [1:59] -0.681 0.848 -1.658 1.274 0.251 ...
.. ..$ 4: num [1:59] -1.3322 -0.0478 -0.6808 -0.355 -2.4358 ...
.. ..$ 5: num [1:59] -1.3322 -0.355 -4.8239 -0.6448 -0.0293 ...
.. ..$ 6: num [1:59] -1.3322 0.5493 -0.0293 -2.6352 0.8478 ...
.. ..$ 7: num [1:59] 0.5493 0.2507 1.1463 -0.0478 1.2736 ...
..$ depression :'data.frame': 24 obs. of 7 variables:
.. ..$ 1: num [1:24] -0.0882 -0.5084 -1.2966 0.542 -2.1891 ...
.. ..$ 2: num [1:24] 0.332 0.255 1.592 0.752 0.945 ...
.. ..$ 3: num [1:24] -2.159 0.332 -0.262 0.962 1.382 ...
.. ..$ 4: num [1:24] -0.2621 -0.0897 -1.7689 1.1172 0.7724 ...
.. ..$ 5: num [1:24] 0.122 -2.159 -2.399 1.462 -2.189 ...
.. ..$ 6: num [1:24] -0.298 -0.434 -0.607 1.172 0.962 ...
.. ..$ 7: num [1:24] 0.6 1.29 1.635 0.542 0.428 ...
..$ anxiety :'data.frame': 10 obs. of 7 variables:
.. ..$ 1: num [1:10] 0.909 -1.379 1.389 -1.268 -0.598 ...
.. ..$ 2: num [1:10] 1.0433 -1.3789 -0.0955 -0.7655 -0.598 ...
.. ..$ 3: num [1:10] 1.0771 -1.8979 -0.0955 -0.5138 0.0052 ...
.. ..$ 4: num [1:10] -0.598 -1.603 0.9095 -2.608 -0.0955 ...
.. ..$ 5: num [1:10] 0.742 0.2395 -1.7249 -2.1055 -0.0955 ...
.. ..$ 6: num [1:10] 1.412 -0.86 1.389 -2.608 0.575 ...
.. ..$ 7: num [1:10] 1.245 -1.033 0.909 0.909 -1.033 ...
..$ choice :'data.frame': 22 obs. of 7 variables:
.. ..$ 1: num [1:22] 4.55 3.91 7.09 4.27 3.55 ...
.. ..$ 2: num [1:22] 8.09 5.09 5.36 4.91 4.45 ...
.. ..$ 3: num [1:22] 4.27 7.09 3.91 3.91 7.09 ...
.. ..$ 4: num [1:22] 5.82 6.27 7 6.82 4.73 ...
.. ..$ 5: num [1:22] 6.18 5.36 5.36 3.18 3.18 ...
.. ..$ 6: num [1:22] 6.18 6.73 4.73 4.73 5 ...
.. ..$ 7: num [1:22] 5.45 7.09 7.45 3.18 4.91 ...
$ m : num 7
$ where : logi [1:342, 1:8] FALSE FALSE FALSE FALSE FALSE FALSE ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:342] "1" "2" "3" "4" ...
.. ..$ : chr [1:8] "id" "diagnosis" "gender" "distress.time" ...
$ blocks :List of 8
..$ id : chr "id"
..$ diagnosis : chr "diagnosis"
..$ gender : chr "gender"
..$ distress.time : chr "distress.time"
..$ distress.score: chr "distress.score"
..$ depression : chr "depression"
..$ anxiety : chr "anxiety"
..$ choice : chr "choice"
..- attr(*, "calltype")= Named chr [1:8] "type" "type" "type" "type" ...
.. ..- attr(*, "names")= chr [1:8] "id" "diagnosis" "gender" "distress.time" ...
$ call : language mice(data = distress_tibmi, m = 7)
$ nmis : Named int [1:8] 0 0 0 0 59 24 10 22
..- attr(*, "names")= chr [1:8] "id" "diagnosis" "gender" "distress.time" ...
$ method : Named chr [1:8] "" "" "" "" ...
..- attr(*, "names")= chr [1:8] "id" "diagnosis" "gender" "distress.time" ...
$ predictorMatrix: num [1:8, 1:8] 0 1 1 1 1 1 1 1 1 0 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:8] "id" "diagnosis" "gender" "distress.time" ...
.. ..$ : chr [1:8] "id" "diagnosis" "gender" "distress.time" ...
$ visitSequence : chr [1:8] "id" "diagnosis" "gender" "distress.time" ...
$ formulas :List of 8
..$ id :Class 'formula' language id ~ 0 + diagnosis + gender + distress.time + distress.score + depression + ...
.. .. ..- attr(*, ".Environment")=<environment: 0x7ff907cd9d00>
..$ diagnosis :Class 'formula' language diagnosis ~ 0 + id + gender + distress.time + distress.score + depression + ...
.. .. ..- attr(*, ".Environment")=<environment: 0x7ff907cd9d00>
..$ gender :Class 'formula' language gender ~ 0 + id + diagnosis + distress.time + distress.score + depression + ...
.. .. ..- attr(*, ".Environment")=<environment: 0x7ff907cd9d00>
..$ distress.time :Class 'formula' language distress.time ~ 0 + id + diagnosis + gender + distress.score + depression + ...
.. .. ..- attr(*, ".Environment")=<environment: 0x7ff907cd9d00>
..$ distress.score:Class 'formula' language distress.score ~ 0 + id + diagnosis + gender + distress.time + depression + ...
.. .. ..- attr(*, ".Environment")=<environment: 0x7ff907cd9d00>
..$ depression :Class 'formula' language depression ~ 0 + id + diagnosis + gender + distress.time + distress.score + ...
.. .. ..- attr(*, ".Environment")=<environment: 0x7ff907cd9d00>
..$ anxiety :Class 'formula' language anxiety ~ 0 + id + diagnosis + gender + distress.time + distress.score + ...
.. .. ..- attr(*, ".Environment")=<environment: 0x7ff907cd9d00>
..$ choice :Class 'formula' language choice ~ 0 + id + diagnosis + gender + distress.time + distress.score + ...
.. .. ..- attr(*, ".Environment")=<environment: 0x7ff907cd9d00>
$ post : Named chr [1:8] "" "" "" "" ...
..- attr(*, "names")= chr [1:8] "id" "diagnosis" "gender" "distress.time" ...
$ blots :List of 8
..$ id : list()
..$ diagnosis : list()
..$ gender : list()
..$ distress.time : list()
..$ distress.score: list()
..$ depression : list()
..$ anxiety : list()
..$ choice : list()
$ seed : logi NA
$ iteration : num 5
$ lastSeedValue : int [1:626] 10403 331 -1243825859 461242975 2057104913 -837414599 -54045022 1529270132 -105270003 -1459771035 ...
$ chainMean : num [1:8, 1:5, 1:7] NaN NaN NaN NaN -0.727 ...
..- attr(*, "dimnames")=List of 3
.. ..$ : chr [1:8] "id" "diagnosis" "gender" "distress.time" ...
.. ..$ : chr [1:5] "1" "2" "3" "4" ...
.. ..$ : chr [1:7] "Chain 1" "Chain 2" "Chain 3" "Chain 4" ...
$ chainVar : num [1:8, 1:5, 1:7] NA NA NA NA 2.26 ...
..- attr(*, "dimnames")=List of 3
.. ..$ : chr [1:8] "id" "diagnosis" "gender" "distress.time" ...
.. ..$ : chr [1:5] "1" "2" "3" "4" ...
.. ..$ : chr [1:7] "Chain 1" "Chain 2" "Chain 3" "Chain 4" ...
$ loggedEvents : NULL
$ version :Classes 'package_version', 'numeric_version' hidden list of 1
..$ : int [1:3] 3 9 0
$ date : Date[1:1], format: ...
- attr(*, "class")= chr "mids"
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id diagnosis gender
Min. : 1.00 psychosis:250 female:196
1st Qu.: 76.75 bpd : 92 male :146
Median :198.00
Mean :215.66
3rd Qu.:337.00
Max. :514.00
distress.time distress.score depression
baseline:171 Min. :-4.8239 Min. :-2.39920
post :171 1st Qu.:-0.6808 1st Qu.:-0.76410
Median :-0.0293 Median : 0.08280
Mean :-0.3083 Mean :-0.06085
3rd Qu.: 0.6221 3rd Qu.: 0.77240
Max. : 1.2736 Max. : 1.80690
NA's :59 NA's :24
anxiety choice
Min. :-2.6080 Min. :0.0909
1st Qu.:-0.9330 1st Qu.:2.4545
Median :-0.0955 Median :4.0454
Mean :-0.1397 Mean :3.8903
3rd Qu.: 0.8702 3rd Qu.:5.1136
Max. : 1.7471 Max. :8.0909
NA's :10 NA's :22
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<dbl>
4
<dbl>
5
<dbl>
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<dbl>
21 -0.6808 1.2736 -0.6808 -1.3322 -1.3322 -1.3322 0.5493
34 -0.6448 0.2507 0.8478 -0.0478 -0.3550 0.5493 0.2507
48 -1.6580 -0.0478 -1.6580 -0.6808 -4.8239 -0.0293 1.1463
141 -0.0293 -0.6448 1.2736 -0.3550 -0.6448 -2.6352 -0.0478
143 -0.3463 1.2736 0.2507 -2.4358 -0.0293 0.8478 1.2736
180 1.1463 -1.0065 -2.3094 -3.6124 -0.6448 -1.5403 -1.0065
181 -0.0293 -0.6808 -0.6808 -3.9381 -0.3463 -1.3322 0.2964
182 1.2736 -0.3463 0.9479 -0.0478 0.9479 -0.3463 1.1463
197 -0.3550 -0.0293 -0.6808 -0.3550 -1.3322 -4.8239 -0.6448
208 0.6221 0.2507 -0.6808 -0.3550 -0.6448 0.6221 -0.6448
1-10 of 59 rows
我用插补数据集创建了一个 lm 并使用 pool(( 对其进行了总结
distressmodel <- with(data = imputeddata, exp = lm(distress.score ~ distress.time * diagnosis))
summary(mice::pool(distressmodel), conf.int = TRUE, conf.level = 0.95 )
但是现在我想获取模型的 3 F 类型值，但此代码不起作用
car::Anova(mice::pool(distressmodel), type = 3)
它会产生以下错误消息：
UseMethod("vcov"( 中的错误：没有适用于类 "c('mipo'， 'data.frame'("的对象 'vcov' 的方法
我还想获得模型的边际效应(例如，仅查看分组变量的一个级别的效应，即诊断(，我已经在我的完整案例分析中成功完成了，但是这段代码：
summary(margins(distressmodel, data = subset(imputeddata, diagnosis == "bpd", type = "response")))
产生此错误
subset_datlist中的错误(datlist = x，子集 = 子集，选择 = 选择，：找不到对象"诊断">
有没有人对代码的更改或让 car：：anova 或边距 (( 包使用 MI 数据集的方法有任何建议？(最好能够合并结果
with(data, exp)程序可用于将统计检验/模型应用于多个插补输出(mipo(，前提是它们允许用coef方法提取估计值，并使用vcov提取方差-协方差矩阵。后者似乎不适用于您使用的函数car::Anova。
幸运的是，有miceadds包，它提供了进行和汇集其他统计测试的程序。miceadds::mi.anova似乎完全符合您的要求：
miceadds::mi.anova(imputeddata, distress.score ~ distress.time * diagnosis, type=3)
然而，我不确定边际影响。通常，您可以进行更多的编码，并将任何统计过程分别应用于每个插补样本。然后，您可以使用pool.scalar函数将其池化。此方法还为您提供合并统计量的插补内方差、插补间方差和总方差估计值。(如果需要，您可以对 0 的差异进行基本的 t 检验。
这种方法依赖于统计数据的正态分布，或者依赖于它们可以转换为正态分布的指标。(Stef van Buuren 给出了一个统计数据列表，这些统计数据可以很容易地转换、池化和反向转换，见表 5.2(所以你想要的边际手段应该是可能的，对吧？
我不知道您使用的margins函数(它来自什么包？但是，如果您想获得边际手段并自己汇集它们，则此方法如下：
# transform your mids into a long-format data frame
imputed_l <- mice::complete(imputeddata, action="long")
nimp <- imputed_l$m #number of imputations for convenience
# create vectors to contain the marginal effects and their SEs from all seven imputations
mm_all <- vector("numeric", nimp)
mmse_all <- mm_all
# get marginal means and SEs for all imputations
for (i in 1:nimp) {
mm_all[i] <- Expression_producing_marginal_mean(..., data = subset(imputed_l, .imp=i) )
mmse_all[i] <- Expression_producing_SE(..., data = subset(imputed_l, .imp=i) )
}
# pool them (the U argument should be variances, so square the SEs)
mm_pool <- pool.scalar(Q=mm_all, U=mmse_all^2, n=nrow(imputed_l)/nimp)
mm_pool$qbar #marginal mean aggregated across imputations
sqrt(mm_pool$t) #SE of marginal mean (based on within- and between-imputations variance)