1

我正在研究纵向/重复测量多级模型（MLM）。通常，对于随时间变化的协变量（在我的情况下为“每周总收入/1000”），您将计算变量的以人为中心的版本（即从个人每周收入的平均值中减去人年收入响应）所有所述人的时间点）。但是，这可能会导致偏差（参见此处），因此更好（更通用）的方法是围绕每个个体的回归线（碰巧，回归的残差用于此目的）。

因此，我需要计算以下回归，但对于每个人（大约 10,000 个人，有 25,000 个观察值）：

lm(Weekly_Gross_Pay_Main_Job~nYear, data=df)

然后，真正关键的部分是我需要将残差提取到我的主数据集中的单独列中，并与每个人匹配。这些残差将取代我的组均值中心变量（这又将用于我的 MLM）。

这是使用我为组均值居中所拥有的功能的可能起点。如果这可以更新以适应每个人的残差输出的回归，那么这将是理想的（如果不是，那么我对其他方法持开放态度）：

#Group mean-centering a variable. Relevant for L1 variables only.
gmc = function(variable, group){
  return(ave(variable, group, FUN = function(x){x - mean(x)}))
}

df$Weekly_Gross_Pay_Main_Jobgmc <- gmc(df$Weekly_Gross_Pay_Main_Job, df$Person_ID)

长格式的数据提取（Person_ID人在哪里，nYear时间，Weekly_Gross_Pay_Main_Job周收入/1000，Weekly_Gross_Pay_Main_Jobgmc组均值居中版本）：

structure(list(Person_ID = c(100003L, 100003L, 100003L, 100006L, 
100006L, 100006L, 100006L, 100010L, 100010L, 100010L, 100010L, 
100010L, 100010L, 100011L, 100014L, 100014L, 100014L, 100014L, 
100014L, 100016L, 100018L, 100018L, 100018L, 100018L, 100018L, 
100018L, 100018L, 100018L, 100018L, 100020L, 100020L, 100020L, 
100020L, 100020L, 100020L, 100020L, 100020L, 100020L, 100021L, 
100021L, 100024L, 100024L, 100024L, 100024L, 100024L, 100024L, 
100024L, 100024L, 100024L, 100024L, 100025L, 100025L, 100025L, 
100025L, 100025L, 100025L, 100025L, 100025L, 100027L, 100027L, 
100027L, 100027L, 100029L, 100029L, 100029L, 100029L, 100029L, 
100031L, 100031L, 100031L, 100032L, 100032L, 100032L, 100033L, 
100033L, 100033L, 100033L, 100033L, 100033L, 100034L, 100034L, 
100034L, 100037L, 100037L, 100037L, 100037L, 100037L, 100037L, 
100037L, 100044L, 100044L, 100044L, 100044L, 100044L, 100044L, 
100044L, 100045L, 100045L, 100045L, 100045L), nYear = c(5L, 6L, 
7L, 2L, 3L, 4L, 6L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 5L, 6L, 7L, 
8L, 9L, 5L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 
5L, 6L, 7L, 8L, 9L, 1L, 2L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
13L, 14L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 4L, 5L, 6L, 1L, 2L, 3L, 3L, 4L, 5L, 6L, 7L, 8L, 
2L, 3L, 5L, 5L, 6L, 7L, 8L, 9L, 11L, 13L, 2L, 3L, 4L, 6L, 7L, 
8L, 9L, 4L, 5L, 6L, 7L), Weekly_Gross_Pay_Main_Job = c(0, 0.58, 
0.35, 0.035, 0.65, 0.195, 0.43, 0, 0, 0, 0, 0, 0, 0.12, 1.653, 
0.967, 1.742, 1.323, 0, 0.709, 0.155, 0.431, 0.235, 0.17, 0.285, 
0.357, 0.28, 0.335, 0.375, 0.111, 0.333, 0.582, 0.882, 0.85, 
0.944, 1.615, 1.615, 1.35, 0.168, 0.08, 0, 0, 0, 0, 0, 0, 0, 
0.134, 0.737, 0, 0.02, 0.372, 0.1, 0.014, 0.307, 0.39, 0.671, 
0.5, 0.278, 0.32, 0.425, 0.4, 0.57, 0.917, 0.75, 0.402, 0.437, 
0.211, 0.537, 0.54, 0.135, 0.15, 0.65, 0.324, 0.399, 0.497, 0.67, 
0.825, 0.825, 0.25, 0.319, 0.35, 0.885, 0.941, 0.975, 0.975, 
1.02, 1.096, 1.148, 0.1, 0.11, 0.413, 0.477, 0.578, 0.686, 0.686, 
0.511, 0.578, 0.8, 0.75), Weekly_Gross_Pay_Main_Jobgmc = c(-0.31, 
0.27, 0.04, -0.2925, 0.3225, -0.1325, 0.1025, 0, 0, 0, 0, 0, 
0, 0, 0.516, -0.17, 0.605, 0.186, -1.137, 0, -0.136444444444444, 
0.139555555555556, -0.0564444444444445, -0.121444444444444, -0.00644444444444447, 
0.0655555555555555, -0.0114444444444444, 0.0435555555555556, 
0.0835555555555555, -0.809222222222222, -0.587222222222222, -0.338222222222222, 
-0.0382222222222223, -0.0702222222222223, 0.0237777777777777, 
0.694777777777778, 0.694777777777778, 0.429777777777778, 0.044, 
-0.044, -0.0871, -0.0871, -0.0871, -0.0871, -0.0871, -0.0871, 
-0.0871, 0.0469, 0.6499, -0.0871, -0.27675, 0.07525, -0.19675, 
-0.28275, 0.01025, 0.09325, 0.37425, 0.20325, -0.07775, -0.03575, 
0.06925, 0.04425, -0.0452, 0.3018, 0.1348, -0.2132, -0.1782, 
-0.218333333333333, 0.107666666666667, 0.110666666666667, -0.176666666666667, 
-0.161666666666667, 0.338333333333333, -0.266, -0.191, -0.093, 
0.0800000000000001, 0.235, 0.235, -0.0563333333333333, 0.0126666666666667, 
0.0436666666666666, -0.120714285714286, -0.0647142857142858, 
-0.0307142857142858, -0.0307142857142858, 0.0142857142857142, 
0.0902857142857143, 0.142285714285714, -0.335714285714286, -0.325714285714286, 
-0.0227142857142857, 0.0412857142857143, 0.142285714285714, 0.250285714285714, 
0.250285714285714, -0.1368, -0.0698000000000001, 0.1522, 0.1022
)), row.names = c(NA, 100L), class = "data.frame")
4

2 回答 2

2

不确定我是否没看错，这可能是一个非常幼稚的答案，没有抓住重点，但“残差”不起作用。这是一个线性混合效应模型，其中包含我周围的一些数据

    some.model<-lme(DV~IV, random=~1|Id, data=df)
    head(residuals(some.model))
       7            7           24           24           32           32 
    -0.054135825 -0.054135825  0.064271638  0.064271638 -0.001975424 -0.001975424

如果您真的想将其放入旁边带有 idnumber 的列中，则需要更多步骤。它可能可以一步完成，但我真的很糟糕。

   extra.column<-residuals(some.model)
   extra.column.id<-names(residuals(some.model))
   extra.column<-residuals(some.model)
   cbind(extra.column,extra.column.id)
   extra.column            extra.column.id
   7    "-0.0541358252373243"   "7"            
   7    "-0.0541358252373243"   "7"            
   24   "0.0642716380035857"    "24"           
   24   "0.0642716380035857"    "24"           
   32   "-0.0019754241828096"   "32"           
   32   "-0.0019754241828096"   "32"

抱歉，如果这不是您要查找的内容，请查看残差命令。

于 2018-11-22T09:39:15.590 回答
1

这是我最终的做法：

#Before you begin, time needs to be grand-mean centered.
df$nYearmc <- df$nYear-mean(df$nYear, na.rm=TRUE)

#Now to regress the time-varying covariate onto grand-mean centered time and complete the process.

#First, create a group called `by_person`.
df <- tidyr::unite(df, Person_Year, c(Person_ID, nYearmc), remove=FALSE)
by_Person <- dplyr::group_by(df, Person_ID)

#Second, regress the time-varying covariate onto the newly created grand-mean centered time variable and merge with the main data frame.
df.Weekly_Gross_Pay_Main_Job <- dplyr::do(by_Person, augment(lm(Weekly_Gross_Pay_Main_Job~nYearmc, data=.)))
df.Weekly_Gross_Pay_Main_Job <- tidyr::unite(df.Weekly_Gross_Pay_Main_Job, Person_Year, c(Person_ID, nYearmc), remove=FALSE)
df <- merge(df, df.Weekly_Gross_Pay_Main_Job, by="Person_Year")

#Third, copy over the required columns (renaming them would be more efficient, but either way).
df$RegResGrossPay <- df$.resid

#Fourth, do an optional tidy up.
colnames(df)[colnames(df)=="Person_ID.x"] <- "Person_ID"
colnames(df)[colnames(df)=="nYearmc.x"] <- "nYearmc"
colnames(df)[colnames(df)=="Weekly_Gross_Pay_Main_Job.x"] <- "Weekly_Gross_Pay_Main_Job"
df$Person_ID.y <- NULL
df$nYearmc.y <- NULL
df$Weekly_Gross_Pay_Main_Job.y <- NULL
df$.fitted <- NULL
df$.se.fit <- NULL
df$.resid <- NULL
df$.hat <- NULL
df$.sigma <- NULL
df$.cooksd <- NULL
df$.std.resid <- NULL
df.Weekly_Gross_Pay_Main_Job <- NULL

#Fifth, generate plots of the variables you need.
ggplot(df, aes(nYearmc, RegResGrossPay))+geom_line(aes(group=Person_ID), alpha =1/3)+geom_smooth(method="lm",se=FALSE)
于 2018-11-27T06:17:45.840 回答