【发布时间】：2014-11-14 06:43:09
【问题描述】：
这是我的数据框中的摘录，代表纵向研究的结果（A 是在两个时间点测量的结果参数）：
wide<-structure(list(ID = c(9000296L, 9001104L, 9001400L, 9001695L,
9001897L, 9002316L), BMI = c(29.8, 30.7, 23.5, 28.6, 25.9,
25.1),B.1 = c(100, 70.83, 100, 89.29, 100, 92.86), A.5 = c(100,
NA, 92.86, NA, 100, 89.29)), .Names = c("ID", "BMI", "A.1",
"A.5"), class = "data.frame", row.names = c(2L, 5L, 6L,
7L, 8L, 10L))
wide
ID BMI A.1 A.5
2 9000296 29.8 100.0 100.0
5 9001104 30.7 70.8 NA
6 9001400 23.5 100.0 92.9
7 9001695 28.6 89.3 NA
8 9001897 25.9 100.0 100.0
10 9002316 25.1 92.9 89.3
如您所见，A1 和 A5 之间存在相关性，这在纵向研究中应该如此：
library (psych)
corr.test (wide [,c(3,4)] )
Call:corr.test(x = wide[, c(3, 4)])
Correlation matrix
A.1 A.5
A.1 1.00 0.78
A.5 0.78 1.00
然后我将我的数据转换为长格式
long<- reshape (wide, varying = c(3,4), direction="long")
long
ID BMI time A id
1.1 9000296 29.8 1 100.0 1
2.1 9001104 30.7 1 70.8 2
3.1 9001400 23.5 1 100.0 3
4.1 9001695 28.6 1 89.3 4
5.1 9001897 25.9 1 100.0 5
6.1 9002316 25.1 1 92.9 6
1.5 9000296 29.8 5 100.0 1
2.5 9001104 30.7 5 NA 2
3.5 9001400 23.5 5 92.9 3
4.5 9001695 28.6 5 NA 4
5.5 9001897 25.9 5 100.0 5
6.5 9002316 25.1 5 89.3 6
然后我尝试首先使用独立的相关结构来拟合gee模型：
library (gee)
model1<- gee(A~time+BMI, id=ID, corstr= "independence", data = long)
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
(Intercept) time BMI
122.389 0.508 -1.127
summary (model1)
GEE: GENERALIZED LINEAR MODELS FOR DEPENDENT DATA
gee S-function, version 4.13 modified 98/01/27 (1998)
Model:
Link: Identity
Variance to Mean Relation: Gaussian
Correlation Structure: Independent
Call:
gee(formula = A ~ time + BMI, id = ID, data = long, corstr = "independence")
Summary of Residuals:
Min 1Q Median 3Q Max
-17.46 -4.62 1.11 5.79 10.69
Coefficients:
Estimate Naive S.E. Naive z Robust S.E. Robust z
(Intercept) 122.389 34.18 3.580 31.00 3.949
time 0.508 1.60 0.317 1.12 0.453
BMI -1.127 1.23 -0.919 1.23 -0.913
Estimated Scale Parameter: 93.6
Number of Iterations: 1
Working Correlation
[,1] [,2]
[1,] 1 0
[2,] 0 0
并使用可交换的关联结构：
model2<- gee(A~time+BMI, id=ID, corstr= "exchangeable", data = long)
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
(Intercept) time BMI
122.389 0.508 -1.127
summary (model2)
GEE: GENERALIZED LINEAR MODELS FOR DEPENDENT DATA
gee S-function, version 4.13 modified 98/01/27 (1998)
Model:
Link: Identity
Variance to Mean Relation: Gaussian
Correlation Structure: Exchangeable
Call:
gee(formula = A ~ time + BMI, id = ID, data = long, corstr = "exchangeable")
Summary of Residuals:
Min 1Q Median 3Q Max
-17.46 -4.62 1.11 5.79 10.69
Coefficients:
Estimate Naive S.E. Naive z Robust S.E. Robust z
(Intercept) 122.389 34.18 3.580 31.00 3.949
time 0.508 1.60 0.317 1.12 0.453
BMI -1.127 1.23 -0.919 1.23 -0.913
Estimated Scale Parameter: 93.6
Number of Iterations: 1
Working Correlation
[,1] [,2]
[1,] 1 0
[2,] 0 0
如您所见，尽管在 gee 模型中使用了不同的相关结构，但输出是相同的。在这两种情况下，相关矩阵中的相关都为零。
在我的实际数据中，我有更多的观察结果和时间点，但也存在显着的受试者内相关性。然而，所有 gee 模型（也使用不同的因变量）在其相关矩阵中也具有零相关性，并且更改 corstr 参数不会导致模型输出发生变化。 这一切似乎都很奇怪。 你能否建议我做错了什么。
【问题讨论】：
标签： r statistics