Question

我正在尝试对模式混合模型进行模拟，我需要R中的“估计协方差参数的估计或协方差矩阵的渐近协方差矩阵”（在非结构化下）。
我知道这将通过SAS中的AsyCov和SPSS中的混合模型来实现。
但我不知道为什么asyCov（metaSEM包）的结果与SAS和SPSS输出不一致。

这是我的SAS代码：

proc Mixed data=OutcomeSort method=reml asycov covtest;
  class Subject Rep; 
  model Y= x / s covb; 
  repeated Rep / subject=Subject type=UN r; 
run;

和我的SAS输出：

                               Covariance Parameter Estimates

                                              Standard         Z
           Cov Parm    Subject    Estimate       Error     Value        Pr Z

           UN(1,1)     Subject     50.6700      5.2325      9.68      <.0001
           UN(2,1)     Subject     38.9197      6.1316      6.35      <.0001
           UN(2,2)     Subject      109.57     11.3113      9.69      <.0001
           UN(3,1)     Subject     37.8759      7.1731      5.28      <.0001
           UN(3,2)     Subject     83.7478     11.5030      7.28      <.0001
           UN(3,3)     Subject      162.44     16.7682      9.69      <.0001
           UN(4,1)     Subject     32.3689      8.6577      3.74      0.0002
           UN(4,2)     Subject     85.1421     13.6659      6.23      <.0001
           UN(4,3)     Subject      149.65     18.3647      8.15      <.0001
           UN(4,4)     Subject      247.16     25.7446      9.60      <.0001


                  Asymptotic Covariance Matrix of Estimates

Row  Cov Parm     CovP1     CovP2     CovP3     CovP4     CovP5     CovP6     CovP7     CovP8

  1  UN(1,1)    27.3790   20.9481   15.9859   20.3577   15.5259   15.0775   17.0100   12.8662
  2  UN(2,1)    20.9481   37.5971   45.4151   30.4336   39.4733   33.8181   29.8290   36.7129
  3  UN(2,2)    15.9859   45.4151    127.95   34.7757   97.8981   74.9444   36.0516    100.22
  4  UN(3,1)    20.3577   30.4336   34.7757   51.4533   50.6228   65.5963   47.2107   45.8363
  5  UN(3,2)    15.5259   39.4733   97.8981   50.6228    132.32    145.12   49.1089    126.34
  6  UN(3,3)    15.0775   33.8181   74.9444   65.5963    145.12    281.17   61.4499    134.74
  7  UN(4,1)    17.0100   29.8290   36.0516   47.2107   49.1089   61.4499   74.9564   69.2153
  8  UN(4,2)    12.8662   36.7129    100.22   45.8363    126.34    134.74   69.2153    186.76
  9  UN(4,3)    12.4790   31.8059   76.8862   58.5769    141.49    260.08   79.1265    182.24
 10  UN(4,4)    10.2027   29.7120   78.8488   52.3062    137.66    240.73   91.0933    231.19

                               Row     CovP9      CovP10

                                 1   12.4790     10.2027
                                 2   31.8059     29.7120
                                 3   76.8862     78.8488
                                 4   58.5769     52.3062
                                 5    141.49      137.66
                                 6    260.08      240.73
                                 7   79.1265     91.0933
                                 8    182.24      231.19
                                 9    337.26      401.18
                                10    401.18      662.78

这里是我的R代码：

Std.Err.Cov.par <- matrix (c(5.2325,6.1316,7.1731,8.6577,6.1316, 11.3113,11.5030,13.6659,7.1731,11.5030,16.7682,18.3647,8.6577,13.6659,18.3647,25.7446),4)) 
Std.Err.Cov.parasyCov
       [,1]    [,2]    [,3]    [,4]
[1,] 5.2325  6.1316  7.1731  8.6577
[2,] 6.1316 11.3113 11.5030 13.6659
[3,] 7.1731 11.5030 16.7682 18.3647
[4,] 8.6577 13.6659 18.3647 25.7446

asyCov(Std.Err.Cov.par,n=100,cor.analysis = F)

      x1x1      x2x1      x3x1      x4x1      x2x2      x3x2     x4x2
x1x1 0.5366875 0.6289067 0.7357319 0.8880043 0.7369721 0.8621534 1.040591
x2x1 0.6289067 0.9485741 1.0209965 1.2211370 1.3595297 1.4865150 1.781083
x3x1 0.7357319 1.0209965 1.3642389 1.5504870 1.3825724 1.8164115 2.079731
x4x1 0.8880043 1.2211370 1.5504870 2.0549314 1.6425356 2.0644151 2.706764
x2x2 0.7369721 1.3595297 1.3825724 1.6425356 2.5079958 2.5505029 3.030071
x3x2 0.8621534 1.4865150 1.8164115 2.0644151 2.5505029 3.1558301 3.576670
x4x2 1.0405908 1.7810828 2.0797308 2.7067640 3.0300707 3.5766699 4.684520
x3x3 1.0085977 1.6174143 2.3577429 2.5822236 2.5937308 3.7809433 4.140927
x4x3 1.2173439 1.9368496 2.7139703 3.3682746 3.0814257 4.3163971 5.362250
x4x4 1.4692936 2.3192276 3.1166576 4.3690889 3.6608210 4.9195376 6.896459

     x3x3     x4x3      x4x4
x1x1 1.008598 1.217344  1.469294
x2x1 1.617414 1.936850  2.319228
x3x1 2.357743 2.713970  3.116658
x4x1 2.582224 3.368275  4.369089
x2x2 2.593731 3.081426  3.660821
x3x2 3.780943 4.316397  4.919538
x4x2 4.140927 5.362250  6.896459
x3x3 5.511570 6.036326  6.611043
x4x3 6.036326 7.536536  9.267696
x4x4 6.611043 9.267696 12.991931

Answer 1

我们确实需要一个可重复的示例，但基于此SAS代码：

proc Mixed data=OutcomeSort method=reml asycov covtest;
  class Subject Rep; 
  model Y= x / s covb; 
  * options: covb displays fixed-effect var-cov matrix
  *          s (solution) displays fixed-effect estimates
  repeated Rep / subject=Subject type=UN r; 
  * repeated: "R-side" (residual) effects
  * Rep governs order? of obs (I think this is 
  *    irrelevant for unstructured var-cov matrices?)
  * Subject is grouping variable
  * unstructured var-cov matrix (this is R's default)
  * r: display blocks of R matrix
run;

我建议相应的R代码如下：

library(nlme)
Orthodont$fAge <- factor(Orthodont$age)
Orthodont$nAge <- as.numeric(Orthodont$fAge)
fit1 <- gls(distance~Sex,
    correlation=corSymm(form=~nAge|Subject),
    weights=varIdent(form=~1|fAge),
    data=Orthodont)

然而，这给出了相关参数（corStruct*），方差2-4与方差1（varStruct*）的比率和残差方差（lSigma）的Wald方差;此外，这些是自然量表上参数的变化。

来自Pinheiro和Bates 2000 p。 93 Google books：

lme [和gls]中使用的方法是在计算置信区间时考虑不同的参数化。这个参数化，我们称之为自然参数化，使用标准偏差的对数和相关的广义logits。对于给定的相关参数$ -1＆lt; \ rho＆lt; 1 $，它的通用logit是$ \ log [（1+ \ rho）/（1- \ rho）] $，它可以在实线上取任何值

这些转换的逆转换是exp(lSigma)和(exp(logitRho)-1)/(exp(logitRho)+1)。

从这里开始，你必须使用delta方法将这些碎片和变换重新组合在一起......

＆GT;

提取协方差矩阵以估计R中的协方差参数？

1 个答案: