如果我使用lme包中的R nlme修复线性混合效果模型，如何获得随机效应估计的标准误差？
例如，如果lme给出以下结果：
null.model <- lme(fixed = fev1 ~ 1, data = Data, random = ~ 1 | conwrd)
null.model
Linear mixed-effects model fit by REML
Data: Dat
Log-restricted-likelihood: -887.7505
Fixed: fev1 ~ 1
(Intercept)
15.00424
Random effects:
Formula: ~1 | conwrd
(Intercept) Residual
StdDev: 3.010589 4.130609
Number of Observations: 308
Number of Groups: 11
如何获得2级（拦截）随机效应估计和残留效应估计的标准误差？例如，Stata的mixed命令不仅返回这些估计值，还返回它们的标准误差，以及从这些标准误差导出的置信区间估计值，如下所示。注意：Stata报告方差，而R报告标准偏差，因此上述R模型输出的3.010589和4.130609等于相同数据下面Stata模型输出的9.063698和17.06193的平方根。
mixed fev1 || conwrd: , reml
[SNIP]
Mixed-effects REML regression Number of obs = 308
Group variable: conwrd Number of groups = 11
Obs per group:
min = 25
avg = 28.0
max = 31
Wald chi2(0) = .
Log restricted-likelihood = -887.75054 Prob > chi2 = .
------------------------------------------------------------------------------
fev1 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons | 15.00424 .9378441 16.00 0.000 13.1661 16.84238
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
conwrd: Identity |
var(_cons) | 9.063698 4.324303 3.557919 23.08952
-----------------------------+------------------------------------------------
var(Residual) | 17.06193 1.400088 14.52711 20.03905
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 94.48 Prob >= chibar2 = 0.0000
这些模型使用的假数据
conwrd <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11)
fev1 <- c(23, 18, 22, 19, 16, 13, 15, 17, 16, 23, 25, 18, 21, 20, 17, 21, 17, 19, 20, 21, 21, 20, 17, 15, 15, 17, 13, 14, 19, 9, 9, 11, 10, 19, 13, 16, 12, 10, 9, 11, 11, 9, 11, 10, 12, 9, 7, 8, 11, 14, 16, 13, 9, 10, 9, 8, 16, 14, 13, 9, 11, 9, 12, 12, 13, 11, 17, 16, 17, 19, 23, 24, 28, 26, 22, 25, 19, 24, 22, 23, 20, 27, 12, 12, 10, 9, 10, 9, 4, 4, 4, 8, 9, 6, 8, 6, 9, 11, 9, 8, 9, 11, 14, 17, 11, 12, 13, 10, 10, 9, 14, 13, 15, 15, 20, 12, 13, 6, 15, 16, 12, 7, 10, 7, 15, 17, 15, 18, 20, 18, 16, 21, 22, 16, 12, 15, 11, 13, 8, 17, 19, 20, 16, 20, 18, 12, 11, 8, 12, 11, 11, 16, 17, 16, 17, 17, 14, 20, 24, 24, 24, 23, 20, 21, 25, 13, 14, 14, 15, 21, 16, 17, 15, 14, 11, 8, 11, 13, 14, 13, 15, 13, 12, 15, 17, 19, 16, 14, 16, 16, 14, 14, 11, 17, 7, 10, 16, 12, 18, 18, 15, 11, 13, 9, 12, 11, 13, 9, 11, 16, 15, 15, 18, 24, 28, 24, 24, 27, 23, 23, 21, 23, 23, 22, 15, 10, 11, 13, 17, 15, 13, 10, 15, 13, 11, 13, 18, 18, 15, 22, 18, 19, 18, 20, 17, 19, 18, 14, 13, 10, 7, 11, 14, 19, 18, 15, 14, 9, 14, 15, 14, 19, 18, 14, 10, 17, 23, 25, 26, 24, 24, 26, 25, 25, 20, 20, 20, 20, 17, 15, 14, 12, 11, 11, 11, 11, 9, 10, 11, 13, 13, 17, 16, 11, 11, 11, 12, 19, 15, 13, 15, 15, 12, 9, 12, 10, 8, 8)
Data <- data.frame(conwrd,fev1)