R中如何提取回归模型预测值的标准差？ 高人指点，不甚感激！

回归分析过程及结果：
> Data= read.table ("WUS_Neq_M_R_Dr100_13_23.txt", header=TRUE)
> Event=Data[,1]
> R=Data[,2]
> M=Data[,3]
> logNeq=Data[,4]
> #Step 2 Perform nls regression named as‘pr_model’
> pr_model<-nls(logNeq~(c1+c2*R^1.65+c3*R^0.65+c4*M^3), start=list(c1=1,c2=1,c3=1,c4=1))
> #Step 3 Display the nls regression result
> summary(pr_model)
Formula: logNeq ~ (c1 + c2 * R^1.65 + c3 * R^0.65 + c4 * M^3)
Parameters:
     Estimate Std. Error t value Pr(>|t|)   
c1  2.300e+00  1.316e-01  17.477  < 2e-16 ***
c2 -2.843e-04  6.403e-05  -4.440 1.28e-05 ***
c3  7.290e-02  9.972e-03   7.310 2.57e-12 ***
c4  2.433e-03  3.731e-04   6.521 3.07e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4779 on 292 degrees of freedom
Number of iterations to convergence: 1
Achieved convergence tolerance: 3.542e-06
> local({pkg <- select.list(sort(.packages(all.available = TRUE)))
+ if(nchar(pkg)) library(pkg, character.only=TRUE)})
> Data= read.table ("WUS_Neq_M_R_Dr100_13_23.txt ", header=TRUE)
> Event=Data[,1]
> R=Data[,2]
> M=Data[,3]
> logNeq=Data[,4]
> #nlme regression named as ‘model’
> model<-nlme(logNeq~(c1+c2*R^1.65+c3*R^0.65+c4*M^3), fixed=c1+c2+c3+c4~1, random=c1~1|Event, start=list(c1=1,c2=1,c3=1,c4=1,fixed=c(2.300e+00, -2.843e-04, 7.290e-02, 2.433e-03)))
> #Step 6 Display the nlme regression result
> summary(model)
Nonlinear mixed-effects model fit by maximum likelihood
Model: logNeq ~ (c1 + c2 * R^1.65 + c3 * R^0.65 + c4 * M^3)
Data: NULL
       AIC      BIC    logLik
  401.5201 423.6622 -194.7600
Random effects:
Formula: c1 ~ 1 | Event
               c1  Residual
StdDev: 0.1629438 0.4507812   ---------------》这个并非是回归模型预测值的标准差，原本以为是，后来觉得有问题。
Fixed effects: c1 + c2 + c3 + c4 ~ 1
        Value  Std.Error  DF   t-value p-value
c1  2.2727993 0.17096778 260 13.293729       0
c2 -0.0002874 0.00006754 260 -4.256044       0
c3  0.0712592 0.01102730 260  6.462070       0
c4  0.0024827 0.00053829 260  4.612292       0
Correlation:
   c1     c2     c3   
c2  0.442              
c3 -0.441 -0.897      
c4 -0.860 -0.134  0.021
Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max
-3.24302794 -0.57308362 -0.03858352  0.61675317  2.78688244
Number of Observations: 296
Number of Groups: 33






本文来自: 人大经济论坛 详细出处参考：http://www.pinggu.org/bbs/viewthread.php?tid=351568&page=1