R 使用先前估计的值重新启动混合效应模型估计

我用lme4包中的lmer()来估计混合效应模型.这很有效，但现在我想运行一个固定迭代次数的估计过程，然后通过指定由上一个估计过程计算的起始值来恢复该过程.

根据?lmer的帮助，通过设置参数，这是可能的:

• start-这些是新的开始值，根据帮助，用户可以从安装的型号中提取插槽ST中的值，并使用这些值，即使用x@ST
• maxiter-作为命名参数提供给control

例如，假设我想用iris数据拟合一个lme，你可以试试这个:

library(lme4)

# Fit model with limited number of iterations

frm <- "Sepal.Length ~ Sepal.Width | Species"

x <- lmer(frm, data=iris, 
          verbose=TRUE, control=list(maxIter=1), model=FALSE)

# Capture starting values for next set of iterations
start <- list(ST=x@ST)

# Update model
twoStep <-  lmer(frm, data=iris, 
          verbose=TRUE, control=list(maxIter=100), model=TRUE, 
          start=start)

这很管用.看看输出，其中first column是REML，即随机效应最大可能性.请特别注意，模型2中的REML从模型1终止的地方开始:

> x <- lmer(frm, data=iris, 
+           verbose=TRUE, control=list(maxIter=1), model=FALSE)
  0:     264.60572: 0.230940 0.0747853  0.00000
  1:     204.22878: 0.518239  1.01025 0.205835
  1:     204.22878: 0.518239  1.01025 0.205835

> # Capture starting values for next set of iterations
> start <- list(ST=x@ST)

> # Update model
> twoStep <-  lmer(frm, data=iris, 
+           verbose=TRUE, control=list(maxIter=100), model=TRUE, 
+           start=start)
  0:     204.22878: 0.518239  1.01025 0.205835
  1:     201.51667: 0.610272  2.00277 0.286049
  2:     201.46706: 0.849203  1.94906 0.358809
  3:     201.44614: 0.932371  1.88581 0.482423
  4:     201.39421:  1.00909  1.71078 0.871824
  5:     201.36543:  1.00643  1.60453  1.01663
  6:     201.31066:  1.00208  1.35520  1.27524
  7:     201.28458:  1.08227  1.22335  1.35147
  8:     201.24330:  1.50333 0.679759  1.31698
  9:     201.11881:  1.95760 0.329767 0.936047

然而，当我有一个不同的值maxIters时，这不再有效:

x <- lmer(frm, data=iris, 
          verbose=TRUE, control=list(maxIter=3), model=FALSE)
start <- list(ST=x@ST)
twoStep <-  lmer(frm, data=iris, 
                 verbose=TRUE, control=list(maxIter=100), model=TRUE, 
                 start=start)

请注意，the REML value restarts at 264，即开头:

> x <- lmer(frm, data=iris, 
+           verbose=TRUE, control=list(maxIter=3), model=FALSE)
  0:     264.60572: 0.230940 0.0747853  0.00000
  1:     204.22878: 0.518238  1.01025 0.205835
  2:     201.94075:  0.00000  1.51757 -1.18259
  3:     201.71473:  0.00000  1.69036 -1.89803
  3:     201.71473:  0.00000  1.69036 -1.89803

> # Capture starting values for next set of iterations
> start <- list(ST=x@ST)

> # Update model
> twoStep <-  lmer(frm, data=iris, 
+           verbose=TRUE, control=list(maxIter=100), model=TRUE, 
+           start=start)
  0:     264.60572: 0.230940 0.0747853  0.00000
  1:     204.22878: 0.518238  1.01025 0.205835
  2:     201.94075:  0.00000  1.51757 -1.18259
  3:     201.71473:  0.00000  1.69036 -1.89803
  4:     201.64641:  0.00000  1.82159 -2.44144
  5:     201.63698:  0.00000  1.88282 -2.69497
  6:     201.63649:  0.00000  1.89924 -2.76298
  7:     201.63649: 4.22291e-08  1.90086 -2.76969
  8:     201.63649: 4.22291e-08  1.90086 -2.76969

Question: How can I reliably restart 100 with start values obtained from a previously fitted model?

会议信息:

packageVersion("lme4")
[1] ‘0.999999.2’