我想从lmermod对象
require(lme4)
(fm1 <- lmer(Yield ~ 1|Batch, Dyestuff))
这会产生
Linear mixed model fit by REML ['lmerMod']
Formula: Yield ~ 1 | Batch
Data: Dyestuff
REML criterion at convergence: 319.6543
Random effects:
Groups Name Std.Dev.
Batch (Intercept) 42.00
Residual 49.51
Number of obs: 30, groups: Batch, 6
Fixed Effects:
(Intercept)
1527
特别是，我想提取2个随机效应的标准偏差，即42.00和49.51。我想可能有一个内置的方法来做到这一点，但我无法快速找到它，所以我认为我应该能够通过使用str(fm1)找到它，这产生了这个：
Formal class 'lmerMod' [package "lme4"] with 13 slots
..@ resp :Reference class 'lmerResp' [package "lme4"] with 9 fields
.. ..$ Ptr :<externalptr>
.. ..$ mu : num [1:30] 1510 1510 1510 1510 1510 ...
.. ..$ offset : num [1:30] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ sqrtXwt: num [1:30] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ sqrtrwt: num [1:30] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ weights: num [1:30] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ wtres : num [1:30] 35.1 -69.9 -69.9 10.1 70.1 ...
.. ..$ y : num [1:30] 1545 1440 1440 1520 1580 ...
.. ..$ REML : int 1
.. ..and 26 methods, of which 14 are possibly relevant:
.. .. allInfo, copy#envRefClass, initialize, initialize#lmResp,
.. .. initializePtr, initializePtr#lmResp, objective, ptr, ptr#lmResp,
.. .. setOffset, setResp, setWeights, updateMu, wrss
..@ Gp : int [1:2] 0 6
..@ call : language lmer(formula = Yield ~ 1 | Batch, data = Dyestuff)
..@ frame :'data.frame': 30 obs. of 2 variables:
.. ..$ Yield: num [1:30] 1545 1440 1440 1520 1580 ...
.. ..$ Batch: Factor w/ 6 levels "A","B","C","D",..: 1 1 1 1 1 2 2 2 2 2 ...
.. ..- attr(*, "terms")=Classes 'terms', 'formula' length 3 Yield ~ 1 + Batch
.. .. .. ..- attr(*, "variables")= language list(Yield, Batch)
.. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
.. .. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. .. ..$ : chr [1:2] "Yield" "Batch"
.. .. .. .. .. ..$ : chr "Batch"
.. .. .. ..- attr(*, "term.labels")= chr "Batch"
.. .. .. ..- attr(*, "order")= int 1
.. .. .. ..- attr(*, "intercept")= int 1
.. .. .. ..- attr(*, "response")= int 1
.. .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
.. .. .. ..- attr(*, "predvars")= language list(Yield, Batch)
.. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "factor"
.. .. .. .. ..- attr(*, "names")= chr [1:2] "Yield" "Batch"
.. .. .. ..- attr(*, "predvars.fixed")= language list(Yield)
.. ..- attr(*, "formula")=Class 'formula' length 3 Yield ~ 1 | Batch
.. .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
..@ flist :List of 1
.. ..$ Batch: Factor w/ 6 levels "A","B","C","D",..: 1 1 1 1 1 2 2 2 2 2 ...
.. ..- attr(*, "assign")= int 1
..@ cnms :List of 1
.. ..$ Batch: chr "(Intercept)"
..@ lower : num 0
..@ theta : num 0.848
..@ beta : num 1527
..@ u : num [1:6] -20.755 0.461 33.669 -27.212 66.877 ...
..@ devcomp:List of 2
.. ..$ cmp : Named num [1:10] 9.15 1.88 61495.41 9590.84 71086.25 ...
.. .. ..- attr(*, "names")= chr [1:10] "ldL2" "ldRX2" "wrss" "ussq" ...
.. ..$ dims: Named int [1:12] 30 30 1 29 1 6 1 1 0 1 ...
.. .. ..- attr(*, "names")= chr [1:12] "N" "n" "p" "nmp" ...
..@ pp :Reference class 'merPredD' [package "lme4"] with 18 fields
.. ..$ Lambdat:Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
.. .. .. ..@ i : int [1:6] 0 1 2 3 4 5
.. .. .. ..@ p : int [1:7] 0 1 2 3 4 5 6
.. .. .. ..@ Dim : int [1:2] 6 6
.. .. .. ..@ Dimnames:List of 2
.. .. .. .. ..$ : NULL
.. .. .. .. ..$ : NULL
.. .. .. ..@ x : num [1:6] 0.848 0.848 0.848 0.848 0.848 ...
.. .. .. ..@ factors : list()
.. ..$ LamtUt :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
.. .. .. ..@ i : int [1:30] 0 0 0 0 0 1 1 1 1 1 ...
.. .. .. ..@ p : int [1:31] 0 1 2 3 4 5 6 7 8 9 ...
.. .. .. ..@ Dim : int [1:2] 6 30
.. .. .. ..@ Dimnames:List of 2
.. .. .. .. ..$ : NULL
.. .. .. .. ..$ : NULL
.. .. .. ..@ x : num [1:30] 0.848 0.848 0.848 0.848 0.848 ...
.. .. .. ..@ factors : list()
.. ..$ Lind : int [1:6] 1 1 1 1 1 1
.. ..$ Ptr :<externalptr>
.. ..$ RZX : num [1:6, 1] 1.98 1.98 1.98 1.98 1.98 ...
.. ..$ Ut :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
.. .. .. ..@ i : int [1:30] 0 0 0 0 0 1 1 1 1 1 ...
.. .. .. ..@ p : int [1:31] 0 1 2 3 4 5 6 7 8 9 ...
.. .. .. ..@ Dim : int [1:2] 6 30
.. .. .. ..@ Dimnames:List of 2
.. .. .. .. ..$ : chr [1:6] "A" "B" "C" "D" ...
.. .. .. .. ..$ : NULL
.. .. .. ..@ x : num [1:30] 1 1 1 1 1 1 1 1 1 1 ...
.. .. .. ..@ factors : list()
.. ..$ Utr : num [1:6] 6384 6481 6634 6354 6787 ...
.. ..$ V : num [1:30, 1] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ VtV : num [1, 1] 30
.. ..$ Vtr : num 45825
.. ..$ X : num [1:30, 1] 1 1 1 1 1 1 1 1 1 1 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:30] "1" "2" "3" "4" ...
.. .. .. ..$ : chr "(Intercept)"
.. .. ..- attr(*, "assign")= int 0
.. ..$ Xwts : num [1:30] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ Zt :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
.. .. .. ..@ i : int [1:30] 0 0 0 0 0 1 1 1 1 1 ...
.. .. .. ..@ p : int [1:31] 0 1 2 3 4 5 6 7 8 9 ...
.. .. .. ..@ Dim : int [1:2] 6 30
.. .. .. ..@ Dimnames:List of 2
.. .. .. .. ..$ : chr [1:6] "A" "B" "C" "D" ...
.. .. .. .. ..$ : NULL
.. .. .. ..@ x : num [1:30] 1 1 1 1 1 1 1 1 1 1 ...
.. .. .. ..@ factors : list()
.. ..$ beta0 : num 0
.. ..$ delb : num 1527
.. ..$ delu : num [1:6] -20.755 0.461 33.669 -27.212 66.877 ...
.. ..$ theta : num 0.848
.. ..$ u0 : num [1:6] 0 0 0 0 0 0
.. ..and 42 methods, of which 30 are possibly relevant:
.. .. b, beta, CcNumer, copy#envRefClass, initialize, initializePtr,
.. .. installPars, L, ldL2, ldRX2, linPred, P, ptr, RX, RXdiag, RXi,
.. .. setBeta0, setDelb, setDelu, setTheta, solve, solveU, sqrL, u, unsc,
.. .. updateDecomp, updateL, updateLamtUt, updateRes, updateXwts
..@ optinfo:List of 7
.. ..$ optimizer: chr "bobyqa"
.. ..$ control :List of 1
.. .. ..$ iprint: int 0
.. ..$ derivs :List of 2
.. .. ..$ gradient: num 1.61e-07
.. .. ..$ Hessian : num [1, 1] 14.1
.. ..$ conv :List of 2
.. .. ..$ opt : int 0
.. .. ..$ lme4: list()
.. ..$ feval : int 16
.. ..$ warnings : list()
.. ..$ val : num 0.848
......但这让我更加聪明。
答案 0 :(得分：5)
lme4提供VarCorr用于提取方差和相关性组件：
varcor <- VarCorr(fm1)
as.data.frame(varcor)
# grp var1 var2 vcov sdcor
#1 Batch (Intercept) <NA> 1764.05 42.0006
#2 Residual <NA> <NA> 2451.25 49.5101