Извлечение результатов из `lmermod` объекта

Question

Я хотел бы извлечь некоторые результаты из lmermod объекта

require(lme4) 
(fm1 <- lmer(Yield ~ 1|Batch, Dyestuff)) 

Это производит

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 

В частности, я хотел бы извлечь из стандартных devations 2 случайных эффекта, то есть 42.00 и 49.51. Я думаю, может быть встроенный способ сделать это, но я не мог найти его быстро, так что я думал, что я должен быть в состоянии найти его с помощью str(fm1), которая производит это:

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 

... но это не оставляет мне ничего более мудрым.

Answer 1

lme4 поставки VarCorr для извлечения дисперсии и корреляции компонентов:

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