Retrieving Results from the lmermod Object

Question

I would like to extract some results from the lmermod object

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

It creates

 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 

In particular, I would like to extract the standard deviations of 2 random effects, i.e. 42.00 and 49.51. I suppose there might be an inline method for this, but I could not find it quickly, so I thought I should find it using str(fm1) , which produces this:

 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 

... but that leaves me nothing more wise.

Answer 1

lme4 supplies VarCorr for the extraction of variance and correlation components:

 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