我试图从nlme模型中提取固定效果截距(1.807425)和残差(1.431592)，但结构中似乎没有这些值，尽管它们出现在摘要中。我怎样才能得到它们呢?
library(nlme)
fm2 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1)
summary(fm2)
> summary(fm2)
Linear mixed-effects model fit by REML
Data: Orthodont
AIC BIC logLik
447.5125 460.7823 -218.7563
Random effects:
Formula: ~1 | Subject
(Intercept) Residual
StdDev: 1.807425 1.431592
Fixed effects: distance ~ age + Sex
Value Std.Error DF t-value p-value
(Intercept) 17.706713 0.8339225 80 21.233044 0.0000
age 0.660185 0.0616059 80 10.716263 0.0000
SexFemale -2.321023 0.7614168 25 -3.048294 0.0054
Correlation:
(Intr) age
age -0.813
SexFemale -0.372 0.000
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-3.74889609 -0.55034466 -0.02516628 0.45341781 3.65746539
Number of Observations: 108
Number of Groups: 27
> str(summary(fm2))
List of 22
$ modelStruct :List of 1
..$ reStruct:List of 1
.. ..$ Subject:Classes 'pdLogChol', 'pdSymm', 'pdMat' atomic [1:1] 0.233
.. .. .. ..- attr(*, "formula")=Class 'formula' language ~1
.. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
.. .. .. ..- attr(*, "Dimnames")=List of 2
.. .. .. .. ..$ : chr "(Intercept)"
.. .. .. .. ..$ : chr "(Intercept)"
.. ..- attr(*, "settings")= num [1:4] 1 1 0 4
.. ..- attr(*, "class")= chr "reStruct"
.. ..- attr(*, "plen")= Named int 1
.. .. ..- attr(*, "names")= chr "Subject"
..- attr(*, "settings")= num [1:4] 1 0 1 4
..- attr(*, "class")= chr [1:3] "lmeStructInt" "lmeStruct" "modelStruct"
..- attr(*, "pmap")= logi [1, 1] TRUE
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr "reStruct"
..- attr(*, "fixedSigma")= logi FALSE
$ dims :List of 5
..$ N : int 108
..$ Q : int 1
..$ qvec : Named num [1:3] 1 0 0
.. ..- attr(*, "names")= chr [1:3] "Subject" "" ""
..$ ngrps: Named int [1:3] 27 1 1
.. ..- attr(*, "names")= chr [1:3] "Subject" "X" "y"
..$ ncol : Named num [1:3] 1 3 1
.. ..- attr(*, "names")= chr [1:3] "Subject" "" ""
$ contrasts :List of 1
..$ Sex: num [1:2, 1] 0 1
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr [1:2] "Male" "Female"
.. .. ..$ : chr "Female"
$ coefficients:List of 2
..$ fixed : Named num [1:3] 17.71 0.66 -2.32
.. ..- attr(*, "names")= chr [1:3] "(Intercept)" "age" "SexFemale"
..$ random:List of 1
.. ..$ Subject: num [1:27, 1] -1.7 -1.7 -1.38 -1.16 -1.05 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:27] "M16" "M05" "M02" "M11" ...
.. .. .. ..$ : chr "(Intercept)"
$ varFix : num [1:3, 1:3] 0.6954 -0.0417 -0.2362 -0.0417 0.0038 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:3] "(Intercept)" "age" "SexFemale"
.. ..$ : chr [1:3] "(Intercept)" "age" "SexFemale"
$ sigma : num 1.43
$ apVar : num [1:2, 1:2] 0.026925 -0.000981 -0.000981 0.00625
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:2] "reStruct.Subject" "lSigma"
.. ..$ : chr [1:2] "reStruct.Subject" "lSigma"
..- attr(*, "Pars")= Named num [1:2] 0.592 0.359
.. ..- attr(*, "names")= chr [1:2] "reStruct.Subject" "lSigma"
..- attr(*, "natural")= logi TRUE
$ logLik : num -219
$ numIter : NULL
$ groups :'data.frame': 108 obs. of 1 variable:
..$ Subject: Ord.factor w/ 27 levels "M16"<"M05"<"M02"<..: 15 15 15 15 3 3 3 3 7 7 ...
$ call : language lme.formula(fixed = distance ~ age + Sex, data = Orthodont, random = ~1)
$ terms :Classes 'terms', 'formula' language distance ~ age + Sex
.. ..- attr(*, "variables")= language list(distance, age, Sex)
.. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "distance" "age" "Sex"
.. .. .. ..$ : chr [1:2] "age" "Sex"
.. ..- attr(*, "term.labels")= chr [1:2] "age" "Sex"
.. ..- attr(*, "order")= int [1:2] 1 1
.. ..- attr(*, "intercept")= int 1
.. ..- attr(*, "response")= int 1
.. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
.. ..- attr(*, "predvars")= language list(distance, age, Sex)
.. ..- attr(*, "dataClasses")= Named chr [1:3] "numeric" "numeric" "factor"
.. .. ..- attr(*, "names")= chr [1:3] "distance" "age" "Sex"
$ method : chr "REML"
$ fitted : num [1:108, 1:2] 23 24.3 25.6 26.9 23 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:108] "1" "2" "3" "4" ...
.. ..$ : chr [1:2] "fixed" "Subject"
$ residuals : Named num [1:5] -3.7489 -0.5503 -0.0252 0.4534 3.6575
..- attr(*, "names")= chr [1:5] "Min" "Q1" "Med" "Q3" ...
$ fixDF :List of 2
..$ X : Named num [1:3] 80 80 25
.. ..- attr(*, "names")= chr [1:3] "(Intercept)" "age" "SexFemale"
..$ terms: Named num [1:3] 80 80 25
.. ..- attr(*, "names")= chr [1:3] "(Intercept)" "age" "Sex"
..- attr(*, "assign")=List of 3
.. ..$ (Intercept): int 1
.. ..$ age : int 2
.. ..$ Sex : int 3
..- attr(*, "varFixFact")= num [1:3, 1:3] 0.3741 -0.6777 0.3102 0 0.0616 ...
$ na.action : NULL
$ data :Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and 'data.frame': 108 obs. of 4 variables:
..$ distance: num [1:108] 26 25 29 31 21.5 22.5 23 26.5 23 22.5 ...
..$ age : num [1:108] 8 10 12 14 8 10 12 14 8 10 ...
..$ Subject : Ord.factor w/ 27 levels "M16"<"M05"<"M02"<..: 15 15 15 15 3 3 3 3 7 7 ...
..$ Sex : Factor w/ 2 levels "Male","Female": 1 1 1 1 1 1 1 1 1 1 ...
..- attr(*, "outer")=Class 'formula' language ~Sex
.. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
..- attr(*, "formula")=Class 'formula' language distance ~ age | Subject
.. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
..- attr(*, "labels")=List of 2
.. ..$ x: chr "Age"
.. ..$ y: chr "Distance from pituitary to pterygomaxillary fissure"
..- attr(*, "units")=List of 2
.. ..$ x: chr "(yr)"
.. ..$ y: chr "(mm)"
..- attr(*, "FUN")=function (x)
.. ..- attr(*, "source")= chr "function (x) max(x, na.rm = TRUE)"
..- attr(*, "order.groups")= logi TRUE
$ corFixed : num [1:3, 1:3] 1 -0.813 -0.372 -0.813 1 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:3] "(Intercept)" "age" "SexFemale"
.. ..$ : chr [1:3] "(Intercept)" "age" "SexFemale"
$ tTable : num [1:3, 1:5] 17.7067 0.6602 -2.321 0.8339 0.0616 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:3] "(Intercept)" "age" "SexFemale"
.. ..$ : chr [1:5] "Value" "Std.Error" "DF" "t-value" ...
$ BIC : num 461
$ AIC : num 448
- attr(*, "class")= chr [1:2] "summary.lme" "lme"
- attr(*, "units")=List of 2
..$ x: chr "(yr)"
..$ y: chr "(mm)"
- attr(*, "labels")=List of 2
..$ x: chr "Age"
..$ y: chr "Distance from pituitary to pterygomaxillary fissure"
- attr(*, "verbose")= logi FALSE
- attr(*, "oClass")= chr "lme"
您最好使用提取函数。
分别用fixef(fm2)和resid(fm2)得到固定效果和残差。
然而，这些不是你在问题中陈述的数字，这是RE的sd。要获得RE成分的方差和相关性，你可以使用VarCorr(fm2)(注意VarCorr返回一个字符矩阵)
fm2$sigma