getVarCov.RdExtract the variance-covariance matrix from a fitted model, such as a mixed-effects model.
getVarCov(obj, ...)
# S3 method for class 'lme'
getVarCov(obj, individuals,
type = c("random.effects", "conditional", "marginal"), ...)
# S3 method for class 'gls'
getVarCov(obj, individual = 1, ...)A fitted model. Methods are available for models fit by
lme and by gls
For models fit by lme a vector of
levels of the grouping factor can be specified for the conditional
or marginal variance-covariance matrices.
For models fit by gls the only type of
variance-covariance matrix provided is the marginal
variance-covariance of the responses by group. The
optional argument individual specifies the group of responses.
For models fit by lme the type
argument specifies the type of variance-covariance matrix, either
"random.effects" for the random-effects variance-covariance
(the default), or "conditional" for the conditional.
variance-covariance of the responses or "marginal" for the
the marginal variance-covariance of the responses.
Optional arguments for some methods, as described above
A variance-covariance matrix or a list of variance-covariance matrices.
fm1 <- lme(distance ~ age, data = Orthodont, subset = Sex == "Female")
getVarCov(fm1)
#> Random effects variance covariance matrix
#> (Intercept) age
#> (Intercept) 3.55020 -0.107490
#> age -0.10749 0.025898
#> Standard Deviations: 1.8842 0.16093
getVarCov(fm1, individuals = "F01", type = "marginal")
#> Subject F01
#> Marginal variance covariance matrix
#> 1 2 3 4
#> 1 3.9344 3.6872 3.8866 4.0860
#> 2 3.6872 4.4368 4.2931 4.5961
#> 3 3.8866 4.2931 5.1463 5.1063
#> 4 4.0860 4.5961 5.1063 6.0630
#> Standard Deviations: 1.9835 2.1064 2.2685 2.4623
getVarCov(fm1, type = "conditional")
#> Subject F01
#> Conditional variance covariance matrix
#> 1 2 3 4
#> 1 0.44659 0.00000 0.00000 0.00000
#> 2 0.00000 0.44659 0.00000 0.00000
#> 3 0.00000 0.00000 0.44659 0.00000
#> 4 0.00000 0.00000 0.00000 0.44659
#> Standard Deviations: 0.66827 0.66827 0.66827 0.66827
fm2 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary,
correlation = corAR1(form = ~ 1 | Mare))
getVarCov(fm2)
#> Marginal variance covariance matrix
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 21.3090000 16.050000 12.089000 9.105600 6.858400 5.165800 3.890900
#> [2,] 16.0500000 21.309000 16.050000 12.089000 9.105600 6.858400 5.165800
#> [3,] 12.0890000 16.050000 21.309000 16.050000 12.089000 9.105600 6.858400
#> [4,] 9.1056000 12.089000 16.050000 21.309000 16.050000 12.089000 9.105600
#> [5,] 6.8584000 9.105600 12.089000 16.050000 21.309000 16.050000 12.089000
#> [6,] 5.1658000 6.858400 9.105600 12.089000 16.050000 21.309000 16.050000
#> [7,] 3.8909000 5.165800 6.858400 9.105600 12.089000 16.050000 21.309000
#> [8,] 2.9307000 3.890900 5.165800 6.858400 9.105600 12.089000 16.050000
#> [9,] 2.2074000 2.930700 3.890900 5.165800 6.858400 9.105600 12.089000
#> [10,] 1.6626000 2.207400 2.930700 3.890900 5.165800 6.858400 9.105600
#> [11,] 1.2523000 1.662600 2.207400 2.930700 3.890900 5.165800 6.858400
#> [12,] 0.9432500 1.252300 1.662600 2.207400 2.930700 3.890900 5.165800
#> [13,] 0.7104600 0.943250 1.252300 1.662600 2.207400 2.930700 3.890900
#> [14,] 0.5351300 0.710460 0.943250 1.252300 1.662600 2.207400 2.930700
#> [15,] 0.4030600 0.535130 0.710460 0.943250 1.252300 1.662600 2.207400
#> [16,] 0.3035900 0.403060 0.535130 0.710460 0.943250 1.252300 1.662600
#> [17,] 0.2286700 0.303590 0.403060 0.535130 0.710460 0.943250 1.252300
#> [18,] 0.1722300 0.228670 0.303590 0.403060 0.535130 0.710460 0.943250
#> [19,] 0.1297300 0.172230 0.228670 0.303590 0.403060 0.535130 0.710460
#> [20,] 0.0977120 0.129730 0.172230 0.228670 0.303590 0.403060 0.535130
#> [21,] 0.0735970 0.097712 0.129730 0.172230 0.228670 0.303590 0.403060
#> [22,] 0.0554340 0.073597 0.097712 0.129730 0.172230 0.228670 0.303590
#> [23,] 0.0417530 0.055434 0.073597 0.097712 0.129730 0.172230 0.228670
#> [24,] 0.0314490 0.041753 0.055434 0.073597 0.097712 0.129730 0.172230
#> [25,] 0.0236880 0.031449 0.041753 0.055434 0.073597 0.097712 0.129730
#> [26,] 0.0178420 0.023688 0.031449 0.041753 0.055434 0.073597 0.097712
#> [27,] 0.0134380 0.017842 0.023688 0.031449 0.041753 0.055434 0.073597
#> [28,] 0.0101220 0.013438 0.017842 0.023688 0.031449 0.041753 0.055434
#> [29,] 0.0076239 0.010122 0.013438 0.017842 0.023688 0.031449 0.041753
#> [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] 2.930700 2.207400 1.662600 1.25230 0.94325 0.71046 0.53513
#> [2,] 3.890900 2.930700 2.207400 1.66260 1.25230 0.94325 0.71046
#> [3,] 5.165800 3.890900 2.930700 2.20740 1.66260 1.25230 0.94325
#> [4,] 6.858400 5.165800 3.890900 2.93070 2.20740 1.66260 1.25230
#> [5,] 9.105600 6.858400 5.165800 3.89090 2.93070 2.20740 1.66260
#> [6,] 12.089000 9.105600 6.858400 5.16580 3.89090 2.93070 2.20740
#> [7,] 16.050000 12.089000 9.105600 6.85840 5.16580 3.89090 2.93070
#> [8,] 21.309000 16.050000 12.089000 9.10560 6.85840 5.16580 3.89090
#> [9,] 16.050000 21.309000 16.050000 12.08900 9.10560 6.85840 5.16580
#> [10,] 12.089000 16.050000 21.309000 16.05000 12.08900 9.10560 6.85840
#> [11,] 9.105600 12.089000 16.050000 21.30900 16.05000 12.08900 9.10560
#> [12,] 6.858400 9.105600 12.089000 16.05000 21.30900 16.05000 12.08900
#> [13,] 5.165800 6.858400 9.105600 12.08900 16.05000 21.30900 16.05000
#> [14,] 3.890900 5.165800 6.858400 9.10560 12.08900 16.05000 21.30900
#> [15,] 2.930700 3.890900 5.165800 6.85840 9.10560 12.08900 16.05000
#> [16,] 2.207400 2.930700 3.890900 5.16580 6.85840 9.10560 12.08900
#> [17,] 1.662600 2.207400 2.930700 3.89090 5.16580 6.85840 9.10560
#> [18,] 1.252300 1.662600 2.207400 2.93070 3.89090 5.16580 6.85840
#> [19,] 0.943250 1.252300 1.662600 2.20740 2.93070 3.89090 5.16580
#> [20,] 0.710460 0.943250 1.252300 1.66260 2.20740 2.93070 3.89090
#> [21,] 0.535130 0.710460 0.943250 1.25230 1.66260 2.20740 2.93070
#> [22,] 0.403060 0.535130 0.710460 0.94325 1.25230 1.66260 2.20740
#> [23,] 0.303590 0.403060 0.535130 0.71046 0.94325 1.25230 1.66260
#> [24,] 0.228670 0.303590 0.403060 0.53513 0.71046 0.94325 1.25230
#> [25,] 0.172230 0.228670 0.303590 0.40306 0.53513 0.71046 0.94325
#> [26,] 0.129730 0.172230 0.228670 0.30359 0.40306 0.53513 0.71046
#> [27,] 0.097712 0.129730 0.172230 0.22867 0.30359 0.40306 0.53513
#> [28,] 0.073597 0.097712 0.129730 0.17223 0.22867 0.30359 0.40306
#> [29,] 0.055434 0.073597 0.097712 0.12973 0.17223 0.22867 0.30359
#> [,15] [,16] [,17] [,18] [,19] [,20] [,21]
#> [1,] 0.40306 0.30359 0.22867 0.17223 0.12973 0.097712 0.073597
#> [2,] 0.53513 0.40306 0.30359 0.22867 0.17223 0.129730 0.097712
#> [3,] 0.71046 0.53513 0.40306 0.30359 0.22867 0.172230 0.129730
#> [4,] 0.94325 0.71046 0.53513 0.40306 0.30359 0.228670 0.172230
#> [5,] 1.25230 0.94325 0.71046 0.53513 0.40306 0.303590 0.228670
#> [6,] 1.66260 1.25230 0.94325 0.71046 0.53513 0.403060 0.303590
#> [7,] 2.20740 1.66260 1.25230 0.94325 0.71046 0.535130 0.403060
#> [8,] 2.93070 2.20740 1.66260 1.25230 0.94325 0.710460 0.535130
#> [9,] 3.89090 2.93070 2.20740 1.66260 1.25230 0.943250 0.710460
#> [10,] 5.16580 3.89090 2.93070 2.20740 1.66260 1.252300 0.943250
#> [11,] 6.85840 5.16580 3.89090 2.93070 2.20740 1.662600 1.252300
#> [12,] 9.10560 6.85840 5.16580 3.89090 2.93070 2.207400 1.662600
#> [13,] 12.08900 9.10560 6.85840 5.16580 3.89090 2.930700 2.207400
#> [14,] 16.05000 12.08900 9.10560 6.85840 5.16580 3.890900 2.930700
#> [15,] 21.30900 16.05000 12.08900 9.10560 6.85840 5.165800 3.890900
#> [16,] 16.05000 21.30900 16.05000 12.08900 9.10560 6.858400 5.165800
#> [17,] 12.08900 16.05000 21.30900 16.05000 12.08900 9.105600 6.858400
#> [18,] 9.10560 12.08900 16.05000 21.30900 16.05000 12.089000 9.105600
#> [19,] 6.85840 9.10560 12.08900 16.05000 21.30900 16.050000 12.089000
#> [20,] 5.16580 6.85840 9.10560 12.08900 16.05000 21.309000 16.050000
#> [21,] 3.89090 5.16580 6.85840 9.10560 12.08900 16.050000 21.309000
#> [22,] 2.93070 3.89090 5.16580 6.85840 9.10560 12.089000 16.050000
#> [23,] 2.20740 2.93070 3.89090 5.16580 6.85840 9.105600 12.089000
#> [24,] 1.66260 2.20740 2.93070 3.89090 5.16580 6.858400 9.105600
#> [25,] 1.25230 1.66260 2.20740 2.93070 3.89090 5.165800 6.858400
#> [26,] 0.94325 1.25230 1.66260 2.20740 2.93070 3.890900 5.165800
#> [27,] 0.71046 0.94325 1.25230 1.66260 2.20740 2.930700 3.890900
#> [28,] 0.53513 0.71046 0.94325 1.25230 1.66260 2.207400 2.930700
#> [29,] 0.40306 0.53513 0.71046 0.94325 1.25230 1.662600 2.207400
#> [,22] [,23] [,24] [,25] [,26] [,27] [,28]
#> [1,] 0.055434 0.041753 0.031449 0.023688 0.017842 0.013438 0.010122
#> [2,] 0.073597 0.055434 0.041753 0.031449 0.023688 0.017842 0.013438
#> [3,] 0.097712 0.073597 0.055434 0.041753 0.031449 0.023688 0.017842
#> [4,] 0.129730 0.097712 0.073597 0.055434 0.041753 0.031449 0.023688
#> [5,] 0.172230 0.129730 0.097712 0.073597 0.055434 0.041753 0.031449
#> [6,] 0.228670 0.172230 0.129730 0.097712 0.073597 0.055434 0.041753
#> [7,] 0.303590 0.228670 0.172230 0.129730 0.097712 0.073597 0.055434
#> [8,] 0.403060 0.303590 0.228670 0.172230 0.129730 0.097712 0.073597
#> [9,] 0.535130 0.403060 0.303590 0.228670 0.172230 0.129730 0.097712
#> [10,] 0.710460 0.535130 0.403060 0.303590 0.228670 0.172230 0.129730
#> [11,] 0.943250 0.710460 0.535130 0.403060 0.303590 0.228670 0.172230
#> [12,] 1.252300 0.943250 0.710460 0.535130 0.403060 0.303590 0.228670
#> [13,] 1.662600 1.252300 0.943250 0.710460 0.535130 0.403060 0.303590
#> [14,] 2.207400 1.662600 1.252300 0.943250 0.710460 0.535130 0.403060
#> [15,] 2.930700 2.207400 1.662600 1.252300 0.943250 0.710460 0.535130
#> [16,] 3.890900 2.930700 2.207400 1.662600 1.252300 0.943250 0.710460
#> [17,] 5.165800 3.890900 2.930700 2.207400 1.662600 1.252300 0.943250
#> [18,] 6.858400 5.165800 3.890900 2.930700 2.207400 1.662600 1.252300
#> [19,] 9.105600 6.858400 5.165800 3.890900 2.930700 2.207400 1.662600
#> [20,] 12.089000 9.105600 6.858400 5.165800 3.890900 2.930700 2.207400
#> [21,] 16.050000 12.089000 9.105600 6.858400 5.165800 3.890900 2.930700
#> [22,] 21.309000 16.050000 12.089000 9.105600 6.858400 5.165800 3.890900
#> [23,] 16.050000 21.309000 16.050000 12.089000 9.105600 6.858400 5.165800
#> [24,] 12.089000 16.050000 21.309000 16.050000 12.089000 9.105600 6.858400
#> [25,] 9.105600 12.089000 16.050000 21.309000 16.050000 12.089000 9.105600
#> [26,] 6.858400 9.105600 12.089000 16.050000 21.309000 16.050000 12.089000
#> [27,] 5.165800 6.858400 9.105600 12.089000 16.050000 21.309000 16.050000
#> [28,] 3.890900 5.165800 6.858400 9.105600 12.089000 16.050000 21.309000
#> [29,] 2.930700 3.890900 5.165800 6.858400 9.105600 12.089000 16.050000
#> [,29]
#> [1,] 0.0076239
#> [2,] 0.0101220
#> [3,] 0.0134380
#> [4,] 0.0178420
#> [5,] 0.0236880
#> [6,] 0.0314490
#> [7,] 0.0417530
#> [8,] 0.0554340
#> [9,] 0.0735970
#> [10,] 0.0977120
#> [11,] 0.1297300
#> [12,] 0.1722300
#> [13,] 0.2286700
#> [14,] 0.3035900
#> [15,] 0.4030600
#> [16,] 0.5351300
#> [17,] 0.7104600
#> [18,] 0.9432500
#> [19,] 1.2523000
#> [20,] 1.6626000
#> [21,] 2.2074000
#> [22,] 2.9307000
#> [23,] 3.8909000
#> [24,] 5.1658000
#> [25,] 6.8584000
#> [26,] 9.1056000
#> [27,] 12.0890000
#> [28,] 16.0500000
#> [29,] 21.3090000
#> Standard Deviations: 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162 4.6162