getVarCov: Extract variance-covariance matrix

Description Usage Arguments Value Author(s) See Also Examples

View source: R/VarCov.R

Description

Extract the variance-covariance matrix from a fitted model, such as a mixed-effects model.

Usage

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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, ...)

Arguments

obj

A fitted model. Methods are available for models fit by lme and by gls

individuals

For models fit by lme a vector of levels of the grouping factor can be specified for the conditional or marginal variance-covariance matrices.

individual

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.

type

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

Value

A variance-covariance matrix or a list of variance-covariance matrices.

Author(s)

Mary Lindstrom [email protected]

See Also

lme, gls

Examples

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fm1 <- lme(distance ~ age, data = Orthodont, subset = Sex == "Female")
getVarCov(fm1)
getVarCov(fm1, individual = "F01", type = "marginal")
getVarCov(fm1, type = "conditional")
fm2 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary,
           correlation = corAR1(form = ~ 1 | Mare))
getVarCov(fm2)

Example output

Random effects variance covariance matrix
            (Intercept)       age
(Intercept)     3.55020 -0.107490
age            -0.10749  0.025898
  Standard Deviations: 1.8842 0.16093 
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 
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 
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 

nlme documentation built on April 7, 2018, 5:03 p.m.

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