[R-sig-ME] adding a kinship matrix as a random covariate

[Ben Bolker]

On 13-11-11 03:02 PM, Federico Calboli wrote:
> [This email might, or might have not, bounced, so I'm sending it a
> second time.  Apologies if you receive multiple copies]
> 
> Hi All,
> 
> I am possibly not up to date on using R mixed models when my random
> covariate is a Kinship matrix (from genetic data, not pedigree).
> 
> I have a data frame (called my.data), with m rows and 126 columns.
> The columns are the phenotypes (quantitative) and a number of
> covariates, plus the genotype (coded as x/y, where y and y are
> microsatellite lengths).  Independently I have a m by m matrix of
> kinship values, derived from the genetic data.
> 
> All looks like:
> 
>> my.data
> 
> id temp     bw    bl dt        rbw       rbl  sex  MSA  MSB  MSC  MSD
> MDE  MSF  MSG  MSH 1   1   16 0.8707 16.66 54 0.01612407 0.3085185
> m 240/256 162/166 256/256 123/123    <NA> 120/156 331/335 115/115 2
> 2   16 0.8644 16.91 54 0.01600741 0.3131481    m 268/270 162/162
> 256/256 123/123 361/373 120/156    <NA> 115/119 3   3   16 0.8464
> 17.25 54 0.01567407 0.3194444    m 240/268 162/162 272/272 123/123
> 353/361 120/120    <NA> 115/115 4   4   16 0.9077 18.16 54 0.01680926
> 0.3362963 <NA> 256/270 162/162    <NA> 123/123 353/361 120/120
> <NA> 115/119 5   5   16 0.9075 18.05 53 0.01712264 0.3405660    f
> <NA>    <NA>    <NA> 111/115 353/361 120/120    <NA> 115/115 6   6
> 16 0.8296 17.36 53 0.01565283 0.3275472    f 268/270 162/162    <NA>
> 111/123 353/361 120/156    <NA> 115/119 7   7   16 0.8354 17.74 51
> 0.01638039 0.3478431    f    <NA>    <NA>    <NA> 115/123    <NA>
> 120/156    <NA> 115/119 8   8   16 0.8614 16.84 53 0.01625283
> 0.3177358    f    <NA> 162/162 256/256 123/123    <NA> 120/156
> <NA> 115/119 9   9   16 0.7756 18.22 54 0.01436296 0.3374074    f
> 268/270    <NA> 272/272 115/123    <NA> 120/120    <NA> 115/115 10 10
> 16 0.8076 16.37 53 0.01523774 0.3088679    f    <NA> 162/172    <NA>
> 115/123 361/373 120/120    <NA> 115/115
> 
> and the kinship matrix:
> 
>> Kinship[1:6, 1:6]
> [,1]      [,2]      [,3]      [,4]      [,5]      [,6] [1,] 0.5438596
> 0.4035088 0.3750000 0.3618421 0.3728070 0.3771930 [2,] 0.4035088
> 0.5614035 0.3903509 0.4298246 0.3925439 0.3925439 [3,] 0.3750000
> 0.3903509 0.5438596 0.3399123 0.3179825 0.3991228 [4,] 0.3618421
> 0.4298246 0.3399123 0.5219298 0.3771930 0.3662281 [5,] 0.3728070
> 0.3925439 0.3179825 0.3771930 0.5175439 0.3662281 [6,] 0.3771930
> 0.3925439 0.3991228 0.3662281 0.3662281 0.5833333
> 
> I tried in lme4 the following:
> 
> lmer(all$bw ~ all$MSA + all$temp + all$sex + all$dt + (1|Kinship))
> 
> returns
> 
> Error in `[[<-.data.frame`(`*tmp*`, i, value = c(233L, 174L, 161L,
> 162L,  : replacement has 384120 rows, data has 485
> 
> 
> I tried in nlme this:
> 
> lme(all$bw ~ all$MSA + all$temp + all$sex + all$dt, random = ~
> 1|Kinship, na.action = na.omit) Error in eval(expr, envir, enclos) :
> object 'bw' not found
> 
> If I put the kinship in my.data
> 
>> my.data$K = Kinship
> 
> lme(bw ~ MSA + temp + sex + dt, random = ~ 1|K, data = my.data,
> na.action =  na.omit) Error in `row.names<-.data.frame`(`*tmp*`,
> value = origOrder) : invalid 'row.names' length
> 
> Obviously using a m by m matrix is not a good way of specifying a
> random effect.  Given my data, which is not going to change, is there
> a way of using the K matrix as a random covariate?  alternatively,
> which packages would allow me to do so?  Finally, I will admit it
> openly, from the start.  I am after a p-value, ideally from an anova,
> to see if the different genotypes at each locus have an effect or
> not.
> 

Are any of these StackOverflow answers relevant/useful?


http://stackoverflow.com/questions/8245132/single-level-variables-in-mixed-model-lme4-error-in-r/8247412#8247412

http://stackoverflow.com/questions/19327088/reproducing-results-from-previous-answer-is-not-working-due-to-using-new-version/19382162#19382162

http://stackoverflow.com/questions/19104475/how-to-modify-slots-lme4-1-0/19106863#19106863

 I would also look at the results of library("sos"); findFn("kinship") ,
although there's a fair amount to sort through there ...  Perhaps in
conjunction with the reverse depends/reverse suggests of nlme and lme4 ... ?