Description Usage Arguments Value Examples
View source: R/eBIC_allmodels_withlmekin_ML.r
Compute log likelihood, BIC and eBIC.
The model with the smallest eBIC should be selected.
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Y |
A numeric named vector where the names are individuals names and the values their phenotype. |
selec_XX |
A list of length one, two or three matrices depending on the models. Use helper function |
KK |
a list of one, two or three matrices depending on the models - additive: a n by n matrix, where n=number of individuals, with rownames()=colnames()=individual names - additive+dominance: two n by n matrices, where n=number of individuals, with rownames()=colnames()=individual names - female+male: a n.female by n.female matrix, with rownames()=colnames()=female names and a n.male by n.male matrix, with rownames()=colnames()=male names - female+male+interaction: the same two matrices as the model female+male and a n by n matrix, where n=number of individuals, with rownames()=colnames()=individual names |
nb.tests |
number of computed tests (total number of SNPs) |
cofs |
A n by q matrix, where n=number of individuals, q=number of fixed effect, |
female |
A factor of levels female names and length n, only for the last two models |
male |
A factor of levels male names and length n, only for the last two models |
lambda |
penalty used in the computation of the eBIC; if NULL, the default will be 1 - 1/(2k) with L=n^k where L=total number of SNPs (see function "lambda.calc") |
A matrix with a line for each mlmm step and 4 columns : BIC, ajout, eBIC_0.5 and LogL.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | ### Additive model ###
## Not run:
data("mlmm.gwas.AD")
XX = list(Xa)
KK = list(K.add)
# GWAS
res_mlmm <- mlmm_allmodels(floweringDateAD, XX, KK)
manhattan.plot(res_mlmm)
# Model selection
sel_XX <- frommlmm_toebic(XX, res_mlmm)
res.eBIC <- eBIC_allmodels(floweringDateAD, sel_XX, KK, ncol(Xa))
# Effects estimations with the selected model
sel_XXclass <- fromeBICtoEstimation(sel_XX, res.eBIC)
eff.estimations <- Estimation_allmodels(floweringDateAD, sel_XXclass, KK)
genotypes.boxplot(Xa, floweringDateAD, effects = eff.estimations)
## End(Not run)
### Additive + dominance model
## Not run:
data("mlmm.gwas.AD")
XX = list(Xa, Xd)
KK = list(K.add, K.dom)
# GWAS
res_mlmm <- mlmm_allmodels(floweringDateAD, XX, KK)
manhattan.plot(res_mlmm)
# Model selection
sel_XX <- frommlmm_toebic(XX, res_mlmm)
res.eBIC <- eBIC_allmodels(floweringDateAD, sel_XX, KK, ncol(Xa))
#the selected model is the null model
## End(Not run)
### Female+Male model
## Not run:
data("mlmm.gwas.FMI")
XX = list(Xf, Xm)
KK = list(K.female, K.male)
# GWAS
res_mlmm <- mlmm_allmodels(floweringDateFMI, XX, KK, female = female, male = male)
manhattan.plot(res_mlmm)
# Model selection
sel_XX <- frommlmm_toebic(XX, res_mlmm)
res.eBIC <- eBIC_allmodels(floweringDateFMI, sel_XX, KK, ncol(Xf), female = female, male = male)
#the selected model is the null model
## End(Not run)
### Female+Male+Interaction model
## Not run:
data("mlmm.gwas.FMI")
XX = list(Xf, Xm, Xfm)
KK = list(K.female, K.male, K.hybrid)
# GWAS
res_mlmm <- mlmm_allmodels(floweringDateFMI, XX, KK, female = female, male = male)
manhattan.plot(res_mlmm)
# Model selection
sel_XX <- frommlmm_toebic(XX, res_mlmm)
res.eBIC <- eBIC_allmodels(floweringDateFMI, sel_XX, KK, ncol(Xf), female = female, male = male)
#the selected model is the null model
## End(Not run)
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