ImputeRandom | R Documentation |
ImputeRandom
imputes missing values with the Q ancestry coeficient matrix and G ancestral
frequencies estimates. Missing values are sampled with the estimated genotype
frequencies (P = Q G^T).
ImputeRound
imputes missing values with the Q ancestry coeficient matrix and G ancestral
frequencies estimates. Missing values are computed as a round of estimated genotype
frequencies (P = Q G^T).
ImputeRandom(tess3.res, masked.X)
ImputeRound(tess3.res, masked.X)
tess3.res |
tess3Main object with Q and G estimates. |
masked.X |
Genotype matrix with the missing values (NA values). |
the imputed genotype matrix.
library(tess3r)
n <- 100
K <- 3
ploidy <- 2
L <- 3001
data.list <- SampleGenoFromGenerativeModelTESS3(G = SampleUnifDirichletG(L, ploidy, K),
Q = SampleUnifQ(n, K),
coord = SampleNormalClusterCoord(n.by.pop = n, K = 1),
ploidy = ploidy)
# mask data
set.seed(0)
masked.prop <- 0.1
masked.X <- data.list$X
masked.X[sample(1:(ncol(masked.X)*nrow(masked.X)), (ncol(masked.X)*nrow(masked.X)) * masked.prop)] <- NA
# run tess3
tess3.obj <- tess3(X = masked.X,
coord = data.list$coord,
K = 3,
ploidy = 2,
lambda = 1.0,
method = "projected.ls",
rep = 2)
imputed.X.random <- ImputeRandom(Gettess3res(tess3.obj,3), masked.X)
mean(data.list$X[is.na(masked.X)] != imputed.X.random[is.na(masked.X)]) # pourcentage of error
imputed.X.round <- ImputeRound(tess3.res = Gettess3res(tess3.obj,3), masked.X)
mean(data.list$X[is.na(masked.X)] != imputed.X.round[is.na(masked.X)]) # pourcentage of error
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