Compute the effective sample size for a LMM.

ess(G, cols = seq(ncol(G)), M = length(cols), h2, s2 = 1)

Arguments

G	A FBM matrix of genotypes. Missing values are not handled.
cols	A vector of columns of G to be used in the model. By default, all columns of G are used.
M	A scalar for normalization of the genetic relationship matrix: GRM = Z'Z / M, where Z is a matrix of standardized genotypes. By default, M = length(cols).
h2	The estimated heritability in the LMM.
s2	The estimated scaling constant for the variance components in the LMM.

Value

A data.frame of results.

Examples

G <- attach_example200()
G
#> A Filebacked Big Matrix of type 'code 256' with 1500 rows and 200 columns.
G[1:5, 1:10]
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [1,]    2    1    0    2    1    1    1    1    2     1
#> [2,]    1    2    1    1    2    1    1    2    1     1
#> [3,]    2    2    1    1    1    1    1    0    2     0
#> [4,]    0    2    0    2    1    0    0    1    0     2
#> [5,]    1    2    0    2    2    1    0    0    1     0

ess(G, h2 = 0.5)
#>      N   M  h2 s2     mult      ESS
#> 1 1500 200 0.5  1 1.768339 2652.508

ess(G, h2 = 0.8)
#>      N   M  h2 s2     mult      ESS
#> 1 1500 200 0.8  1 4.357941 6536.911