This uses simulations to match the rse

Usage

dfWishart(omega, n, rse, upper, totN = 1000, diag = TRUE, seed = 1234)

Arguments

omega: represents the matrix for simulation
n: This represents the number of subjects/samples this comes from (used to calculate rse). When present it assumes the rse= sqrt(2)/sqrt(n)
rse: This is the rse that we try to match, if not specified, it is derived from n
upper: The upper boundary for root finding in terms of degrees of freedom. If not specified, it is n*200
totN: This represents the total number of simulated inverse wishart deviates
diag: When TRUE, represents the rse to match is the diagonals, otherwise it is the total matrix.
seed: to make the simulation reproducible, this represents the seed that is used for simulating the inverse Wishart distribution

Value

output from uniroot() to find the right estimate

Author

Matthew L. Fidler

Examples


dfWishart(lotri::lotri(a+b~c(1, 0.5, 1)), 100)
#> $root
#> [1] 164.9818
#> 
#> $f.root
#> [1] 0.0005180792
#> 
#> $iter
#> [1] 28
#> 
#> $init.it
#> [1] NA
#> 
#> $estim.prec
#> [1] 7.882673e-05
#>