This uses simulations to match the rse

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

See also

Other Variability and uncertainty: confint.rxSolve(), cvPost(), rinvchisq(), rxEtaDistExpand(), rxEtaDistMuRef(), rxPp(), rxRmvn(), rxRmvt(), rxSimThetaOmega(), rxUiEtaDistAnchors(), rxUiEtaDists(), swapMatListWithCube()

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
#>