Parametric bootstrap

bootstrap(lmm::LMM; double = false, n = 100, verbose = true, init = lmm.result.theta, rng = default_rng())

Parametric bootstrap.

• double - use double approach (default - false);
• n - number of bootstrap samples;
• verbose - show progress bar;
• init - initial values for lmm;
• rng - random number generator.

Parametric bootstrap based on generating random responce vector from known distribution, that given from fitted LMM model.

• Simple bootstrap:

For one-stage bootstrap variance parameters and coefficients simulated in one step.

• Double bootstrap:

For double bootstrap (two-tage) variance parameters simulated in first cycle, than they used for simulating coefficients and var(β) on stage two. On second stage parent-model β used for simulations.

lmm = Metida.LMM(@formula(var~sequence+period+formulation), df0m;
random = Metida.VarEffect(Metida.@covstr(formulation|subject), Metida.CSH),
)
Metida.fit!(lmm)
bt = Metida.bootstrap(lmm; n = 1000, double = true, rng = MersenneTwister(1234))
confint(bt)

See also: confint, Metida.miboot, Metida.nvar, Metida.tvar, Metida.straps, Metida.sdstraps, Metida.thetastraps

nvar(br::BootstrapResult)

Number of coefficient in the model.

tvar(br::BootstrapResult) = length(br.theta)

Number of theta parameters in the model.

straps(br::BootstrapResult, idx::Int)

Return coefficients vector.

sdstraps(br::BootstrapResult, idx::Int)

Return sqrt(var(β)) vector.

straps(br::BootstrapResult, idx::Int)

Return theta vector.