print.plcp_mc_se

An object created with <code><a rd-options="" href="/link/get_monte_carlo_se?package=powerlmm&version=0.4.0" data-mini-rdoc="powerlmm::get_monte_carlo_se">get_monte_carlo_se</a></code>.

digits

Print method for <code>get_monte_carlo_se</code>-objects

Calculate power for the 'time x treatment' effect
in two- and three-level multilevel longitudinal studies with missing data.
Both the third-level factor (e.g. therapists, schools, or physicians),
and the second-level factor (e.g. subjects), can be assigned random slopes.
Studies with partially nested designs, unequal cluster sizes,
unequal allocation to treatment arms, and different dropout patterns
per treatment are supported. For all designs power can be
calculated both analytically and via simulations. The analytical
calculations extends the method described in Galbraith et al. (2002)
<doi:10.1016/S0197-2456(02)00205-2>, to three-level models.
Additionally, the simulation tools provides flexible ways to investigate
bias, Type I errors and the consequences of model misspecification.

Kristoffer Magnusson

powerlmm

Power Analysis for Longitudinal Multilevel Models

print.plcp_mc_se function

An object created with <code><a rd-options='' href='get_monte_carlo_se'>get_monte_carlo_se</a></code>.

print.plcp_mc_se: Print method for `get_monte_carlo_se`-objects

Description

Usage

Arguments