# Power analysis for longitudinal multilevel models: powerlmm 0.2.0 is now out on CRAN

My R packge `powerlmm`

0.2.0 is now out on CRAN. It can be installed from CRAN https://cran.r-project.org/package=powerlmm or GitHub https://github.com/rpsychologist/powerlmm.

# Changes in version 0.2.0

## New features

- Analytical power calculations now support using Satterthwaite’s degrees of freedom approximation.
`Simulate.plcp`

will now automatically create lme4 formulas if none is supplied, see`?create_lmer_formula`

.- You can now choose what alpha level to use.
- Treat cluster sizes as a random variable,
`uneqal_clusters`

now accepts a function indicating the distribution of cluster sizes, via the new argument`func`

, e.g.`rpois`

or`rnorm`

could be used to draw cluster sizes. - Expected power for designs with parameters that are random variables,
can be calculated by averaging over multiple realizations, using the
argument
`R`

. - Support for parallel computations on Microsoft Windows, and in GUIs/interactive
environments, using
`parallel::makeCluster`

(PSOCK). Forking is still used for non-interactive Unix environments.

## Improvements

- Calculations of the variance of the treatment effect is now much faster for designs with unequal clusters and/or missing data, when cluster sizes are large. The calculations now use the much faster implementation used by lme4.
- Cleaner print-methods for
`plcp_multi`

-objects. - Multiple power calculations can no be performed in parallel, via the
argument
`cores`

. `simulate.plcp_multi`

now have more options for saving intermediate results.`print.plcp_multi_power`

now has better support for subsetting via either [], head(), or subset().

## Breaking changes

`icc_pre_subject`

is now defined as`(u_0^2 + v_0^2) / (u_0^2 + v_0^2 + error^2)`

, instead of`(u_0^2) / (u_0^2 + v_0^2 + error^2)`

. This would be the subject-level ICC, if there’s no random slopes, i.e. correlation between time points for the same subject.`study_parameters()`

: 0 and NA now means different things. If 0 is passed, the parameters is kept in the model, if you want to remove it specify it as NA instead.`study_parameters()`

: is now less flexible, but more robust. Previously a large combination if raw and relative parameters could be combined, and the individual parameters was solved for. To make the function less bug prone and easier to maintain, it is now only possible to specify the cluster-level variance components as relative values, if the other parameters as passed as raw inputs.

## Bug fixes and minor changes

- Output from
`simulate_data()`

now includes a column`y_c`

that contains the full outcome vector, without missing values added. This makes it easy to compare the complete and incomplete data set, e.g. via`simulate()`

. `simulate()`

new argument`batch_progress`

enables showing progress when doing multiple simulations.- Fix bug in
`summary.plcp_sim`

where the wrong % convergence was calculated. - Simulation function now accepts lme4 formulas containing ”||“.
- The cluster-level intercept variance is now also set to zero in the control group, when a partially nested design is requested.
- Fix incorrect error message from
`study_parameters`

when`icc_cluster_pre = NULL`

and all inputs are standardized. - Fix bug that would cause all slopes to be zero when
`var_ratio`

argument was passed a vector of values including a 0, e.g.`var_ratio = c(0, 0.1, 0.2)`

. - Fix bug for multi-sim objects that caused the wrong class the be used for,
e.g.
`res[[1]]$paras`

, and thus the single simulation would not print correctly. - Results from multi-sim objects can now be summarized for all random effects in the model.
- More support for summarizing random effects from partially nested formulas,
e.g.
`cluster_intercept`

and`cluster_slope`

is now correctly extracted from`(0 + treatment + treatment:time || cluster)`

. - When Satterthwaite’s method fails the between clusters/subjects DFs
are used to calculate
*p*-values. `Power.plcp_multi`

is now exported.`get_power.plcp_multi`

now shows a progress bar.- Fix a bug that caused dropout to be wrong when one condition had 0 dropout, and
`deterministic_dropout = FALSE`

.

Written by **Kristoffer Magnusson** a researcher in clinical psychology. You should follow him on Twitter and come hang out on the open science discord Git Gud Science.

Published March 21, 2018 (View on GitHub)