summary.plcp_sim: Summarize the results from a simulation of a single study...

Description Usage Arguments Details Value

View source: R/simulate.R

Description

Summarize the results from a simulation of a single study design-object

Usage

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## S3 method for class 'plcp_sim'
summary(object, model = NULL, alpha = 0.05,
  para = NULL, ...)

## S3 method for class 'plcp_sim_formula_compare'
summary(object, model = NULL,
  alpha = 0.05, model_selection = NULL, LRT_alpha = 0.1, para = NULL,
  ...)

Arguments

object

A simulate.plcp-object

model

Indicates which model that should be returned. Default is NULL which return results from all model formulas. Can also be a character matching the names used in sim_formula_compare.

alpha

Indicates the significance level. Default is 0.05 (two-tailed), one-tailed tests are not yet implemented.

para

Selects a parameter to return. Default is NULL, which returns all parameters. If multiple model formulas are compared a named list can be used to specify different parameters per model.

...

Currently not used

model_selection

indicates if the summary should be based on a LRT model selection strategy. Default is NULL, which returns all models, if FW or BW a forward or backward model selection strategy is used, see Details.

LRT_alpha

Indicates the alpha level used if doing LRT model comparisons.

Details

Model selection

It is possible to summarize the performance of a data driven model selection strategy based on the formulas used in the simulation (see sim_formula_compare). The two model selection strategies are:

The model comparison is performed using a likelihood ratio test based the REML criterion. Hence, it assumed you are comparing models with the same fixed effects, and that one of the models is a reduced version of the other (nested models). The LRT test is done as a post-processing step, so model_selection option will not re-run the simulation. This also means that different alpha levels for the LRTs can be investigated without re-running the simulation.

Data transformation

If the data has been transformed sim_formula(data_transform = ...), then true parameter values (thetas shown in the summary will most likely no longer apply. Hence, relative bias and CI coverage will be in relation to the original model. However, the empirical estimates will be summarized correctly, enabling investigation of power and Type I errors using arbitrary transformations.

Value

Object with class plcp_sim_summary. It contains the following output:


rpsychologist/powerlmm documentation built on May 5, 2018, 11:54 p.m.