scenario_sim: Simulate SONE values for scenario.

In ionr: Test for Indifference of Indicator

Description

A wrapper that takes a scenario, and produces the Significance Of iNdicator Exclusion (SONE) values for each exclusion and calculates efficacy. Used by optimal_p.

Usage

scenario_sim(sizes, n_sim, to_n, tn_n = 8 - to_n, ...)

Arguments

sizes	An array of sample sizes to be simulated. Can be single value.
n_sim	number of simulations. 1000 is a start, 10000 was used in paper, but takes a long time
to_n	Number of indicators in a Trait relating to Outcome
tn_n	Number of indicators in a Trait Not relating to outcome.
...	further tweaking of the scale simulator, see scale_sim for details.

Value

Returns a list of SONE values and related efficacy. See example for details

1. SONE results. Feed this to scenario_plot or scenario_plot80 (see examples)

2. Summary efficacy. Such data comprises Table 1 in Vainik, Mottus et al., 2015 EJP

3. Full efficacy data.

Examples

#A scenario with 8 items relating to outcome, testing 2 different samples
sizes=c(250,500)
n_sim=100
to_n=8
scen1=scenario_sim(sizes,n_sim,to_n)  # takes a few seconds..
scenario_plot80(scen1[[1]],sizes,n_sim)

# A scenario with 2 out of 8 items relating to outcome, 2 different samples
to_n=2
scen2=scenario_sim(sizes,n_sim,to_n)  # takes a few seconds..
scenario_plot(scen2[[1]],sizes,n_sim,to_n)

ionr documentation built on May 2, 2019, 11:25 a.m.