generate_interaction {InteractionPoweR}

Generate interaction data set

Description

Simulate a single data set with an interaction (y ~ x1 + x2 + x1*x2). All values other than 'N' are population-level effects - the values within any single simulated data set will vary around the defined values.

Usage

generate_interaction(
  N,
  r.x1.y,
  r.x2.y,
  r.x1x2.y,
  r.x1.x2,
  rel.x1 = 1,
  rel.x2 = 1,
  rel.y = 1,
  k.x1 = 0,
  k.x2 = 0,
  k.y = 0,
  adjust.correlations = TRUE,
  tol = 0.005,
  iter = 10,
  N.adjustment = 1e+06,
  r.x1.y.adjust = NULL,
  r.x2.y.adjust = NULL,
  r.x1.x2.adjust = NULL,
  r.x1x2.y.adjust = NULL,
  internal.adjust = FALSE,
  skew.x1 = NA,
  skew.x2 = NA,
  skew.y = NA
)

Arguments

N

Sample size. Must be a positive integer. Has no default value.

r.x1.y

Pearson's correlation between x1 and y. Must be between -1 and 1. Has no default value.

r.x2.y

Pearson's correlation between x2 and y. Must be between -1 and 1. Assumed to be the 'moderator' in some functions. Has no default value.

r.x1x2.y

Pearson's correlation between the interaction term x1x2 (x1 * x2) and y. Must be between -1 and 1. Has no default value.

r.x1.x2

Pearson's correlation between x1 and x2. Must be between -1 and 1. Has no default value.

rel.x1

Reliability of x1 (e.g. test-retest reliability, ICC, Cronbach's alpha). Default is 1 (perfect reliability). Must be greater than 0 and less than or equal to 1.

rel.x2

Reliability of x2 (e.g. test-retest reliability, ICC, Cronbach's alpha). Default is 1 (perfect reliability). Must be greater than 0 and less than or equal to 1.

rel.y

Reliability of xy (e.g. test-retest reliability, ICC, Cronbach's alpha). Default is 1 (perfect reliability). Must be greater than 0 and less than or equal to 1.

k.x1

Number of discrete values for x1. Can be used to make a variable binary or ordinal.

k.x2

Number of discrete values for x2. Can be used to make a variable binary or ordinal.

k.y

Number of discrete values for y.Can be used to make a variable binary or ordinal.

adjust.correlations

If variables are ordinal or binary, should correlations be adjusted so that output data has the specified correlation structure? Default is TRUE.

tol

Correlation adjustment tolerance. When adjust.correlations = TRUE, correlations are adjusted so that the population correlation is within r='tol' of the target. Default = 0.005.

iter

Max number of iterations to run the correlation adjustment for. Typically only a couple are needed. Default = 10.

N.adjustment

Sample size to use when adjusting correlations. Default = 1000000.

r.x1.y.adjust

Internal use only.

r.x2.y.adjust

Internal use only.

r.x1.x2.adjust

Internal use only.

r.x1x2.y.adjust

Internal use only.

internal.adjust

Internal use only.

skew.x1

No longer supported.

skew.x2

No longer supported.

skew.y

No longer supported.

Value

A data frame containing variables 'x1', 'x2', 'y', and 'x1x2'. 'x1x2' is x1*x2. The correlations between these variables are drawn from the defined population-level values. Output variables are all z-scored (mean=0, sd=1).

Examples

dataset <- generate_interaction(N = 10,r.x1.y = 0,r.x2.y = .1,r.x1x2.y = -.2,r.x1.x2 = .3)