neg.intcont {negligible}

Negligible Interaction Test for Continuous Predictors

Description

Testing for the presence of a negligible interaction between two continuous predictor variables

Usage

neg.intcont(
  outcome = NULL,
  pred1 = NULL,
  pred2 = NULL,
  eiL,
  eiU,
  standardized = TRUE,
  nbootpd = 1000,
  data = NULL,
  alpha = 0.05,
  plot = TRUE,
  save = FALSE
)

## S3 method for class 'neg.intcont'
print(x, ...)

Arguments

outcome

continuous outcome variable

pred1

first continuous predictor variable

pred2

second continuous predictor variable

eiL

lower limit of the negligible effect (equivalence) interval

eiU

upper limit of the negligible effect (equivalence) interval

standardized

logical; should the solution be based on standardized variables (and eiL/eiU)

nbootpd

number of bootstrap samples for the calculation of the CI for the proportional distance

data

optional data file containing the categorical variables

alpha

nominal acceptable Type I error rate level

plot

logical; should a plot be printed out with the effect and the proportional distance

save

logical; should the plot be saved

x

object of class neg.intcont

...

extra arguments

Details

This function evaluates whether the interaction between two continuous predictor variables is negligible. This can be important for deciding whether to remove an interaction term from a model or to evaluate a hypothesis related to negligible interaction.

eiL/eiU represent the bounds of the negligible effect (equivalence) interval (i.e., the minimally meaningful effect size, MMES) and should be set based on the context of the research. When standardized = TRUE, Acock (2014) suggests that the MMES for correlations can also be applied to standardized effects - Acock, A. C. (2014). A Gentle Introduction to Stata (4th ed.). Texas: Stata Press.

User can input the outcome variable and two predictor variable names directly (i.e., without a data statement), or can use the data statement to indicate the dataset in which the variables can be found.

The advantage of this approach when standardized = TRUE and there are only two predictors is that the Delta method is adopted. However, for general cases researchers can also use the neg.reg function.

The proportional distance (interaction coefficient/negligible effect bound) estimates the proportional distance of the effect from 0 to negligible effect bound, and acts as an alternative effect size measure.

The confidence interval for the proportional distance is computed via bootstrapping (percentile bootstrap).

Value

A list containing the following:

• intcoef Interaction coefficient

• intcil Lower bound of the 1-alpha CI for the interaction coefficient

• intciu Upper bound of the 1-alpha CI for the interaction coefficient

• eiL Lower bound of the negligible effect (equivalence) interval

• eiU Upper bound of the negligible effect (equivalence) interval

• sprs Semi-partial correlation squared for the interaction term

• PD Proportional distance

• CI95L Lower bound of the 1-alpha CI for the PD

• CI95U Upper bound of the 1-alpha CI for the PD

• alpha Nominal Type I error rate

Author(s)

Rob Cribbie [e-mail]

Examples

y<-rnorm(25)
x1<-rnorm(25)
x2<-rnorm(25)
d<-data.frame(y,x1,x2)
neg.intcont(outcome = y, pred1 = x1, pred2 = x2, data = d,
eiL = -.25, eiU = .25, standardized = TRUE, nbootpd = 100)