BANOVA.floodlight {BANOVA}

Floodlight analysis based on BANOVA models

Description

BANOVA.floodlight conducts floodlight analysis based on various BANOVA models.

Usage

BANOVA.floodlight(sol, var_numeric, var_factor, flood_values = list())
## S3 method for class 'BANOVA.floodlight'
print(x, ...)

Arguments

sol

a BANOVA.* object

var_numeric

the numeric variable

var_factor

the factor variable

flood_values

a list of values of the other numeric variables which interact with var_factor and var_numeric, the floodlight analysis will be based on these values, default 0

x

a BANOVA.floodlight object

...

additional arguments, currently ignored

Details

A floodlight analysis (Spiller et al. 2013; Johnson and Neyman 1936) based on BANOVA models is conducted, which identifies regions of the numeric variable for which differences between the levels of the factor are significant. The endpoints of the 95% credible interval of the numeric variable provide the Johnson-Neyman points; for values outside of that interval there is 'strong' evidence that there is a difference between the levels of the factor.

Value

BANOVA.floodlight returns an object of class "BANOVA.floodlight". The returned object is a list containing:

sol

table of the floodlight analysis including the 95% credible interval

num_range

range of the numeric variable

References

Spiller, S., Fitzsimons, G., Lynch Jr., J. and McClelland, G. (2013) Spotlights, Floodlights, and the Magic Number Zero: Simple Effects Tests in Moderated Regression. Journal of Marketing Research, Vol. L, pp. 277-288.

Wedel, M. and Dong, C. (2016) BANOVA: Bayesian Analysis of Variance for Consumer Research. Submitted.

Examples

data(condstudy_sub)

library(rstan)
# use BANOVA.run
model <- BANOVA.model('Normal')
stanmodel <- BANOVA.build(model)
res <- BANOVA.run(att~cond+pict, ~type, fit = stanmodel, data = condstudy_sub, 
                  id = 'id', iter = 500, thin = 1, chains = 2)
BANOVA.floodlight(res, var_factor = 'type', var_numeric = 'pict')