splithalf: Running a Split-Half analysis on a PARAFAC model

Description

The samples are split into four subsamples: A,B,C,D. Subsamples are then combined and compared: AB vs. CD, AC vs. BD, AD vs. BC. The results show graphs from the components of each of the 6 models.

Usage

splithalf(eem_list, comps, splits = NA, rand = FALSE, normalise = TRUE,
  nstart = 10, cores = parallel::detectCores()/2, maxit = 500,
  ctol = 10^(-5), ..., verbose = FALSE)

Arguments

eem_list

eemlist containing sample data

comps

number of desired components

splits

optional, list of 4 numerical vectors containing the sample numbers for A,B,C and D sample subsets

rand

logical, splits are randomised

normalise

state whether EEM data should be normalised in advance

nstart

number of random starts

cores

number of parallel calculations (e.g. number of physical cores in CPU)

maxit

maximum iterations for PARAFAC algorithm

ctol

Convergence tolerance (R^2 change)

...

additional parameters that are passed on to parafac

verbose

states whether you want additional information during calculation

Value

data frame containing components of the splithalf models

Details

Split data sets can be split suboptimal and cause low TCCs. Therefore, subsamples are recombined in 3 different ways and a TCC close to 1 in only one split combination per component is already a positive result. Check the split sets to check for sample independency.

Examples

Run this code

# NOT RUN {
data(eem_list)

splithalf(eem_list,6,nstart=2)
# }