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.
data frame containing components of the splithalf models
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)
rescale
rescale splithalf models to Fmax, see eempf_rescaleBC
strictly_converging
calculate nstart converging models and take the best. Please see eem_parafac.
verbose
states whether you want additional information during calculation
...
additional parameters that are passed on to parafac
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.