Short descriptions about the objectives
MexPosition did not update to be on the current CRAN, but can still be downloaded from Github. PTCA4CATA with parallelized bootstrap is on JY's github. superheat that enables reversing of axis when plotting suqare matrices are on MC's github
devtools::install_github("cran/MexPosition") devtools::install_github("juchiyu/PTCA4CATA") devtools::install_github("mychan24/superheat")
2019.9.23: HMFA-normalization fixed.
This was only fixed for the 4rows analysis, not for the original MuSu.
The original version extracted both levels of singular values from the original data table. However, these two levels of singular values should be extracted hierarchically: extract delta for edges -> weight the edge tables -> extract delta for subject -> weight the subject tables
2019.9.25: Contribution fixed
2019.11.05: Comparison to DiSTATIS
(1) DiSTATIS with Gorden's parcellation
(2) DiSTATIS with mean connectivity based on Gordon's parcellation
(3) DiSTATIS with mean connectivity based on individual parcellation
Our method: HMFA with subject first and edgetype (between vs. within) then
Expectation:
Compared to (1), (2) will miss some effect because the pattern within each edge is smoothed and the some effect will be missed.
Compared to (3), (2) will be less likely to detect effects in small networks
need another simulation in small network
Compare to our SVD, (3) will miss the effect of patterns