lakhesize {lakhesis} | R Documentation |
This function returns the row and column consensus seriation for a list
of strands, containing their rankings, the results of their PCA, and coefficients of association and concentration.
lakhesize(strands, obj)
strands |
A |
obj |
The intial incidence matrix. |
Consensus seriation is achieved by iterative, multi-step linear regression using simulation. On one iteration, strands are chosen at random, omitting incomplete or missing pairs, using PCA to determine the best-fitting line for their rankings. Both strands' rankings are then regressed onto that line to determine missing values, and then re-ranked, repeating until all strands have been regressed. PCA of the simulated rankings is then used to determine the final sequence of the row and column elements.
A list
of the following:
RowConsensus
Data frame of the consensus seriation of the row elements in the order of their projection on the first principal axis. Contains one column, Row
.
ColConsensus
Data frame of the consensus seriation of the column elements in the order of their project onto the first principal axis. Contains one column, Column
.
RowPCA
The results of \link[stats]{prcomp}
performed on the row elements of strands.
ColPCA
The results of \link[stats]{prcomp}
performed on the column elements of strands.
Coef
A data frame containing the coefficients of agreement and concentration:
Strand
The number of the strand.
Consensus.Spearman.Sq
the measure of agreement, i.e., how well each strand accords with the consensus seriation. Using the square of Spearman's rank correlation coefficient, \rho^2
, between each strand and the consensus ranking, agreement is computed as the product of \rho^2
for their row and column rankings, \rho_r^2
\rho_c^2
.
Concentration.Kappa
the concentration coefficient \kappa
, which provides a measure of the optimality of each strand (see kappa.coef
).
data("quattrofontanili")
data("qfStrands")
lakhesize(qfStrands, quattrofontanili)