effective_cor  R Documentation 
Will return the estimated correlation between the design matrix and the surrogate variables when you assign a target correlation. The method is described in detail in Gerard (2020).
effective_cor( design_perm, sv, target_cor, calc_first = c("cor", "mean"), method = c("hungarian", "marriage"), iternum = 1000 )
design_perm 
A numeric design matrix whose rows are to be permuted (thus controlling the amount by which they are correlated with the surrogate variables). The rows index the samples and the columns index the variables. The intercept should not be included (though see Section "Unestimable Components"). 
sv 
A matrix of surrogate variables 
target_cor 
A numeric matrix of target correlations between the
variables in 
calc_first 
Should we calculate the correlation of the mean

method 
Should we use the GaleShapley algorithm
for stable marriages ( 
iternum 
The total number of simulated correlations to consider. 
This function permutes the rows of design_perm
many times, each
time calculating the Pearson correlation between the columns of
design_perm
and the columns of sv
. It then returns the
averages of these Pearson correlations. The permutation is done
using permute_design
.
A matrix of correlations. The rows index the observed covariates
and the columns index the surrogate variables. Element (i, j) is
the estimated correlation between the ith variable in
design_perm
and the jth variable in sv
.
David Gerard
Gale, David, and Lloyd S. Shapley. "College admissions and the stability of marriage." The American Mathematical Monthly 69, no. 1 (1962): 915. doi: 10.1080/00029890.1962.11989827.
Gerard, D (2020). "Databased RNAseq simulations by binomial thinning." BMC Bioinformatics. 21(1), 206. doi: 10.1186/s1285902034509.
Hornik K (2005). "A CLUE for CLUster Ensembles." Journal of Statistical Software, 14(12). doi: 10.18637/jss.v014.i12. doi: 10.18637/jss.v014.i12.
C. Papadimitriou and K. Steiglitz (1982), Combinatorial Optimization: Algorithms and Complexity. Englewood Cliffs: Prentice Hall.
## Generate the design matrices and set target correlation  n < 10 design_perm < cbind(rep(c(0, 1), each = n / 2), rep(c(0, 1), length.out = n)) sv < matrix(rnorm(n)) target_cor < matrix(c(0.9, 0.1), ncol = 1) ## Get estimated true correlation  ## You should use a much larger iternum in practice effective_cor(design_perm = design_perm, sv = sv, target_cor = target_cor, iternum = 10)
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