calculates the partial correlation between X and Y after removing the influence of Z.
nan_partcorrcoef(X [,Mode]); nan_partcorrcoef(X,Y,Z); nan_partcorrcoef(X,Y,Z,[]); nan_partcorrcoef(X,Y,Z,'Pearson'); nan_partcorrcoef(X,Y,Z,'Rank'); nan_partcorrcoef(X,Y,Z,'Spearman'); [R,p,ci1,ci2] = nan_partcorrcoef(...);
[default]
computes the partial correlation based on cc(x,y),cc(x,z) and cc(y,z) with the respective mode.
is the partialcorrelation matrix
,i) and Y(:,j)
gives the significance of PCC
do not reject the Null hypothesis: "R is zero".
The alternative hypothesis "R2 is larger than zero" is true with probability (1-alpha).
lower 0.95 confidence interval
upper 0.95 confidence interval
X, Y and Z can contain missing values encoded with NaN. NaN's are skipped, NaN do not result in a NaN output. (Its assumed that the occurence of NaN's is uncorrelated) The output gives NaN, only if there are insufficient input data.
The partial correlation is defined as pcc(xy|z)=(cc(x,y)-cc(x,z)*cc(y,z))/sqrt((1-cc(x,y)')*((1-cc(x,z)')))
on the partial correlation coefficient
[1] http://www.tufts.edu/~gdallal/partial.htm
[2] http://www.nag.co.uk/numeric/fl/manual/pdf/G02/g02byf.pdf