The
Partial Correlation Diagram
process helps you infer
association
and potential causal relationships between a set of
variables
.
This process fits so-called
covariance
selection
models
(also known as
graphical Gaussian models
), in which partial correlations (
the correlation between two variables adjusted for all other variables)
are estimated, and then plots
each variable as a node. The nodes are then connected with line segments, whose size and color are determined by the partial correlations. Additional graphs are also available, along with options for controlling them.
where
E
is the
eigenvector
matrix, and
M
is the diagonal matrix of eigenvalues.
The
X
and
Y
position of each node is determined by the following:
M[1]
and
M[2]
are the 1st and 2nd eigenvalue, respectively.
E[,1]
and
E[,2]
are the 1st and 2nd eigenvector, respectively.
One
Input Data Set
is required to run the
Partial Correlation Diagram
process.
Because the
Distance Matrix and Clustering
process calculates the
distance between the
observations
(rows), a
wide
-formatted data set (in which the rows comprise each of the variables) is normally used as the input data set. If you are working with a
tall
data set and you want to compute distance between columns, first run the
Transpose Rectangular
process.