Description Usage Arguments Value See Also Examples
Estimate partial correlations according to SPACE model.
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Y |
Numeric matrix (N \times Q) containing N iid samples of the response vector \textbf{y}. |
lam1 |
Non-negative numeric value, the space-lasso penalty corresponding to λ_1, which subjects the partial correlation vector, \bf ρ_{yy}, to the l_1 norm. It induces overall sparsity of \{ y_q - y_l : q \neq l \} edges. . |
sridge |
The l_2 penalty parameter defaults to 0. When it is positive,
it imposes an elastic net penalty in tandem with |
sig |
Positive numeric vector (p \times 1) representing the estimate of σ^{ii}, the diagonal of
the inverse covariance matrix. It defaults to NULL and
and will be estimated |
weight |
The weights applied to the Q regressions in the SPACE model. If If If Otherwise, The scale of weight does not matter. In this function, weight will be rescaled to have mean=1 |
iter |
Positive integer specifying the number of iterations for estimating σ^{ii} and ρ. Defaults to 3. |
tol |
Positive numeric value specifying the convergence tolerance of the coordinate descent algorithm;
in other words, it is criterion that stops parameter estimation when no parameter changes value exceeding |
cdmax |
Positive integer specifiying the maximum number of parameter updates allowed before reporting the algorithm as having failed to converge. Default may need to be increased for inferring very large-scale networks (i.e. p,q > 1000). |
rho |
Numeric matrix (p \times p) representing the estimate of \hatρ, the partial correlation matrix.
It defaults to NULL and and will be estimated |
iscale |
Logical indicating to standardize the whole input data. Defaults to TRUE.
See |
A list containing
ParCor
The estimated partial correlation matrix (P \times P),
where off-diagonals |\hat ρ^{p,q}_{yy}| > 1e-6 encode the edges \{ y_q - y_l : q \neq l \}
and the diagonals are 1's.
sig.fit
The estimated diagonal \hat σ^{ii}.
rss
The residual sums of squares from the model fit.
convergence logical: true for successful convergence, otherwise failed to converge. Failure can be
mitigated by increasing tol
and/or cdmax
.
deltaMax
The maximum change in parameter values between the penultimate and ultimate iteration.
If spacemap
does not converge, deltaMax
provides some measure of how far away it was from converging
when compared to tol
.
spacemap
, cvVote
, bootEnsemble
, bootVote
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