pathway.var.test(y1,y2,dag,alpha,variance=FALSE,s1=NULL,s2=NULL)
gRbase
for more details.TRUE
the estimates of the
covariance matrices are included in the result.The expression data may contain some genes differing from those in the pathway: in such case the function automatically takes the intersection between the two gene sets.
A necessary condition for the existence of the covariance estimates is that
the number of statistical units (samples) is greater than the number of
variables. If this is not the case, penalized techniques for estimating
$s1^(-1)$ and $s2^(-1)$
have to be employed, that are currently not provided by the package. In theses
cases, one can perform penalized estimation of
$s1^(-1)$ and $s2^(-1)$
outside topologyGSA, and then provide such estimates as input arguments
to the function pathway.var.test
to compute the value of the test for
homogeneity. In this case, computation of the p-value deserves attention,
as standard results on the asymptotic distribution of the test statistic may
no longer be valid. Therefore, computation of the p-value has to be dealt
with by the user.
This function requires gRBase
and qpgraph
packages.
Lauritzen, S.L. (1996). Graphical models. Clarendon Press, Oxford.
pathway.mean.test
, clique.var.test
,
clique.mean.test
.
data(examples)
pathway.var.test(y1, y2, dag_bcell, 0.05)
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