CASE Model Training

Fit the priors for the cis-eQTL effect sizes.

CASE_train(
  Z = NULL,
  R,
  hatB = NULL,
  hatS = NULL,
  N,
  V = NULL,
  verbose = TRUE,
  ...
)

Arguments

Z

M * C matrix of z scores.

R

M * M matrix of LD.

hatB

M * C matrix of the estimated effects. Alternative summary data (together with hatS) to be provided instead of Z.

hatS

M * C matrix of standard errors of the estimated effects. Alternative summary data (together with hatB) to be provided instead of Z.

N

either C vector of the sample size, or C * C matrix of the sample size (diagonal) and ovelaps (off-diagonal). If provided with a vector, CASE assumes that each pair of traits overlaps with their minimal sample size.

V

(optional) C * C covariance (correlation) matrix for the noise between traits. If not provided, the default is an identity matrix.

verbose

(optional) logical, whether to print logging information. Default = TRUE.

...

additional arguments.

Value

A "CASE_training" object with the following elements:

pi:

L-vector, the prior probabilities of sharing patterns.

U:

L-list of C * C matrix, the prior covariances of sharing patterns.

V:

C * C matrix, the sample-adjusted phenotypical variance.

Details

TBD

References

TBD

Author

Chen Lin, Hongyu Zhao