trans_GGMM {TransGraph}

Transfer learning of high-dimensional Gaussian graphical mixture models.

Description

Transfer learning of high-dimensional Gaussian graphical mixture models.

Usage

trans_GGMM(t.data, lambda.t, M, A.data, lambda.A.list, M.A.vec,
                  pseudo.cov="soft", cov.method="opt", cn.lam2=0.5, clambda.m=1,
                  theta.algm="cd", initial.selection="K-means", preselect.aux=0,
                  sel.type="L2", trace=FALSE )

Arguments

t.data

The target data, a n * p matrix, where n is the sample size and p is data dimension.

lambda.t

A list, the sequences of the tuning parameters (lambda1, lambda2, and lambda3) used in the initialization of the target domain.

M

Int, a selected upper bound of the true numbers of subgroups in the target domain.

A.data

The auxiliary data in K auxiliary domains, a list with K elements, each of which is a nk * p matrix, where nk is the sample size of the k-th auxiliary domain.

lambda.A.list

A list consisting of K lists, the k-th list is the sequences of the tuning parameters (lambda1, lambda2, and lambda3) used in the initialization of the k-th auxiliary domain.

M.A.vec

A vector composed of K integers, the k-th element is a selected upper bound of the true numbers of subgroups in the k-th auxiliary domain.

pseudo.cov

The method for calculating pseudo covariance matricex in auxiliary domains, which can be selected from "soft"(default, subgroups based on samples of soft clustering via posterior probability ) and "hard" (subgroups based on samples of hard clustering).

cov.method

The method of aggregating K auxiliary covariance matrices, which can be selected as "size" (the sum weighted by the sample sizes), "weight" (the sum weighted by the differences) or "opt" (select the optimal one).

cn.lam2

A vector or a float value: the coefficients set in tuning parameters used to solve the target precision matrix, default is cn.lam2*sqrt( log(p) / n ).

clambda.m

The coefficients set in tuning parameters used in transfer learning for mean eatimation, and the default setting is clambda.m * sqrt( log(p) / n ).

theta.algm

The optimization algorithm used to solve the precision, which can be selected as "admm" (ADMM algorithm) or "cd" (coordinate descent).

initial.selection

The different initial values from two clustering methods, which can be selected from c("K-means","dbscan").

preselect.aux

Whether to pre-select informative auxiliary domains based on the distance between initially estimated auxiliary and target parameters. The default is 0, which means that pre-selection will not be performed. If "preselect.aux" is specified as a real number greater than zero, then the threshold value is forpreselect.auxssqrt( log(p) / n ).

sel.type

If pre-selection should be performed, "sel.type" is the type of distance. The default is L2 norm, and can be specified as "L1" to use L1 norm.

trace

The logical variable, whether or not to output the number of identified subgroups during the search for parameters in the initialization.

Value

A result list including:

res.target

A list including transfer learning results of the target domain.

res.target$opt_Mu_hat

The final estimation of means in all detected subgroups via transfer learning.

res.target$opt_Theta_hat

The final estimation of precision matrices in all detected subgroups via transfer learning.

res.target0

A list including initial results of the target domain.

res.target0$opt_Mu_hat

The initial estimation of means in all detected subgroups.

res.target0$opt_Theta_hat

The initial estimation of precision matrices in all detected subgroups.

t.res

A list including results of the transfer precision matrix for each subgroup.

Author(s)

Mingyang Ren [e-mail].

References

Ren, M. and Wang J. (2023). Local transfer learning of Gaussian graphical mixture models.

Examples

"Will be supplemented in the next version."