pcgenFast {pcgen}

pcgen with residual-based screening

Description

The pcgen algorithm starting with a skeleton estimated using the standard pc-algorithm, based on residuals from the GBLUP.

Usage

pcgenFast(suffStat, alpha = 0.01, m.max = Inf, res.m.max = Inf, verbose = FALSE, 
          covariates = NULL, fixedEdges = NULL, QTLs = integer(), max.iter = 50,
          stop.if.significant = TRUE, cov.method = 'uni', use.res = FALSE, 
          return.pvalues = FALSE)

Arguments

suffStat

A data.frame, of which the first column is the factor G (genotype), and subsequent columns contain the traits, and optionally some QTLs. The name of the first column should be G.

alpha

The significance level used in each conditional independence test. Default is 0.01.

m.max

Maximum size of the conditioning set, in the pcgen algorithm.

res.m.max

Maximum size of the conditioning set, in the pc-algorithm on the residuals (used for prior screening).

verbose

If TRUE, p-values for the conditional independence tests are printed.

covariates

A data.frame containing covariates, to be used in each conditional independence test. Cannot contain factors. Should be either NULL (default) or a data.frame with the same number of rows as suffStat. An intercept is already included for each trait in suffStat; covariates should not contain a column of ones.

fixedEdges

A logical matrix of dimension (p+1) \times (p+1), where p is the number of traits. The first row and column refer to the node G, and subsequent rows and columns to the traits. As in the pcalg package, the edge i - j is never considered for removal if the entry [i, j] or [j, i] (or both) are TRUE. In that case, the edge is guaranteed to be present in the resulting graph.

QTLs

Column numbers in suffStat that correspond to QTLs.

max.iter

Maximum number of iterations in the EM-algorithm, used to fit the bivariate mixed model (when use.res = FALSE).

stop.if.significant

If TRUE, the EM-algorithm used in some of the conditional independence tests (when use.res = FALSE) will be stopped whenever the p-value becomes significant, i.e. below alpha. This will speed up calculations, and can be done because (1) the PC algorithm only needs an accept/reject decision (2) In EM the likelihood is nondecreasing. Should be put to FALSE if the precise p-values are of interest.

cov.method

A string, specifying which method should be used to compute the GBLUP. Options are 'us' (unstructured multi-trait model fitted using sommer) and 'uni' (based on univariate GBLUPs). Default is 'uni'.

use.res

If FALSE, residuals from GBLUP are only used for screening with the standard pc algotihm. After that, the standard pcgen algorithm is run on the remaining edges; the test for conditional independence of 2 traits given a set of other traits and G is based on bivariate mixed models. If TRUE, this test is based on the residuals. In this case, no further edges between traits are removed after screening and pcgen will only infer the orientation, and the direct genetic effects.

return.pvalues

If TRUE, the maximal p-value for each edge is returned.

Value

If return.pvalues = FALSE, the output is a graph (an object with S3 class "pcgen"). If return.pvalues = TRUE, the output is a list with elements gr (the graph) and pMax (a matrix with the p-values).

Author(s)

Willem Kruijer and Pariya Behrouzi. Maintainers: Willem Kruijer [e-mail] and Pariya Behrouzi [e-mail]

References

1. Kruijer, W., Behrouzi, P., Rodriguez-Alvarez, M. X., Wit, E. C., Mahmoudi, S. M., Yandell, B., Van Eeuwijk, F., (2018, in preparation), Reconstruction of networks with direct and indirect genetic effects.
2. Colombo, D. and Maathuis, M.H., 2014. Order-independent constraint-based causal structure learning. The Journal of Machine Learning Research, 15(1), pp.3741-3782.

See Also

getResiduals

Examples

data(simdata)
out <- pcgenFast(suffStat = simdata, alpha = 0.01, verbose= FALSE, use.res = TRUE)