huge.NPN(), huge.GECT(), huge.MBGEL(), huge.glassoM() sequentially as a pipeline to analyze data.huge(L, lambda = NULL, nlambda = NULL, lambda.min.ratio = NULL, NPN = FALSE, NPN.func = "shrinkage", NPN.thresh = NULL, method = "MBGEL",
scr = NULL, scr.num = NULL, cov.glasso = FALSE, sym = "or", verbose = TRUE)L: (1) An n by d data matrix L representing n observations in d dimensions. (2) A list L containing L$data as an 30 if method = "GECT" and 10 if method = "MBGEL" or method = "GLASSO".method = "MBGEL" or method = "GLASSO", it is the smallest value for lambda, as a fraction of the uppperbound (MAX) of the regularization/thresholding parameter which makes all estimates equal to NPN = TRUE, the nonparanormal transformation is applied to the input data L or L$data. The default value is FALSE.NPN.func = "truncation", the truncated ECDF is applied. If NPN.func = "shrinkage", the shrunken ECDF is applied. The default value is "shrinkage"NPN.func = "truncation". The default value is
1/(4*(n^0.25)*sqrt(pi*log(n)))."MBGEL", "GECT" and "GLASSO". The defaulty value is "MBGEL".scr = TRUE, the Graph Sure Screening(GSS) is applied to preselect the neighborhood before MBGEL. The default value is TRUE for n and FALSE for n>=d. ONLY applicable when method = scr = TRUE. The default value is n-1. An alternative value is n/log(n). ONLY applicable when scr = TRUEcov.glasso = TRUE, the outpu will inlcude a path of estimated covariance matrices. ONLY applicable when method = "GLASSO". Since the estimated covariance matrices are generally not sparse, please use it with care, or it may tasym = "and", the edge between node i and node j is selected ONLY when both node i and node j are selected as neighbors for each other. If sym = "or"verbose = FALSE, tracing information printing is disabled. The default value is TRUE."huge" is returned:n by d data matrix from the inputscr.num by k matrix with each column correspondsing to a variable in ind.group and contains the indices of the remaining neighbors after the GSS. ONLY applicable when scr = TRUE and approx = FALSEsym from the input. ONLY applicable when method = "MBGEL".NPN from the input.scr from the input. ONLY applicable when method = "MBGEL".k by k adjacency matrices of estimated graphs as a graph path corresponding to lambda.d by d precision matrices as an alternative graph path (numerical path) corresponding to lambda. ONLY applicable when {method = "GLASSO"}d by d estimated covariance matrices corresponding to lambda. ONLY applicable when cov.glasso = TRUE and {method = "GLASSO"}k by nlambda matrix. Each row is corresponding to a variable in ind.group and contains all RSS's (Residual Sum of Squares) along the lasso solution path. ONLY applicable when method = "MBGEL".method = "MBGEL", it is a k by nlambda matrix. Each row corresponds to a variable in ind.group and contains the number of nonzero coefficients along the lasso solution path. If method = "GLASSO", it is a nlambda dimensional vector containing the number of nonzero coefficients along the graph path wi.nlambda dimensional vector containing the likelihood scores along the graph path (wi). ONLY applicable when
method = "GLASSO"huge.generator, huge.NPN, huge.GECT, huge.MBGEL, huge.glassoM, huge.select, huge.plot, huge.roc, lasso.stars and huge-package.#generate data
L = huge.generator(n = 200, d = 80, graph = "hub")
#graph path estimation with input as a list
out1 = huge(L)
summary(out1)
plot(out1)
plot(out1, align = TRUE)
huge.plot(out1$path[[3]])
plot(out1$lambda,out1$sparsity)
#graph path estimation using the GECT
out2 = huge(L$data,method = "GECT")
summary(out2)
plot(out2)
#graph path estimation using the GLASSO
out3 = huge(L, method = "GLASSO")
summary(out3)
plot(out3)Run the code above in your browser using DataLab