huge.npn(), huge.scr(), huge.subgraph(), huge.glassoM() sequentially as a pipeline to analyze data.huge(L, ind.group = NULL, lambda = NULL, nlambda = NULL, lambda.min.ratio = 0.1,
alpha = 1, sym = "or", npn = TRUE, npn.func = "shrinkage", npn.thresh = NULL,
method = "GEL", scr = TRUE, scr.num = NULL, verbose = TRUE)L: (1) An n by d data matrix L representing n observations in d dimensions. (2) A list L containing L$data as an k vector indexing a subset of all d variables. ONLY applicable when estimating a subgraph of the whole graph. The default value is c(1:d).30 if method = "GACT" and 10 if method = "GEL" or method = "GLASSO".method = "GEL" or method = "GLASSO", it is the smallest value for lambda, as a fraction of the uppperbound (MAX) of the regularization/thresholding parameter which makes all estimates equal to 01 (lasso). When some dense pattern exists in the graph or some variables are highly correlated, the elastic-net is encouraged for its grouping effect. ONLY applicable wsym = "and", the edge between node i and node j is selected ONLY when both node i and node j are selected as neighbors for each other. If sym = "or"npn = TRUE, the nonparanormal transformation is applied to the input data L or L$data. The default value is TRUE.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)))."GEL", "GACT" and "GLASSO". The defaulty value is "GEL".scr = TRUE, the Graph Sure Screening(GSS) is applied to preselect the neighborhood before GEL. The default value is TRUE for n and FALSE for n>=d. ONLY applicable when method = "G scr = TRUE. The default value is n-1. An alternative value is n/log(n). ONLY applicable when scr = TRUEverbose = FALSE, tracing information printing is disabled. The default value is TRUE."huge" is returned:n by d data matrix from the inputind.group 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 = FALSEalpha from the input. ONLY applicable when approx = FALSE.sym from the input. ONLY applicable when approx = FALSE.npn from the input.scr from the input. ONLY applicable when approx = FALSE.k and "fullgraph path" when k==d. k by k adjacency matrices of estimated graphs as a solution path (graph path) corresponding to lambda.d by d precision matrices as an alternative solution path (numerical path) corresponding to lambda. ONLY applicable when {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 = "GEL".method = "GEL", 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 solution path wi.nlambda dimensional vector containing the likelihood scores along the solution path (wi). ONLY applicable when
method = "GLASSO"huge.generator, huge.npn, huge.scr, huge.subgraph, huge.glassoM, huge.select, huge.plot, huge.roc, lasso.stars and huge-package.#generate data
L = huge.generator(n = 200, d = 80, graph = "hub")
ind.group = c(1:50)
#subgraph solution path estimation with input as a list
out1 = huge(L, ind.group = ind.group)
summary(out1)
plot(out1)
plot(out1, align = TRUE)
huge.plot(out1$path[[3]])
plot(out1$lambda,out1$sparsity)
#subgraph solution path estimation using the GACT
out2 = huge(L$data, ind.group = ind.group, method = "GACT")
summary(out2)
plot(out2)
#fullgraph solution path estimation using the GLASSO
out3 = huge(L, method = "GLASSO")
summary(out3)
plot(out3)Run the code above in your browser using DataLab