huge | R Documentation |
The main function for high-dimensional undirected graph estimation. Three graph estimation methods, including (1) Meinshausen-Buhlmann graph estimation (mb
) (2) graphical lasso (glasso
) (3) correlation thresholding graph estimation (ct
) and (4) tuning-insensitive graph estimation (tiger
), are available for data analysis.
huge( x, lambda = NULL, nlambda = NULL, lambda.min.ratio = NULL, method = "mb", scr = NULL, scr.num = NULL, cov.output = FALSE, sym = "or", verbose = TRUE )
x |
There are 2 options: (1) |
lambda |
A sequence of decreasing positive numbers to control the regularization when |
nlambda |
The number of regularization/thresholding parameters. The default value is |
lambda.min.ratio |
If |
method |
Graph estimation methods with 4 options: |
scr |
If |
scr.num |
The neighborhood size after the lossy screening rule (the number of remaining neighbors per node). ONLY applicable when |
cov.output |
If |
sym |
Symmetrize the output graphs. If |
verbose |
If |
The graph structure is estimated by Meinshausen-Buhlmann graph estimation or the graphical lasso, and both methods can be further accelerated via the lossy screening rule by preselecting the neighborhood of each variable by correlation thresholding. We target on high-dimensional data analysis usually d >> n, and the computation is memory-optimized using the sparse matrix output. We also provide a highly computationally efficient approaches correlation thresholding graph estimation.
An object with S3 class "huge"
is returned:
data |
The |
cov.input |
An indicator of the sample covariance. |
ind.mat |
The |
lambda |
The sequence of regularization parameters used in mb or thresholding parameters in ct. |
sym |
The |
scr |
The |
path |
A list of |
sparsity |
The sparsity levels of the graph path. |
icov |
A list of |
cov |
A list of |
method |
The method used in the graph estimation stage. |
df |
If |
loglik |
A |
This function ONLY estimates the graph path. For more information about the optimal graph selection, please refer to huge.select
.
huge.generator
, huge.select
, huge.plot
, huge.roc
, and huge-package
.
#generate data L = huge.generator(n = 50, d = 12, graph = "hub", g = 4) #graph path estimation using mb out1 = huge(L$data) out1 plot(out1) #Not aligned plot(out1, align = TRUE) #Aligned huge.plot(out1$path[[3]]) #graph path estimation using the sample covariance matrix as the input. #out1 = huge(cor(L$data), method = "glasso") #out1 #plot(out1) #Not aligned #plot(out1, align = TRUE) #Aligned #huge.plot(out1$path[[3]]) #graph path estimation using ct #out2 = huge(L$data,method = "ct") #out2 #plot(out2) #graph path estimation using glasso #out3 = huge(L$data, method = "glasso") #out3 #plot(out3) #graph path estimation using tiger #out4 = huge(L$data, method = "tiger") #out4 #plot(out4)
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