| Type: | Package |
| Title: | Semi-Parametric Estimation with Gaussian Copula |
| Version: | 1.0.0 |
| Description: | A method for estimating the correlation matrix of the Gaussian copula from the observed data. This package also contains a penalized estimation of the corresponding precision matrix, and enables to generate random vectors that are distributed according to a Gaussian copula. |
| Imports: | mvtnorm, stats, igraph, matrixcalc, graphics, foreach, stringr, doSNOW, utils, huge |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.2 |
| Depends: | R (≥ 2.10) |
| Suggests: | knitr, rmarkdown, kableExtra, dplyr |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2025-06-06 09:08:54 UTC; etomilina |
| Author: | Julie Cartier [aut], Florence Jaffrezic [aut], Gildas Mazo [aut], Ekaterina Tomilina [aut, cre] |
| Maintainer: | Ekaterina Tomilina <ekaterina.tomilina@inrae.fr> |
| Repository: | CRAN |
| Date/Publication: | 2025-06-06 09:20:02 UTC |
CopulaSim
Description
This function enables the user to simulate data from a Gaussian copula and arbitrary marginal quantile functions
Usage
CopulaSim(n, R, qdist, random = FALSE)
Arguments
n |
the number of observations |
R |
a correlation matrix of size dxd |
qdist |
a vector containing the names of the marginal quantile functions as well as the number of times they are present in the dataset |
random |
a boolean defining whether the order of the correlation coefficients should be randomized |
Value
a list containing an nxd data frame, the shuffled correlation matrix R, and the permutation leading to the new correlation matrix
Examples
M <- diag_block_matrix(c(3,4,5),c(0.7,0.8,0.2))
CopulaSim(20,M,c(rep("qnorm(0,1)",6),rep("qexp(0.5)",4),rep("qbinom(4,0.8)",2)),random=TRUE)
cor_network_graph
Description
This function enables the user to plot the graph corresponding to the correlations of the Gaussian copula
Usage
cor_network_graph(R, TS, binary = TRUE, legend)
Arguments
R |
a correlation matrix of size dxd (d is the number of variables) |
TS |
a threshold for the absolute values of the correlation matrix coefficients |
binary |
a boolean specifying whether the coefficients should be binarized, TRUE by defaut (zero if the coefficient is less than the threshold in absolute value, 1 otherwise). If FALSE, the edge width is proportional to the coefficient value. |
legend |
a vector containing the type of each variable used to color the vertices |
Value
a graph representing the correlations between the latent Gaussian variables
Examples
R <- diag_block_matrix(c(3,4,5),c(0.7,0.8,0.2))
data <- CopulaSim(20,R,c(rep("qnorm(0,1)",6),rep("qexp(0.5)",4),
rep("qbinom(4,0.8)",2)),random=FALSE)[[1]]
cor_network_graph(R,TS=0.3,binary=TRUE,legend=c(rep("Normal",6),
rep("Exponential",4),rep("Binomial",2)))
diag_block_matrix
Description
This function enables the user to generate a diagonal block-matrix with homogeneous blocks
Usage
diag_block_matrix(blocks, coeff)
Arguments
blocks |
a vector containing the sizes of the blocks |
coeff |
a vector containing the coefficient corresponding to each block, the coefficients must be between 0 and 1 |
Value
a diagonal block-matrix containing the specified coefficients
Examples
diag_block_matrix(c(3,4,5),c(0.3,0.4,0.8))
gauss_gen
Description
This function enables the user to generate gaussian vectors with correlation matrix R
Usage
gauss_gen(R, n)
Arguments
R |
a correlation matrix of size dxd |
n |
the number of observations |
Value
a nxd data frame containing n observations of the d variables
Examples
M <- diag_block_matrix(c(3,4,5),c(0.7,0.8,0.2))
gauss_gen(M,20)
ICGC dataset
Description
Dataset containing RNA counts, protein expression and mutations measured on breast cancer tumors.
Usage
icgc_data
Format
A dataframe of 15 variables and 250 observations containing the following:
- ACACA, AKT1S1, ANLN,ANXA1,AR
RNA counts (discrete)
- ACACA_P, AKT1S1_P, ANLN_P,ANXA_P,AR_P
protein expression measurements (discrete)
- MU5219,MU4468,MU7870,MU4842,MU6962
5 mutations (binary)
matrix_cor_ts
Description
This function enables the user to threshold matrix coefficients
Usage
matrix_cor_ts(R, TS, binary = TRUE)
Arguments
R |
a correlation matrix |
TS |
a threshold |
binary |
a boolean specifying whether the coefficients should be binarized, TRUE by defaut (zero if the coefficient is less than the threshold in absolute value, 1 otherwise) |
Value
the thresholded input matrix
Examples
M <- diag_block_matrix(c(3,4,5),c(0.7,0.8,0.2))
matrix_cor_ts(M,0.5)
matrix_gen
Description
This function enables the user to generate a sparse, nonnegative definite correlation matrix via the Cholesky decomposition
Usage
matrix_gen(d, gamma)
Arguments
d |
the number of variables |
gamma |
an initial sparsity parameter for the lower triangular matrices in the Cholesky decomposition, must be between 0 and 1 |
Value
a list containing the generated correlation matrix and its final sparsity parameter (ie the proportion of zeros)
Examples
matrix_gen(15,0.81)
omega_estim
Description
This function enables the user estimate the precision matrix of the latent variables via gLasso inversion
Usage
omega_estim(data, Type, lambda, n)
Arguments
data |
a dataset of size nxd or a correlation matrix R of size dxd |
Type |
a vector containing the type of the variables, "C" for continuous and "D" for discrete (in the case a data set is entered as the first parameter) |
lambda |
a grid of penalization parameters to be evaluated |
n |
the sample size used (in the case of a correlation matrix entered as the first parameter) |
Value
a list containing the correlation matrix, the optimal precision matrix, the optimal lambda, the minimal HBIC, all values of lambda, all corresponding HBIC values
Examples
M <- diag_block_matrix(c(3,4,5),c(0.7,0.8,0.2))
data <- CopulaSim(20,M,c(rep("qnorm(0,1)",6),rep("qexp(0.5)",4),
rep("qbinom(4,0.8)",2)),random=FALSE)[[1]]
## Not run: P <- omega_estim(data,c(rep("C",10),rep("D",2)),seq(0.01,1,0.05))
rho_estim
Description
This function enables the user to estimate the correlation matrix of the Gaussian copula for a given dataset
Usage
rho_estim(data, Type, ncores = 1)
Arguments
data |
an nxd data frame containing n observations of d variables |
Type |
a vector containing the type of the variables, "C" for continuous and "D" for discrete |
ncores |
an integer specifying the number of cores to be used for parallel computation. "1" by default, leading to non-parallel computation. |
Value
the dxd estimated correlation matrix of the Gaussian copula
Examples
M <- diag_block_matrix(c(3,4,5),c(0.7,0.8,0.2))
data <- CopulaSim(20,M,c(rep("qnorm(0,1)",6),rep("qexp(0.5)",4),
rep("qbinom(4,0.8)",2)),random=FALSE)[[1]]
rho_estim(data,c(rep("C",10),rep("D",2)))