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netregR (version 1.0.1)

Regression of Network Responses

Description

Regress network responses (both directed and undirected) onto covariates of interest that may be actor-, relation-, or network-valued. In addition, compute principled variance estimates of the coefficients assuming that the errors are jointly exchangeable. Missing data is accommodated. Additionally implements building and inversion of covariance matrices under joint exchangeability, and generates random covariance matrices from this class. For more detail on methods, see Marrs, Fosdick, and McCormick (2017) .

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Version

Install

install.packages('netregR')

Monthly Downloads

150

Version

1.0.1

License

MIT + file LICENSE

Maintainer

Frank W. Marrs

Last Published

August 1st, 2018

Functions in netregR (1.0.1)

Find all possible combinations of elements in two vectors, or all combinations of all elements in one without repeats

Calculate DC meat using rows of X, e

build_exchangeable_matrix

Build an exchangeable matrix of sparseMatrix class

build_phi_matrix

Build intermediate C(phi,n) matrix in inversion of Exchangeable variance matrix

Social interaction data set

invert_exchangeable_matrix

Invert an exchangeable matrix

Generate list indicator matrix of overlapping dyads

Perform GEE estimate / IRWLS of coefficients

Calculate parameter estimates using rows of e

calculate_matrix_params

calculate parameter estimates for different types of matrices, i.e. 6a, 6b, etc.

model.matrix.lmnet

model.matrix S3 generic for class lmnet

build_blocklist

build list of time blocks that are correlated based on the maximum time and type of temporal model #'

node_preprocess

Pre-processes data for ordering etc.

Generate node pairs for complete network

Linear regression for network response

param_est_single_ilist

Given matrix of time blocks and a particular exchangeable parameter set (within each block), calculate a single parameter/phi. ASSUMES NO MISSING DATA

Print S3 generic for vnet object

Calculate E meat using rows of X, e

make.positive.var

Replace negative eigenvalues with zeros in variance matrix

Matrix product of A^TBC where B is a short list of parameters A and C must be matrices B is parameterized by phi, row.list, and assumed symmetric without repeats, with phi[1] along diagonal

row_list_missing

Generate row list based on nodes input with missingness

calculate_parameter_inverse

Invert matrix parameters based on inputs.

Wolf network data set

Perform GEE estimate / IRWLS of coefficients for temporal data

Dyad map from nodes i,j --> dyad d

Plot S3 generic for class lmnet

Print S3 generic for class lmnet

Summary S3 generic for class lmnet

node_preprocess_time

Pre-processes data for ordering, FOR TEMPORAL DATA, etc.

Vectorize a network matrix (without diagonal)

Eigenvalues of exchangeable matrices if calcall == TRUE, then output eigenvalues with multiplicities Outputs eigenvectors when directed==FALSE

Variance computation for linear regression of network response

Summary S3 generic for vnet object

print.summary.lmnet

Print S3 generic for class summary.lmnet

print.summary.vnet

Print S3 generic for summary.vnet object

symm_square_root

Compute symmetric square root of A, assuming it is real, symmetric, positive definite

eigen_exch_time

Compute eigenvalues of covariance matrices of jointly exchangeable errors with repeated observations

vcov S3 generic for class lmnet

Input preprocessing

Generate node sets of various overlapping dyad pairs

Make complete node indices for temporal relational data

Make row list for complete temporal relational data

Generate positive definite phi set

Coef S3 generic for class lmnet

Matricize a network vector (without diagonal)