Multivariate Covariance Generalized Linear Models
Wald Tests for Fixed Effects in mcglm Models
Model Coefficients
Confidence Intervals for Model Parameters
Cross variability matrix
Generalized Error Sum of Squares
Chaser and Reciprocal Likelihood Algorithms
Fitted Values
Measures of Goodness-of-Fit
Gosho Information Criterion
Wald Tests for Dispersion Components
Bias-corrected Standard Error for Regression Parameters
Build a block-diagonal matrix of zeros.
Build the joint covariance matrix
Auxiliar function: Build F matrix for Wald multivariate test
Build omega matrix
Build the correlation matrix between response variables
Build variance-covariance matrix
Conditional Autoregressive Model Structure
Complete Data Set with NA
Autocorrelation Estimates
Conditional Hypotheses Tests
Core of the Pearson estimating function.
Pearson correction term
Cross-sensitivity
Compute the cross-variability matrix
Derivative of C with respect to rho.
Derivatives of the Cholesky decomposition
Derivative of exponential-matrix function
Derivatives of with respect to beta.
Exponential-matrix and its derivatives
Double Generalized Linear Models Structure
Distance Models Structure
Exponential-matrix covariance link function
Getting information about model parameters
Independent Model Structure
Automatic Initial Values
Link Functions
Auxiliar function transforms list to a vector.
Moving Average Model Structure
MANOVA-Type Test for Dispersion Components of mcglm Models
MANOVA-Type Test for Multivariate Covariance GLMs
Matrix Linear Predictor
Mixed Models Structure
Non-structured Covariance Model
Pearson Estimating Function
Quasi-Score Function
Remove Missing Observations from Matrix Linear Predictor
Robust Standard Errors for Regression Parameters
Random Walk Model Structure
Matrix product in sandwich form
Sensitivity matrix
Score Information Criterion for Covariance Components
Score Information Criterion for Regression Components
Auxiliary Function for Block-Diagonal Matrix Construction
Twin Model Covariance Structures
Update Regression Parameters
Update Covariance Parameters
Variability Matrix
Variance Functions for Generalized Linear Models
Fitting Multivariate Covariance Generalized Linear Models
Pseudo Akaike Information Criterion
Pseudo Bayesian Information Criterion
Pseudo Kullback-Leibler Information Criterion
Gaussian Pseudo-Loglikelihood
Diagnostic Plots for mcglm Objects
Print Method for mcglm Objects
Residuals for mcglm Objects
Rotnitzky--Jewell Information Criterion
Summary for mcglm Objects
Variance-Covariance Matrix for mcglm Objects
Fitting multivariate covariance generalized linear models (McGLMs) to data. McGLM is a general framework for non-normal multivariate data analysis, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link function combined with a matrix linear predictor involving known matrices. The models take non-normality into account in the conventional way by means of a variance function, and the mean structure is modelled by means of a link function and a linear predictor. The models are fitted using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of different types of response variables and covariance structures, including multivariate extensions of repeated measures, time series, longitudinal, spatial and spatio-temporal structures. The package offers a user-friendly interface for fitting McGLMs similar to the glm() R function. See Bonat (2018) <doi:10.18637/jss.v084.i04>, for more information and examples.