mcglm: Multivariate Covariance Generalized Linear Models

Fitting multivariate covariance generalized linear models (McGLMs) to data. McGLMs 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.

AuthorWagner Hugo Bonat [aut, cre], Walmes Marques Zeviani [ctb], Fernando de Pol Mayer [ctb]
Date of publication2016-06-09 20:23:56
MaintainerWagner Hugo Bonat <wbonat@ufpr.br>
LicenseGPL-3 | file LICENSE
Version0.3.0
https://github.com/wbonat/mcglm

View on CRAN

Man pages

ahs: Australian Health Survey

anova.mcglm: Anova Tables

coef.mcglm: Model Coefficients

confint.mcglm: Confidence Intervals for Model Parameters

covprod: Cross variability matrix

ESS: Generalized Error Sum of Squares

fit_mcglm: Chaser and Reciprocal Likelihood Algorithms

fitted.mcglm: Fitted Values

gof: Measures of Goodness-of-Fit

GOSHO: Gosho Information Criterion

Hunting: Hunting in Pico Basile, Bioko Island, Equatorial Guinea.

mc_bias_corrected_std: Bias-corrected Standard Error for Regression Parameters

mc_build_bdiag: Build a block-diagonal matrix of zeros.

mc_build_C: Build the joint covariance matrix

mc_build_omega: Build omega matrix

mc_build_sigma: Build variance-covariance matrix

mc_build_sigma_between: Build the correlation matrix between response variables

mc_car: Conditional Autoregressive Model Structure

mc_compute_rho: Autocorrelation Estimates

mc_conditional_test: Conditional Hypotheses Tests

mc_core_pearson: Core of the Pearson estimating function.

mc_correction: Pearson correction term

mc_cross_sensitivity: Cross-sensitivity

mc_cross_variability: Compute the cross-variability matrix

mc_derivative_cholesky: Derivatives of the Cholesky decomposition

mc_derivative_C_rho: Derivative of C with respect to rho.

mc_derivative_expm: Derivative of exponential-matrix function

mc_derivative_sigma_beta: Derivatives of V^1/2 with respect to beta.

mc_dexp_gold: Exponential-matrix and its derivatives

mc_dist: Distance Models Structure

mc_expm: Exponential-matrix covariance link function

mc_getInformation: Getting information about model parameters

mcglm: Fitting Multivariate Covariance Generalized Linear Models

mc_id: Independent Model Structure

mc_initial_values: Automatic Initial Values

mc_link_function: Link Functions

mc_list2vec: Auxiliar function transforms list to a vector.

mc_ma: Moving Average Models Structure

mc_matrix_linear_predictor: Matrix Linear Predictor

mc_mixed: Mixed Models Structure

mc_pearson: Pearson estimating function

mc_quasi_score: Quasi-score function

mc_robust_std: Robust Standard Error for Regression Parameters

mc_rw: Random Walk Models Structure

mc_sandwich: Matrix product in sandwich form

mc_sensitivity: Sensitivity matrix

mc_sic: Score Information Criterion - Regression

mc_sic_covariance: Score Information Criterion - Covariance

mc_transform_list_bdiag: Auxiliar function to compute the derivatives of the C matrix.

mc_updateBeta: Updated regression parameters

mc_updateCov: Updated covariance parameters

mc_variability: Variability matrix

mc_variance_function: Variance Functions

NewBorn: Respiratory Physiotherapy on Premature Newborns.

pAIC: Pseudo Akaike Information Criterion

pKLIC: Pseudo Kullback-Leibler Information Criterion

plogLik: Gaussian Pseudo-loglikelihood

plot.mcglm: Residuals and algorithm check plots

print.mcglm: Print

residuals.mcglm: Residuals

RJC: Rotnitzky-Jewell Information Criterion

soil: Soil Chemistry Properties Data

soya: Soybeans

summary.mcglm: Summarizing

vcov.mcglm: Variance-Covariance Matrix

Files in this package

mcglm
mcglm/inst
mcglm/inst/doc
mcglm/inst/doc/vignette-01.html
mcglm/inst/doc/GLMExamples.html
mcglm/inst/doc/GLMExamples.R
mcglm/inst/doc/UniModels.R
mcglm/inst/doc/vignette-01.Rmd
mcglm/inst/doc/vignette-01.R
mcglm/inst/doc/GLMExamples.Rmd
mcglm/inst/doc/UniModels.Rmd
mcglm/inst/doc/UniModels.html
mcglm/tests
mcglm/tests/testthat.R
mcglm/tests/testthat
mcglm/tests/testthat/test_mc_build_sigma_between.R
mcglm/tests/testthat/test_mc_link_function.R
mcglm/tests/testthat/test_mc_matrix_linear_predictor.R
mcglm/tests/testthat/test_mc_build_C.R
mcglm/tests/testthat/test_mc_build_omega.R
mcglm/tests/testthat/test_mc_build_sigma.R
mcglm/tests/testthat/test_mc_variance_function.R
mcglm/tests/testthat/test_mc_quasi_score.R
mcglm/NAMESPACE
mcglm/data
mcglm/data/NewBorn.RData
mcglm/data/soya.RData
mcglm/data/ahs.RData
mcglm/data/Hunting.RData
mcglm/data/soil.RData
mcglm/R
mcglm/R/mc_cross_sensitivity.R mcglm/R/mc_pearson.R mcglm/R/mc_transform_list_bdiag.R mcglm/R/mc_getInformation.R mcglm/R/mc_variability.R mcglm/R/mc_cross_variability.R mcglm/R/mc_conditional_test.R mcglm/R/mc_compute_rho.R mcglm/R/mc_correction.R mcglm/R/mc_build_bdiag.R mcglm/R/mc_build_omega.R mcglm/R/zzz_onAttach.R mcglm/R/mc_derivative_expm.R mcglm/R/mc_build_C.R mcglm/R/mc_id.R mcglm/R/mc_dexp_gold.R mcglm/R/mcglm.R mcglm/R/mc_pAIC.R mcglm/R/mc_KLIC.R mcglm/R/mc_S3_methods.R mcglm/R/mc_derivative_C_rho.R mcglm/R/mc_dist.R mcglm/R/mc_derivative_cholesky.R mcglm/R/mc_ma.R mcglm/R/mc_plogLik.R mcglm/R/mc_main_function.R mcglm/R/mc_sic_covariance.R mcglm/R/mc_car.R mcglm/R/mc_ess.R mcglm/R/mc_rw.R mcglm/R/mc_initial_values.R mcglm/R/mc_updatedCov.R mcglm/R/mc_RJC.R mcglm/R/mc_bias_correct_std.R mcglm/R/mc_matrix_linear_predictor.R mcglm/R/mc_robust_std.R mcglm/R/mc_auxiliar.R mcglm/R/mc_core_pearson.R mcglm/R/mc_gosho.R mcglm/R/mc_sensitivity.R mcglm/R/mc_dexpm.R mcglm/R/mc_build_sigma.R mcglm/R/mc_core_cross_variability.R mcglm/R/mc_sic.R mcglm/R/mc_gof.R mcglm/R/fit_mcglm.R mcglm/R/mc_derivative_sigma_beta.R mcglm/R/mc_list2vec.R mcglm/R/mc_variance_function.R mcglm/R/mc_updatedBeta.R mcglm/R/mc_build_sigmab.R mcglm/R/mc_quasi_score.R mcglm/R/mc_link_function.R mcglm/R/mc_mixed.R
mcglm/vignettes
mcglm/vignettes/_output.yaml
mcglm/vignettes/vignette-01.Rmd
mcglm/vignettes/GLMExamples.Rmd
mcglm/vignettes/UniModels.Rmd
mcglm/vignettes/style.css
mcglm/vignettes/MathJax.html
mcglm/MD5
mcglm/build
mcglm/build/vignette.rds
mcglm/DESCRIPTION
mcglm/man
mcglm/man/mc_bias_corrected_std.Rd mcglm/man/mc_sic.Rd mcglm/man/mc_link_function.Rd mcglm/man/plogLik.Rd mcglm/man/mc_sandwich.Rd mcglm/man/mc_core_pearson.Rd mcglm/man/soya.Rd mcglm/man/mc_robust_std.Rd mcglm/man/NewBorn.Rd mcglm/man/covprod.Rd mcglm/man/confint.mcglm.Rd mcglm/man/mc_compute_rho.Rd mcglm/man/mc_dexp_gold.Rd mcglm/man/mc_expm.Rd mcglm/man/mc_id.Rd mcglm/man/mc_quasi_score.Rd mcglm/man/mc_ma.Rd mcglm/man/mc_build_sigma_between.Rd mcglm/man/mc_derivative_expm.Rd mcglm/man/GOSHO.Rd mcglm/man/mc_build_sigma.Rd mcglm/man/mc_cross_variability.Rd mcglm/man/mc_list2vec.Rd mcglm/man/mc_car.Rd mcglm/man/mc_matrix_linear_predictor.Rd mcglm/man/summary.mcglm.Rd mcglm/man/mc_cross_sensitivity.Rd mcglm/man/print.mcglm.Rd mcglm/man/mc_variability.Rd mcglm/man/mc_mixed.Rd mcglm/man/mc_build_bdiag.Rd mcglm/man/mc_updateCov.Rd mcglm/man/mc_sic_covariance.Rd mcglm/man/pAIC.Rd mcglm/man/mc_derivative_cholesky.Rd mcglm/man/mc_build_C.Rd mcglm/man/mc_derivative_sigma_beta.Rd mcglm/man/mc_rw.Rd mcglm/man/mcglm.Rd mcglm/man/mc_transform_list_bdiag.Rd mcglm/man/mc_correction.Rd mcglm/man/vcov.mcglm.Rd mcglm/man/mc_build_omega.Rd mcglm/man/mc_derivative_C_rho.Rd mcglm/man/ESS.Rd mcglm/man/soil.Rd mcglm/man/mc_getInformation.Rd mcglm/man/Hunting.Rd mcglm/man/mc_initial_values.Rd mcglm/man/mc_pearson.Rd mcglm/man/mc_sensitivity.Rd mcglm/man/pKLIC.Rd mcglm/man/plot.mcglm.Rd mcglm/man/RJC.Rd mcglm/man/ahs.Rd mcglm/man/fit_mcglm.Rd mcglm/man/mc_conditional_test.Rd mcglm/man/fitted.mcglm.Rd mcglm/man/coef.mcglm.Rd mcglm/man/residuals.mcglm.Rd mcglm/man/mc_updateBeta.Rd mcglm/man/mc_variance_function.Rd mcglm/man/anova.mcglm.Rd mcglm/man/mc_dist.Rd mcglm/man/gof.Rd
mcglm/LICENSE

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.