Description Usage Arguments Details Value Author(s) See Also Examples

`MGLMreg`

fits multivariate response generalized linear models, specified by a symbolic description of the linear predictor and a description of the error distribution.

1 2 3 4 5 6 7 | ```
MGLMreg(formula, data, dist, init = NULL, weight = NULL, epsilon = 1e-08,
maxiters = 150, display = FALSE, LRT = FALSE, parallel = FALSE,
cores = NULL, cl = NULL, sys = NULL, regBeta = FALSE)
MGLMreg.fit(Y, init = NULL, X, dist, weight = NULL, epsilon = 1e-08,
maxiters = 150, display = FALSE, LRT = FALSE, parallel = FALSE,
cores = NULL, cl = NULL, sys = NULL, regBeta = FALSE)
``` |

`formula` |
an object of class |

`data` |
an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in |

`dist` |
a description of the error distribution to fit. See |

`init` |
an optional matrix of initial value of the parameter estimates. Should have the compatible dimension with |

`weight` |
an optional vector of weights assigned to each row of the data. Should be |

`epsilon` |
an optional numeric controlling the stopping criterion. The algorithm terminates when the relative change in the loglikelihoods of two successive iterates is less than |

`maxiters` |
an optional numeric controlling the maximum number of iterations. The default value is |

`display` |
an optional logical variable controlling the display of iterations. The default value is |

`LRT` |
an optional logical variable controlling whether to perform likelihood ratio test on each predictor. The default value is |

`parallel` |
an optional logical variable controlling whether to perform parallel computing. On a multi-core Windows machine, a cluster is created based on socket; on a multi-core Linux/Mac machine, a cluster is created based on forking. The default value is |

`cores` |
an optional value specifying the number of cores to use. Default value is half of the logical cores. |

`cl` |
a cluster object, created by the package parallel or by package snow. If |

`sys` |
the operating system. Will be used when choosing parallel type. |

`regBeta` |
an optional logical variable. When |

`Y, X` |
for |

The formula should be in the form responses ~ covariates where the responses are the multivariate count matrix or a few columns from a data frame which is specified by `data`

. The covariates are either matrices or from the data frame. The covariates can be numeric or character or factor.
See `dist`

for details about distributions.

Instead of using the formula, the user can directly input the design matrix and the response vector using `MGLMreg.fit`

function.

Returns an object of class `"MGLMreg"`

. An object of class `"MGLMreg"`

is a list containing the following components:

`coefficients`

the estimated regression coefficients.`SE`

the standard errors of the estimates.`Hessian`

the Hessian at the estimated parameter values.`gradient`

the gradient at the estimated parameter values.`wald.value`

the Wald statistics.`wald.p`

the p values of Wald test.`test`

test statistic and the corresponding p-value. If`LRT=FALSE`

, only returns test resultsfrom Wald test; if`LRT=TRUE`

, returns the test results from both Wald test and likelihood ratio test.`logL`

the final loglikelihood.`BIC`

Bayesian information criterion.`AIC`

Akaike information criterion.`fitted`

the fitted values from the regression model`iter`

the number of iterations used.`call`

the matched call.`distribution`

the distribution fitted.`data`

the data used to fit the model.`Dof`

degrees of freedom.

Yiwen Zhang and Hua Zhou

See also `MGLMfit`

for distribution fitting.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
##----------------------------------------##
## Generate data
n <- 2000
p <- 5
d <- 4
m <- rep(20, n)
set.seed(1234)
X <- 0.1* matrix(rnorm(n*p),n, p)
alpha <- matrix(1, p, d-1)
beta <- matrix(1, p, d-1)
Alpha <- exp(X %*% alpha)
Beta <- exp(X %*% beta)
gdm.Y <- rgdirmn(n, m, Alpha, Beta)
##----------------------------------------##
## Regression
gdm.reg <- MGLMreg(gdm.Y~X, dist="GDM", LRT=FALSE)
``` |

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