Fit multivariate discrete distributions
Fit the specified multivariate discrete distribution.
DMD.DM.fit(data, init, weight, epsilon = 1e-08, maxiters = 150, display = FALSE) DMD.GDM.fit(data, init, weight, epsilon = 1e-08, maxiters = 150, display = FALSE) DMD.NegMN.fit(data, init, weight, epsilon = 1e-08, maxiters = 150, display = FALSE) MGLMfit(data, dist, init, weight, epsilon = 1e-08, maxiters = 150, display = FALSE)
data |
a data frame or matrix containing the count data. Rows of the matrix represent observations and columns are the categories. Rows and columns of all zeros are automatically removed. |
init |
an optional vector of initial value of the parameter estimates. Should have the same dimension as the estimated parameters. See |
weight |
an optional vector of weights assigned to each row of the data. Should be Null or a numeric vector with the length equal to the number of rows of |
epsilon |
an optional numeric controlling the stopping criterion. The algorithm terminates when the relative change in the log-likelihoods of two successive iterates is less than |
maxiters |
an optional number controlling the maximum number of iterations. The default value is |
display |
an optional logical variable controlling the display of iterations. The default value is FALSE. |
dist |
a description of the distribution to fit. Choose from |
See dist for details about model parameterization.
Returns an object of S4 class "MGLMfit". An object of class "MGLMfit" is a list containing at least the following components:
estimate the vector of the distribution prameter estimates.
SE the vector of standard errors of the estimates.
vcov the variance-covariance matrix of the estimates.
logL the loglikelihood value.
iter the number of iterations used.
BIC Bayesian information criterion.
AIC Akaike information criterion.
distribution the distribution fitted.
LRT when dist="DM" or "GDM", it is the likelihood ratio test statistic for comparing the current model to the multinomial model. No LRT provided when dist="NegMN".
LRTpvalue the likelihood ratio test P value.
gradient the gradient at the estimated parameter values.
DoF the degrees of freedom of the model.
Yiwen Zhang and Hua Zhou
data(rnaseq) Y <- as.matrix(rnaseq[, 1:6]) fit <- MGLMfit(data=Y, dist="GDM")
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