The function initiates starting values. Users are allowed to set some non-null values to starting values for a set of parameters. The function will automatically generate starting values for any parameters whose values are not specified.
initiate_startValues(Formula, Y, data, model = "MMZIP", B = NULL, beta0 = NULL,
V = NULL, SigmaV = NULL, gamma_beta = NULL, A = NULL, alpha0 = NULL, W = NULL,
m = NULL, gamma_alpha = NULL, sigSq_beta = NULL, sigSq_beta0 = NULL,
sigSq_alpha = NULL, sigSq_alpha0 = NULL)initiate_startValues returns a list containing starting values that can be used for mmzipBvs.
a list containing three formula objects: the first formula specifies the \(p_z\) covariates for which variable selection is to be performed in the binary component of the model; the second formula specifies the \(p_x\) covariates for which variable selection is to be performed in the count part of the model; the third formula specifies the \(p_0\) confounders to be adjusted for (but on which variable selection is not to be performed) in the regression analysis.
a data.frame containing \(q\) count outcomes from n subjects. It is of dimension \(n\times q\).
a data.frame containing the variables named in the formulas in lin.pred.
MMZIP
starting values of \(B\)
starting values of \(\beta_0\)
starting values of \(B\)
starting values of \(\Sigma_V\)
starting values of \(\gamma_{\beta}\)
starting values of \(A\)
starting values of \(\alpha_0\)
starting values of \(W\)
starting values of \(m\)
starting values of \(\gamma_{\alpha}\)
starting values of \(\sigma_{\beta}^2\)
starting values of \(\sigma_{\beta_0}^2\)
starting values of \(\sigma_{\alpha}^2\)
starting values of \(\sigma_{\alpha_0}^2\)
Maintainer: Kyu Ha Lee <klee@hsph.harvard.edu>
update..
mmzipBvs
## See Examples in \code{\link{mmzipBvs}}.
Run the code above in your browser using DataLab