Description Usage Arguments Value
This function performs maximum-likelihood estimation via the E-M algorithm to obtain estimates of regression coefficients in a mixture of tobit regression models.
1 2 3 4 |
formula |
a regression formula describing the relationship between the response and the covariates |
data |
the data.frame containing the responses and covariates |
K |
the number of mixtures (or latent classes) |
start.beta |
a list of length K of starting values for each mixture's beta coefficients |
start.sigma |
a vector of length K of starting values for each mixture's sigma value |
start.lambda |
a vector of length K of starting values for the mixing proportions |
id |
a string specifying the name of the column that identifies subjects |
left |
a number specifying where left-censoring occurred |
tol |
a vector of numbers specifying the tolerance(s) used to determine convergence. If length(tol) > 1, the methods used should be supplied as a vector to "method". |
theta.lower |
a numeric vector of lower bounds for the theta parameters |
theta.upper |
a numeric vector of upper bounds for the theta parameters |
method |
a vector of strings specifying the optimization routine(s) to be used by optim. If length(method) > 1, optimization will be performed in succession with the tolerances provided to "tol" |
beta.starting.sd |
a numeric for what std deviation to use for starting the EM with random betas |
a list containing the following elements:
beta |
a list containing the estimated regression coefficients |
sigma |
a vector containing the estimated values of sigma |
lambda |
a vector containing the estimated mixing proportions |
delta |
a list of length K containing the estimated class membership probabilities for each observation |
ll |
the log-likelihood function evaluated at the MLE |
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