latentclasslogit: Perform maximum-likelihood estimation for a latent class...
In WannabeSmith/mixturetobit: Estimate Tobit Mixtures Using the E-M Algorithm

Description Usage Arguments Value

View source: R/latentclasslogit.R

This function performs maximum-likelihood estimation via the E-M algorithm to obtain estimates of regression coefficients in a latent class logistic regression model.

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latentclasslogit(formula, data, K = 2, start.beta = NULL,
  start.lambda = NULL, id = NULL, tol = 1e-05, theta.lower = NULL,
  theta.upper = NULL, method = "L-BFGS-B")

`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.lambda`	a vector of length K of starting values for the mixing proportions
`id`	the (character) name of the column containing subject IDs
`tol`	a numeric tolerance used to determine convergence
`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 string specifying the optimization routine to be used by optim

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