Estimate Latent Classes on a Mixture of Continuous and Dichotomous Data
M-step driver to be used in flexmix accounting for heteroscedastisity
M-step driver to be used in flexmix
extract parameter estimates as named vector
function to decode which group or observation was classified to which ...
function for model estimation for EQ-5D valueset data accounting for h...
function for model estimation for EQ-5D valuesets
plot function to visualize the classification based on the model estim...
construct model summary as in base R
creating environment for package internal objects
EQ-5D value set estimation can be done using the hybrid model likelihood as described by Oppe and van Hout (2010) <doi:10.1002/hec.3560> and Ramos-Goñi et al. (2017) <doi:10.1097/MLR.0000000000000283 >. The package is based on `flexmix()` and among others contains an M-step-driver as described by Leisch (2004) <doi:10.18637/jss.v011.i08>. Users can estimate latent classes and address preference heterogeneity. Both uncensored and censored data are supported. Furthermore, heteroscedasticity can be taken into account. It is possible to control for different covariates on the continuous and dichotomous parts of the data and start values can differ between the expected latent classes.