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Description

Adjusted Limited Dependent Variable Mixture Models.

The goal of the package 'aldvmm' is to fit adjusted limited dependent variable mixture models of health state utilities. Adjusted limited dependent variable mixture models are finite mixtures of normal distributions with an accumulation of density mass at the limits, and a gap between 100% quality of life and the next smaller utility value. The package 'aldvmm' uses the likelihood and expected value functions proposed by Hernandez Alava and Wailoo (2015) <doi:10.1177/1536867X1501500307> using normal component distributions and a multinomial logit model of probabilities of component membership.

aldvmm

The goal of 'aldvmm' is to fit adjusted limited dependent variable mixture models of health state utilities. Adjusted limited dependent variable mixture models are finite mixtures of normal distributions with an accumulation of density mass at the limits, and a gap between 100% quality of life and the next smaller utility value. The package 'aldvmm' uses the likelihood and expected value functions proposed by Hernandez Alava and Wailoo (2015) using normal component distributions and a multinomial logit model of probabilities of component membership.

Installation

You can install the released version of aldvmm from CRAN with:

install.packages("aldvmm")

Example

The package 'aldvmm' includes a simulated sample data set to run simple examples.

library("aldvmm")

data(utility)

fit <- aldvmm(eq5d ~ age + female | 1,
              data = utility,
              psi = c(0.883, -0.594),
              ncmp = 2,
              init.method = "constant",
              optim.method = "Nelder-Mead")

summary(fit)

yhat <- predict(fit)

Vignette

Please consult the vignette for further details on the methods and the usage of the package.

References

Hernández Alava, Mónica, and Allan Wailoo. 2015. “Fitting Adjusted Limited Dependent Variable Mixture Models to EQ-5D.” The Stata Journal 15 (3): 737–50. doi: 10.1177/1536867X1501500307

Metadata

Version

0.8.8

License

Unknown

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