aldvmm.pred {aldvmm}

Predicting Expected Values from Adjusted Limited Dependent Variable Mixture Models

Description

aldvmm.pred makes predictions of observations in design matrices in 'X' using parameter estimates returned by aldvmm.

Usage

aldvmm.pred(par, X, y = NULL, psi, ncmp, dist, lcoef, lcpar, lcmp)

Arguments

par

a named numeric vector of parameter values.

X

a list of design matrices returned by aldvmm.mm. 'X' is of length 2 and includes a design matrix for the model of component distributions and a design matrix for the model of probabilities of group membership.

y

a numeric vector of observed outcomes from complete observations in 'data' supplied to aldvmm.

psi

a numeric vector of minimum and maximum possible utility values smaller than or equal to 1 (e.g. c(-0.594, 0.883)). The potential gap between the maximum value and 1 represents an area with zero density in the value set from which utilities were obtained. The order of the minimum and maximum limits in 'psi' does not matter.

ncmp

a numeric value of the number of components that are mixed. The default value is 2. A value of 1 represents a tobit model with a gap between 1 and the maximum value in 'psi'.

dist

an optional character value of the distribution used in the components. In this release, only the normal distribution is available, and the default value is set to "normal".

lcoef

a character vector of length 2 with labels of objects including regression coefficients of component distributions (default "beta") and coefficients of probabilities of component membership (default "delta").

lcpar

a character vector with the labels of objects including constant parameters of component distributions (e.g. the standard deviation of the normal distribution). The length of 'lcpar' depends on the distribution supplied to 'dist'.

lcmp

a character value representing a stub (default "Comp") for labeling objects including regression coefficients in different components (e.g. "Comp1", "Comp2", ...). This label is also used in summary tables returned by summary.aldvmm.

Details

aldvmm.pred calculates expected values for observations in design matrices in 'X' using the expected value function published in Hernandez Alava and Wailoo (2015). Constant distribution parameters that need to be non-negative (i.e. standard deviations of normal distributions) enter the expected value function as log-transformed values.

Value

a list of of predicted outcomes including the following elements.

y

a numeric vector of observed outcomes in 'data'.

yhat

a numeric vector of fitted values.

res

a numeric vector of residuals.

prob

a numeric matrix of expected probabilities of group membership per individual in 'data'.