AIC.hopit

object

additional objects of the class <code>hopit</code>.

a penalty per parameter to be used; the default k = 2 is the
classical AIC.

Extract the Akaike Information Criterion (AIC) from a fitted <code>hopit</code> model.

internal

Self-reported health, happiness, attitudes, and other statuses or
perceptions are often the subject of biases that may come from different
sources. For example, the evaluation of own health may depend on previous
medical diagnoses, functional status, and symptoms and signs of illness, as
well as life-style behaviors including contextual social, gender, age-specific,
linguistic and other cultural factors (Jylha 2009
<doi:10.1016/j.socscimed.2009.05.013>; Oksuzyan et al. 2019
<doi:10.1016/j.socscimed.2019.03.002>). This package offers versatile functions
for analyzing different self-reported ordinal variables and helping to estimate
their biases.
Specifically, the package provides the function to fit a generalized ordered
probit model that regresses original self-reported status measures on two sets
of independent variables (King et al. 2004 <doi:10.1017/S0003055403000881>;
Jurges 2007 <doi:10.1002/hec.1134>; Oksuzyan et al. 2019
<doi:10.1016/j.socscimed.2019.03.002>). In contrast to standard ordered probit
models, generalized ordered probit models relax the assumption that individuals
use a common scale when rating their own statuses, and thus allow for
distinguishing between the status (e.g., health) and reporting differences
based on other individual characteristics. In other words, the model accounts
for heterogeneity in reporting behaviors. The first set of variables
(e.g., health variables) included in the regression are individual statuses and
characteristics that are directly related to the self-reported variable.
In case of self-reported health, these could be chronic conditions, mobility
level, difficulties with daily activities, performance on grip strength tests,
anthropometric measures, and lifestyle behaviors. The second set of independent
variables (threshold variables) is used to model cut-points between adjacent
self-reported response categories as functions of individual characteristics,
such as gender, age group, education, and country
(Oksuzyan et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>).
The model helps adjust for these specific socio-demographic and cultural
differences in how the continuous latent health is projected onto the ordinal
self-rated measure.
The fitted model can be used to calculate an individual latent status variable
that serves as a proxy of the true status. In case of self-reported health, the
predicted latent health variable can be standardized to a health index, which
varies from 0 representing the (model-based) worst health state to 1
representing the (model-based) best health in the sample.
The standardized latent coefficients (disability weights for the case of
self-rated health) provide information about the individual impact of the
specific latent (e.g., health) variables on the latent (e.g., health)
construct. For example, they indicate the extent to which the latent health
index is reduced by the presence of Parkinson<e2><80><99>s disease, poor mobility, and
other specific health measures (Jurges 2007 <doi:10.1002/hec.1134>; Oksuzyan
et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>).
The latent index can in turn be used to reclassify the categorical status
measure that has been adjusted for inter-individual differences in reporting
behavior. Two methods for doing so are available, one which uses model
estimated cut-points, and a second which reclassifies responses according to
the percentiles of the original categorical response distribution
(Jurges 2007 <doi:10.1002/hec.1134>;
Oksuzyan et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>).

AIC.hopit: Extracting the Akaike Information Criterion from the fitted model

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Arguments