| Name : R-hopit
| |
| Version : 0.11.6
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp156.5.1
| Date : 2024-09-10 06:38:02
|
| Group : Development/Libraries/Other
| Source RPM : R-hopit-0.11.6-lp156.5.1.src.rpm
|
| Size : 0.79 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Hierarchical Ordered Probit Models with Application to Reporting Heterogeneity
|
Description :
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 an individual’s own
health may depend on previous medical diagnoses, functional status, and
symptoms and signs of illness; as on 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>). The hopit
package offers versatile functions for analyzing different
self-reported ordinal variables, and for 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>). 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 the 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 to adjust for 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 predicted latent status variable, a latent index, and
standardized latent coefficients; and makes it possible to reclassify a
categorical status measure that has been adjusted for inter-individual
differences in reporting behavior.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.6/x86_64 |
