help scattercub

Title

scattercub - Module to estimate the "uncertainty vs. feeling" scatterplot for the model "cub00"

Syntax

scattercub
varlist
[if] [in]
[weight],
[
m(list_of_integers)
save_data(filename)
save_graph(filename)
]

fweights and pweights are allowed;
see weight.

Description

scattercub estimates the model cub00 (see cub) and generates the scatterplot of the "uncertainty vs. feeling" measures. The model cub00 is the simplest cub model possible as it does not contain covariates.
It takes ordinal outcome variables as inputs and draws the related "uncertainty vs. feeling" graph.

Options

m(list_of_integers) allows, for each ordinal dependent variable in varlist, to optionally specify the total number of categories. This total number comprises both observed and unobserved categories.
If this option is not specified, only observed categories are considered in the estimation phase.

save_data(filename) saves in filename the dataset containing the "uncertainty" and "feeling" measures.

save_graph(filename) saves in filename the "uncertainty vs. feeling" scatterplot.

Example

********************************************************************************
* LOAD THE DATASET
********************************************************************************
. use universtata.dta , clear
********************************************************************************
* ESTIMATE "cub00" AND PRODUCE THE SCATTERPLOT OF "UNCERTAINTY" VS. "FEELING"
********************************************************************************
. scattercub informat willingn officeho compete global , ///
m(9 10 12 10 12) save_graph(mygraph1) save_data(mydata1)
********************************************************************************

References

Piccolo, D., and Simone, R. 2019a. The class of CUB models: statistical foundations, inferential issues and empirical evidence. Statistical Methods & Applications, Vol. 28, pp. 389–435.

Piccolo, D., and Simone, R. 2019b. Rejoinder to the discussion of "The class of cub models: statistical foundations, inferential issues and empirical evidence". Statistical Methods & Applications, Vol. 28, Issue 3, pp. 477–493.

Baum, F.C., Cerulli, G., Di Iorio, F., Piccolo, D., Simone, R. 2018, The Stata module CUB for fitting mixture models for ordinal data. Presented at: "The 2018 Italian Stata Users Group meeting", Bologna, 15 November.

Piccolo, D. 2006. Observed information matrix for cub models. Quaderni di Statistica, Vol. 8.

Author

Giovanni Cerulli

IRCrES-CNR

Research Institute for Sustainable Economic Growth, National Research Council of Italy

E-mail: giovanni.cerulli@ircres.cnr.it

Acknowledgements

I wish to thank Kit Baum, Francesca Di Iorio, Domenico Piccolo, and Rosaria Simone for their help and suggestions in improving this work. I am also grateful to the organizers and participants to the 2018 Italian Stata Users Group meeting held in Bologna (Italy) on the 15th of November 2018.

Also see

Online: cub , glm