A data frame containing 1190 observations (n = 1190) and 6 variables (p = 6) measured on the binary scale.
data("women_math")
A data frame containing 1190 observations (n = 1190) and 6 variables (p = 6) measured on the binary scale (Fowlkes et al. 1988) . These data have been analyzed in Tarantola (2004) and in (Madigan and Raftery 1994) . The variable descriptions were copied from (section 5.2 ) (section 5.2, Talhouk et al. 2012)
1 Lecture attendance (attend/did not attend)
2 Gender (male/female)
3 School type (urban/suburban)
4 “I will be needing Mathematics in my future work” (agree/disagree)
5 Subject preference (math/science vs. liberal arts)
6 Future plans (college/job)
Fowlkes EB, Freeny AE, Landwehr JM (1988).
“Evaluating logistic models for large contingency tables.”
Journal of the American Statistical Association, 83(403), 611--622.
Madigan D, Raftery AE (1994).
“Model selection and accounting for model uncertainty in graphical models using Occam's window.”
Journal of the American Statistical Association, 89(428), 1535--1546.
Talhouk A, Doucet A, Murphy K (2012).
“Efficient Bayesian inference for multivariate probit models with sparse inverse correlation matrices.”
Journal of Computational and Graphical Statistics, 21(3), 739--757.
Tarantola C (2004).
“MCMC model determination for discrete graphical models.”
Statistical Modelling, 4(1), 39--61.
data("women_math")