Dear prof Andrew J,
I've been reading your article in the Journal of Multivariate Analysis called Dimensionality reduction for binary data through the projection of natural parameters. As far as I know, you obtained the principal component score by establishing the relationship between the natural parameters of the saturation model and the natural parameters of the Bernoulli distribution. I try to generate high-dimensional related binary data by cutting quantiles through mixed multivariate normal distribution, and get the principal component of binary data from the R package you provided, and predict the principal component score of new data. Surprisingly, the generation method of new data is the same, only the random-seed is different. But the final predicted principal component scores varied widely. So I would like to ask you how to understand this phenomenon?
I am looking forward to your early reply.
Yours sincerely
Dear prof Andrew J, I've been reading your article in the Journal of Multivariate Analysis called Dimensionality reduction for binary data through the projection of natural parameters. As far as I know, you obtained the principal component score by establishing the relationship between the natural parameters of the saturation model and the natural parameters of the Bernoulli distribution. I try to generate high-dimensional related binary data by cutting quantiles through mixed multivariate normal distribution, and get the principal component of binary data from the R package you provided, and predict the principal component score of new data. Surprisingly, the generation method of new data is the same, only the random-seed is different. But the final predicted principal component scores varied widely. So I would like to ask you how to understand this phenomenon? I am looking forward to your early reply. Yours sincerely