logisticPCA
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andland
Dimensionality reduction for binary data
Hi, I was wondering if logisticPCA::logisticSVD function might incorporate a way to retrieve (or calculate) the proportion of deviance explained by each principal component, in addition to the overall proportion...
No really an "issue" but a question: does the "m" parameter calculated in the cv.lpca function correspond to anything meaningful in the data, or the output. The reason, I ask...
Hi, I'm trying to evaluate the three different methods shipped with this package on my data. The data is a 76x4623 matrix. Estimating `m` with `cv.lpca()` is extremely slow for...
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...
Tipping, M. E. (1998). Probabilistic visualisation of high-dimensional binary data. NIPS 11, pp. 592-598.
While trying to implement LPCA into an analysis, I find it hard to know how to construct the methods. I think it would be helpful to include examples of how...
Hi, I am having the following issues, first logistic SVD fails ``` > logisticPCA::logisticSVD(bdata,k=2) 45 rows and 395 columns Rank 2 solution 20.3% of deviance explained 11 iterations to converge...
[SampleData.xlsx](https://github.com/andland/logisticPCA/files/2211119/SampleData.xlsx) I have a similar question to this person: https://stats.stackexchange.com/questions/319818/how-to-analyse-the-strength-of-the-variables-in-a-logistic-pca-using-r Your code essentially ends at creating a plot, and I am unsure of how to interpret the results. Essentially, my...
I have written different method functions for lpca, lsvd, and clpca. I can probably combine many of them. The methods to combine are: - [ ] print - [ ]...
Due to its structure, the convex formulation may prefer higher values of `M` no matter `k` using the current setup of `cv.clpca`. It may be better to see how well...