Description Usage Arguments Examples
Predict Convex Generalized PCA scores or reconstruction on new data
1 2 3 |
object |
convex generalized PCA object |
newdata |
matrix with all binary entries. If missing, will use the
data that |
type |
the type of fitting required. |
... |
Additional arguments |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | # construct a low rank matrices in the logit scale
rows = 100
cols = 10
set.seed(1)
loadings = rnorm(cols)
mat_logit = outer(rnorm(rows), loadings)
mat_logit_new = outer(rnorm(rows), loadings)
# convert to a binary matrix
mat = (matrix(runif(rows * cols), rows, cols) <= inv.logit.mat(mat_logit)) * 1.0
mat_new = (matrix(runif(rows * cols), rows, cols) <= inv.logit.mat(mat_logit_new)) * 1.0
# run generalized PCA on it
cgpca = convexGeneralizedPCA(mat, k = 1, M = 4, family = "binomial")
PCs = predict(cgpca, mat_new)
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