Describe the bug
I have an ultra-high-dimensional sparse dataset (hundreds of thousands of features, hundreds of samples). It is a genetic dataset for copy number variation. Each column represents a genomic repeat, and each row is one subject. The outcome is a binary phenotype (0: absent, 1: present).
Hence, it is as a simple numeric matrix with features as columns and rows as samples. There are no missing values or negative values. Most of the values are 0, many are 1, and rarely some are higher (> 1).
I receive an error about the first tangent point having non-positive probability when prior="ghs" or prior="neg" (but not the default prior="t").
To Reproduce
X = data.matrix(read.table("tiny.txt",sep = '\t'))
y = read.table("tinyout.txt")[,1]
model = htlr(X,y,prior = 'ghs')
Note that the bug occurs (like in this toy example) even when N > p, so it is not a dimensionality problem.
Is there anything particular about the data distribution requirements?
Expected behavior
I was hoping for a more descriptive error message if the problem is my data, or for the model to finish without error otherwise.
R session info
R 3.5.3, latest HTLR installed with devtools.
Screenshots
The best lambda chosen by CV: 0.05
Error in htlr_fit_helper(p = p, K = K, n = n, X = as.matrix(X_addint), :
Error in adaptive rejection sampling:
the first tangent point doesn't have positive probability.
Describe the bug I have an ultra-high-dimensional sparse dataset (hundreds of thousands of features, hundreds of samples). It is a genetic dataset for copy number variation. Each column represents a genomic repeat, and each row is one subject. The outcome is a binary phenotype (0: absent, 1: present).
Hence, it is as a simple numeric matrix with features as columns and rows as samples. There are no missing values or negative values. Most of the values are 0, many are 1, and rarely some are higher (> 1).
I receive an error about the first tangent point having non-positive probability when prior="ghs" or prior="neg" (but not the default prior="t").
To Reproduce
tiny.txt tinyout.txt
Note that the bug occurs (like in this toy example) even when N > p, so it is not a dimensionality problem. Is there anything particular about the data distribution requirements?
Expected behavior I was hoping for a more descriptive error message if the problem is my data, or for the model to finish without error otherwise.
R session info R 3.5.3, latest HTLR installed with devtools.
Screenshots