Comments (6)
Actually, abess checks whether max(support.size) < nobs, but not rank(X). The following code will produce an error. However, when rank(X) < smax < nrow(X), there is no warning reported.
library(abess)
data <- generate.data(n = 10, p = 100, support.size = 10)
abess(data$x, data$y, support.size = 0:15)
#> Error in abess.default(data$x, data$y, support.size = 0:15) : max(support.size) < nobs is not TRUE
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@brtang63 , I am confused about this question. In this example, n is 30, i.e., rank(X) = 30; on the contrary, the maximum support size is 15 such that rank(X) >= support size. So, what warning shall be thrown?
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@brtang63 , I am confused about this question. In this example, n is 30, i.e., rank(X) = 30; on the contrary, the maximum support size is 15 such that rank(X) >= support size. So, what warning shall be thrown?
Oh... I see... Let me simplify this code.
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@brtang63 , I am confused about this question. In this example, n is 30, i.e., rank(X) = 30; on the contrary, the maximum support size is 15 such that rank(X) >= support size. So, what warning shall be thrown?
Oh... I see... Let me simplify this code.
Shall you do this since I cannot obtain the newest result shown in the screenshot. Also, simplify this code to a minimal one. And paste the results in code chunck... Many thanks!
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Thanks for this comment and your clarification!
It is an excellent comment in view of numerical analysis, but we will not address this issue temporarily. This is because computing/estimating the rank of a matrix, actually, is very time-consuming. To my knowledge, computing rank has to obtain all of the eigenvalues of this matrix which has a time complexity of N^3 where N is the matrix size.
So, until a fast rank determination algorithm is well developed, we will incorporate this fast algorithm into my implementation. Or if you know about such a fast algorithm for computing rank, we are glad to implement it into our project.
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I get it. Thanks for your clear and helpful explanation!
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