The aimer package implements the aimer algorithm as described in Ding and McDonald 2017. The code below generates data with 100 rows and 1000 columns, uses cross-validation to select the optimal number of columns, eigenvectors, and threshold for the coefficients, and plots a heatmap of the cross-validation error between the remaining two parameters when one of the three is fixed at the optimal level.
library(aimer) dat = factorModelSim1(100,1000,5,c(5,1),2,.1,.1) MyCV = findThresholdSelect(dat$X, dat$Y, seq(2, 50, 2)) plot(MyCV)
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