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## ---- eval=FALSE--------------------------------------------------------------
# # Python - Coeff (scores)
# [[-2.809 0.097 0.244 0.050]
# [-1.834 0.286 0.010 -0.135]
# [-0.809 0.963 -0.341 0.078]
# [-0.155 -1.129 0.548 0.026]
# [0.707 -0.723 -0.736 -0.024]
# [1.830 -0.290 -0.157 0.030]
# [3.070 0.796 0.431 -0.026]]
#
# # m1e <- empca(x=B1, w=B1wt, ncomp=4)
# # Un-sweep the eigenvalues to compare to python results
# # R round( sweep( m1e$scores, 2, m1e$eig, "*"), 3)
# PC1 PC2 PC3 PC4
# G1 -2.809 0.097 -0.244 0.050
# G2 -1.834 0.286 -0.010 -0.135
# G3 -0.809 0.963 0.341 0.078
# G4 -0.155 -1.129 -0.548 0.026
# G5 0.707 -0.723 0.736 -0.024
# G6 1.830 -0.290 0.157 0.030
# G7 3.070 0.796 -0.431 -0.026
#
# # Matlab - P (scores)
# 0.5590 0.0517 0.2210 0.2910
# 0.3650 0.1520 0.0095 -0.7840
# 0.1610 0.5120 -0.3080 0.4530
# 0.0309 -0.6010 0.4950 0.1510
# -0.1410 -0.3850 -0.6640 -0.1380
# -0.3650 -0.1540 -0.1420 0.1760
# -0.6110 0.4230 0.3890 -0.1490
#
# # R round(m1e$scores, 3)
# PC1 PC2 PC3 PC4
# G1 -0.559 -0.052 0.221 -0.291
# G2 -0.365 -0.152 0.009 0.784
# G3 -0.161 -0.512 -0.308 -0.453
# G4 -0.031 0.601 0.495 -0.151
# G5 0.141 0.385 -0.664 0.138
# G6 0.365 0.154 -0.142 -0.176
# G7 0.611 -0.423 0.389 0.149
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