Predict the projection of new individuals/rows or variables/columns.
Usage
# S4 method for CA
predict(object, newdata, margin = 1)
# S4 method for MCA
predict(object, newdata, margin = 1)
# S4 method for PCA
predict(object, newdata, margin = 1)
Value
A data.frame
of coordinates.
Examples
## Create a matrix
A <- matrix(data = sample(1:10, 100, TRUE), nrow = 10, ncol = 10)
## Compute correspondence analysis
X <- ca(A, sup_row = 8:10, sup_col = 7:10)
## Predict new row coordinates
Y <- matrix(data = sample(1:10, 120, TRUE), nrow = 20, ncol = 6)
predict(X, Y, margin = 1)
#> F1 F2 F3 F4 F5
#> 1 -0.016180251 -0.077592576 -0.01343008 -0.15784502 -0.133522902
#> 2 -0.033895179 -0.328592023 -0.26341671 0.30270493 0.355614089
#> 3 -0.127934128 0.061615988 -0.16253292 -0.38665397 0.124998588
#> 4 -0.157773852 0.102959785 -0.24594788 -0.13332535 0.160451942
#> 5 -0.068729501 -0.191322198 0.02053709 -0.11189040 -0.004501452
#> 6 0.252222115 -0.091312728 -0.21183868 -0.15475244 0.230578553
#> 7 0.081323499 -0.277625596 0.23904583 0.06461852 -0.276636651
#> 8 -0.148091661 0.093316811 -0.18692812 -0.12552963 0.249424847
#> 9 0.557943849 0.438932297 0.23151019 -0.03084062 0.117453222
#> 10 -0.060637558 0.039644931 -0.03032355 -0.15349881 -0.342285107
#> 11 0.010347067 -0.303781759 -0.10551184 0.42208790 0.372575047
#> 12 0.249809992 0.110677609 0.13968170 0.46825774 0.359126007
#> 13 0.140237787 -0.061402810 0.33366129 0.12741400 0.177470768
#> 14 0.065019780 0.326064354 0.30494219 0.29323520 0.105539059
#> 15 -0.158497969 0.002617092 0.15073247 0.22936973 0.415578958
#> 16 0.303747857 0.341633353 -0.37073250 0.08106986 -0.090304840
#> 17 0.003772628 -0.018710373 -0.09780423 -0.35332587 0.159165332
#> 18 0.248147149 -0.254611742 -0.28227942 -0.17260588 -0.111091594
#> 19 0.168304594 0.195696384 -0.44343241 -0.04313237 -0.198473100
#> 20 0.231248932 0.165774410 -0.31739560 0.32210890 0.084939007
## Predict new column coordinates
Z <- matrix(data = sample(1:10, 140, TRUE), nrow = 7, ncol = 20)
predict(X, Z, margin = 2)
#> F1 F2 F3 F4 F5
#> 1 -0.30729049 0.236384813 0.43793640 4.065473e-01 -0.327051045
#> 2 0.35087757 -0.352722273 0.56114690 1.517428e-01 -0.311094940
#> 3 0.13352074 0.148547902 -0.17343781 1.866746e-01 -0.032048361
#> 4 0.07420491 0.155005848 0.12610837 3.785932e-01 -0.094358693
#> 5 0.13457211 0.019071126 -0.42420280 -6.396752e-02 0.105549048
#> 6 0.08384225 0.191327883 -0.15530005 -1.369250e-01 -0.136965827
#> 7 0.46736580 -0.108452624 0.36197475 4.598092e-02 0.131312472
#> 8 0.23410593 -0.243493221 -0.51012482 1.512683e-01 0.212685556
#> 9 0.23850634 -0.198287596 -0.39778721 1.348564e-02 -0.001077467
#> 10 -0.15584395 -0.282040633 -0.28189939 1.980180e-01 -0.141969085
#> 11 0.01737386 0.116412826 -0.03194465 2.488541e-01 0.231644346
#> 12 -0.02816312 0.001211305 0.10368149 -2.305333e-01 -0.026121750
#> 13 -0.01316391 -0.007522721 -0.32932409 -2.790850e-02 0.492044025
#> 14 -0.18403429 0.184775332 0.09736779 -1.437911e-01 0.126826911
#> 15 0.11695152 -0.258524254 0.50187404 -6.142728e-02 0.197945452
#> 16 -0.15528839 -0.155831888 0.17285225 -2.276840e-01 0.037895840
#> 17 0.27159721 0.239326287 0.09883240 3.225330e-01 0.207134115
#> 18 0.23106420 0.118433799 -0.22163972 -1.379934e-04 -0.336719378
#> 19 -0.27507329 -0.088306233 0.08904732 2.989908e-05 -0.007155850
#> 20 0.14064699 -0.189041894 0.06936555 -6.800809e-02 -0.242509678