Predict the projection of new individuals/rows or variables/columns.
Usage
# S4 method for class 'CA'
predict(object, newdata, margin = 1)
# S4 method for class 'MCA'
predict(object, newdata, margin = 1)
# S4 method for class '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 0.297490379 0.097878222 0.32730750 -0.08670700 -0.18573017
#> 2 -0.313426609 0.594843892 0.03655129 -0.37486425 -0.35490186
#> 3 0.356802550 -0.200141363 -0.12682117 -0.07312492 0.34820040
#> 4 -0.263612660 0.394598197 0.02279673 -0.22883478 -0.09532713
#> 5 -0.190487429 -0.401114923 0.12506244 0.17177566 -0.04565600
#> 6 0.007030088 -0.008806933 0.19267682 0.09037668 -0.08126021
#> 7 -0.062670459 -0.266202620 0.13826557 -0.42502942 -0.14673778
#> 8 0.396044221 0.011033386 -0.07031766 0.09314319 -0.26351638
#> 9 -0.029569729 0.173970880 -0.26718643 -0.16111445 -0.16437769
#> 10 -0.028575352 0.022847301 -0.15775198 -0.08543860 -0.19825379
#> 11 0.235916004 -0.137323754 0.45025971 0.18507469 0.09373412
#> 12 0.170901172 -0.157114306 -0.04326690 0.30528826 0.13914006
#> 13 -0.116398356 -0.139808207 -0.45686923 0.18135085 0.06789483
#> 14 0.205702088 -0.073663416 0.13073005 0.21448477 0.11844901
#> 15 -0.140222473 0.286408113 -0.01715162 0.04206743 -0.33052551
#> 16 0.030874105 -0.155941520 0.22228018 -0.59689455 -0.19982782
#> 17 0.187422940 0.172658799 0.04401447 0.12705101 0.06469886
#> 18 -0.082061050 0.147745332 0.17861350 -0.01215004 0.08247793
#> 19 0.273069567 0.254930838 0.36661749 -0.14470018 0.37483272
#> 20 0.166112974 0.271460683 0.42447423 0.01717218 -0.17346440
## 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 0.056456533 0.001354843 -0.23580956 -0.02665807 0.23840100
#> 2 0.122940987 0.263453863 -0.12795097 -0.03290069 0.20974080
#> 3 -0.152415818 -0.247413681 0.27650858 -0.12217455 -0.11191471
#> 4 0.129625759 -0.053124768 0.61392007 -0.30107491 0.14630630
#> 5 0.097918984 0.027188030 -0.20611334 0.08391671 0.21040869
#> 6 -0.110492949 0.074663277 -0.20000345 -0.08819936 0.07669736
#> 7 0.069889055 -0.085059653 0.10212533 0.17372487 0.35938648
#> 8 0.041468064 -0.034038683 -0.21984887 0.21876694 -0.19074327
#> 9 0.077338319 -0.062602866 -0.27556496 -0.37459904 -0.03840334
#> 10 0.285367586 -0.279576690 -0.39686158 -0.17733878 0.10816741
#> 11 0.134051707 -0.265811227 0.08259165 -0.02971663 0.18040186
#> 12 -0.014583237 0.078895904 0.01612977 -0.34621468 0.23353582
#> 13 -0.062043182 0.235230181 -0.10833539 -0.03839515 -0.10424868
#> 14 0.015404913 -0.098228511 0.20737676 0.17235483 0.36357546
#> 15 0.215377009 -0.129303529 0.24561175 0.14620860 0.14340878
#> 16 0.138049350 0.089071993 -0.15710159 -0.24473722 0.30093378
#> 17 0.204501996 -0.210671614 -0.27763833 -0.36507570 -0.23006282
#> 18 0.043810295 -0.152438046 0.10576771 0.15825697 0.17479236
#> 19 -0.005509191 -0.174711771 -0.10198307 -0.27174611 -0.16527083
#> 20 -0.399109908 0.418862832 0.01117604 -0.09711557 0.24085461