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 -0.22984790 0.22339744 -0.22446601 0.14573354 0.181147656
#> 2 0.12109012 0.11785375 -0.15816258 -0.01213206 -0.216803763
#> 3 -0.23649478 0.02010735 -0.27194406 -0.17464071 0.004694312
#> 4 -0.22466803 0.34123011 -0.16201640 -0.21901299 0.099064599
#> 5 0.06850205 -0.48819388 -0.24625716 -0.09764892 0.006713169
#> 6 0.16724263 0.14756745 0.33433338 -0.29859197 0.024620967
#> 7 0.53995548 -0.07678737 0.15537943 -0.04114299 0.364877048
#> 8 0.30194199 -0.38036616 -0.09664650 -0.28370508 -0.011526677
#> 9 0.25024205 -0.44356792 0.10951934 -0.45234477 0.172925433
#> 10 -0.10262459 0.27978328 0.02389598 -0.05348861 0.281518413
#> 11 0.28899651 0.26090596 -0.12469636 0.03166779 0.078563954
#> 12 -0.35548325 0.60557976 -0.23333581 0.19834291 -0.076035094
#> 13 -0.07108079 0.23617505 0.32379701 0.09093099 0.136181209
#> 14 0.20498573 -0.25716338 0.05508702 0.12947667 0.152732854
#> 15 0.24799006 0.03677552 -0.11389546 -0.11416812 0.165384096
#> 16 -0.33523511 0.25086232 0.18246551 -0.19273040 -0.165932779
#> 17 -0.04457814 0.26803288 -0.05718665 -0.19945476 0.295026362
#> 18 0.28930946 0.37755828 -0.33240593 -0.03828841 0.091695220
#> 19 0.51083443 0.04041245 -0.34600420 -0.07566008 -0.232495800
#> 20 0.14725862 0.05618548 -0.02167869 0.13608174 0.094928766
## 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 -0.4466090045 0.406295781 0.10727807 -0.15893196 -0.1788408377
#> 2 0.1315399156 0.224004570 0.37424905 -0.04390725 0.3516888817
#> 3 0.4513717908 -0.004536489 0.13294947 0.11110379 0.1754847881
#> 4 -0.2550115654 0.226816494 -0.20271663 0.34410746 0.1268956885
#> 5 -0.2018713199 -0.121812569 0.10960253 -0.07457661 -0.4596579759
#> 6 -0.0008006736 0.304071510 -0.46883342 0.22211348 -0.0535238926
#> 7 0.4859435763 -0.005787092 -0.16828999 -0.19587074 0.0201276347
#> 8 0.0917721712 0.124022469 -0.15405133 -0.04184395 -0.1394249752
#> 9 0.0933808114 -0.237898604 0.10994040 -0.36463834 0.2490203667
#> 10 0.1937853025 0.027502108 -0.23522528 -0.18982868 -0.2209218609
#> 11 0.4942137076 0.004918101 -0.13860808 0.27168835 -0.0024116618
#> 12 0.3332635148 0.048078596 -0.15793977 -0.12297949 0.0945725579
#> 13 0.2097816216 -0.134223192 0.03936531 -0.13342062 -0.2482250891
#> 14 0.0274122032 0.038212113 -0.15541007 0.12693730 0.3245487410
#> 15 -0.0578160816 0.255787678 -0.20051450 0.05951471 0.2092218993
#> 16 0.0735984987 0.079321248 0.08777002 -0.23385498 0.3265644250
#> 17 0.2160610636 0.230196162 0.25328087 -0.17201132 0.1133773325
#> 18 -0.4306321919 -0.066298983 0.14231808 -0.23844740 0.0003338666
#> 19 0.4041131592 0.087214730 0.13997104 -0.49152023 0.1957259499
#> 20 0.2513168088 0.563541947 -0.47070575 0.01686564 -0.2411289177