Predict the projection of new individuals/rows or variables/columns.
Usage
# S4 method for CA
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 0.26537515 0.38952779 2.751045e-01 0.167021712 0.049673965
#> 2 -0.38291567 0.33192718 -1.039264e-01 0.065142897 0.471848665
#> 3 0.15909332 -0.04878874 -1.269940e-01 -0.108145112 -0.438485457
#> 4 0.05056052 -0.34561213 3.684817e-01 -0.078266078 0.160237440
#> 5 0.16147773 0.17479993 -2.240285e-01 0.187752867 0.272329800
#> 6 0.24271123 -0.01922505 6.457606e-02 -0.031903837 0.243817930
#> 7 0.11272592 -0.14108534 -6.843560e-02 0.101192259 0.019921941
#> 8 0.11223811 -0.17476258 3.999361e-01 0.146547913 0.214472276
#> 9 0.23501076 -0.37402172 -1.954058e-02 -0.144174562 0.105560307
#> 10 0.09097496 -0.01123157 -1.349008e-01 0.170939040 0.304515395
#> 11 -0.25491128 -0.21491700 4.821574e-02 -0.078303279 0.060933216
#> 12 -0.29091205 0.34312319 -7.651157e-02 0.103141965 0.064829965
#> 13 0.18337746 0.19106609 -3.035282e-01 0.215603429 -0.016524652
#> 14 0.13891584 -0.15740085 1.671563e-01 0.137558763 -0.094471189
#> 15 0.13387375 -0.04014952 -2.802286e-01 0.058294624 0.151286392
#> 16 0.07708776 0.19054102 -4.544388e-03 -0.129302375 0.004684898
#> 17 0.04673067 -0.84971022 4.697247e-07 0.093779535 -0.055205889
#> 18 0.29831486 -0.01697459 -1.296309e-01 0.003600986 -0.257760536
#> 19 0.05958225 0.07827655 7.493912e-02 0.043425215 0.141321539
#> 20 0.08995072 0.04284973 7.633691e-02 -0.138832146 0.339980548
## 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 -0.165843046 0.321194530 -2.367059e-01 -0.302586631 -0.01050897
#> 2 -0.085467558 0.209230395 3.678932e-01 -0.028892103 -0.11460931
#> 3 0.657830755 0.169253264 -1.765485e-01 0.007804920 0.24959723
#> 4 -0.162325449 -0.151375438 7.566869e-02 0.030984111 -0.25788431
#> 5 0.294629965 0.326479300 2.540340e-01 0.076753113 -0.34572497
#> 6 -0.155923698 0.140083433 -7.114223e-02 -0.116372335 -0.23237200
#> 7 -0.492429945 0.237445670 -3.526466e-01 -0.123618240 0.24999753
#> 8 0.009631388 -0.209424920 2.392178e-01 0.037897695 0.00493662
#> 9 0.268489565 0.171238699 1.754170e-01 -0.071244813 0.45842796
#> 10 -0.087724092 -0.134932270 -3.373045e-02 -0.121169930 -0.02807452
#> 11 0.141758640 0.116210684 2.228880e-01 -0.192868519 0.18100529
#> 12 0.209469356 0.105863036 4.960747e-01 0.327834512 -0.39497794
#> 13 0.180260372 0.356719587 2.660949e-01 -0.003751829 -0.43912968
#> 14 0.369791034 0.006009517 6.585158e-02 -0.164572233 -0.31487156
#> 15 -0.207165492 -0.102954464 2.907125e-01 -0.160161163 -0.28668019
#> 16 -0.063899351 0.083442616 6.460950e-02 0.105433554 -0.59042893
#> 17 -0.208543345 -0.063038148 2.399970e-01 0.201234201 -0.57971339
#> 18 0.315334342 -0.081623585 4.800067e-01 -0.023590763 -0.15593569
#> 19 -0.032083868 -0.094410193 8.384391e-05 0.039105374 0.30792365
#> 20 -0.345183752 0.125995270 -1.883245e-01 -0.342679782 -0.03609059