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Predict New Coordinates

Source: R/AllGenerics.R, R/predict.R
predict.Rd

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)

Arguments

object

A CA or PCA object.

newdata

An object of supplementary points coercible to a matrix for which to compute principal coordinates.

margin

A length-one numeric vector giving the subscript which the data will be predicted: 1 indicates individuals/rows (the default), 2 indicates variables/columns.

Value

A data.frame of coordinates.

See also

Other multivariate analysis: ca(), mca(), pca(), pcoa()

Author

N. Frerebeau

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  -0.00478730 -0.041188329  0.61467580 -0.07379945 -0.117412555
#> 2  -0.09218019  0.190863599  0.07038407  0.08459084  0.001893532
#> 3   0.14780605  0.038614444 -0.03673148 -0.35308760 -0.245743210
#> 4  -0.09442708  0.055058731  0.10807567  0.30859411 -0.156125856
#> 5  -0.37177664  0.120862440  0.65933867  0.32434798 -0.366779279
#> 6   0.07790236  0.116582600  0.21266030  0.19724028  0.199336336
#> 7   0.30201666 -0.163002345 -0.09564004 -0.10547591  0.085394493
#> 8   0.62379579 -0.052425648  0.18362867 -0.04215614  0.002932787
#> 9   0.09556146 -0.065833223  0.21843956  0.13073446 -0.145574770
#> 10 -0.37202424  0.680543338  0.11860880 -0.27519813 -0.133280379
#> 11 -0.38478986 -0.003996842  0.39572237  0.23395841 -0.254636410
#> 12 -0.39041881  0.320438828 -0.05203969 -0.12961609  0.229341814
#> 13  0.20645307  0.374752264  0.15301794 -0.03845264  0.244953749
#> 14 -0.20106947  0.047879025 -0.14036712 -0.25969333  0.250815085
#> 15  0.30806732  0.021792602  0.41081308 -0.34232985  0.399063893
#> 16  0.19483911  0.236539792 -0.16007619 -0.05097245 -0.498739072
#> 17  0.12449068  0.157809188  0.29570350  0.21176447 -0.201378230
#> 18  0.16107766 -0.188957392  0.54296593 -0.13938986  0.268968383
#> 19  0.25760924  0.047988663 -0.07688950  0.60108850 -0.401404605
#> 20  0.03601531  0.066577916  0.29265869  0.15089625 -0.222565212

## 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   0.41770310 -0.18340696 -0.05638240 -0.10048350  0.24265252
#> 2   0.45507616  0.13057599  0.06408124 -0.15885416 -0.03986550
#> 3  -0.11646227  0.18590042 -0.48994388 -0.03848177 -0.15153501
#> 4   0.03234031 -0.02243677  0.17656717 -0.28390798  0.23162739
#> 5   0.15458941 -0.25633914 -0.34059703 -0.12529427 -0.06487952
#> 6  -0.03160722  0.08887754  0.06629717  0.10465828 -0.17560651
#> 7  -0.43761518 -0.10006713 -0.56227867  0.02311103  0.35316676
#> 8   0.03184870 -0.39868913 -0.22787271  0.18456120  0.02609875
#> 9  -0.10226096  0.16407770 -0.35218280  0.14606842 -0.11547918
#> 10 -0.12749181  0.09909558 -0.23773365 -0.14033899  0.01829664
#> 11 -0.02152775  0.11270289 -0.01930091 -0.27350705  0.09872964
#> 12  0.24134056  0.12335194 -0.23448812 -0.23781535  0.34686896
#> 13  0.07126504 -0.15035976 -0.50910270  0.06025939 -0.36938414
#> 14  0.26619017  0.11013720 -0.26520409  0.17737012 -0.02487129
#> 15 -0.40669069  0.02291560 -0.24249026  0.07793922 -0.11725374
#> 16 -0.47093698  0.05942306 -0.21975135 -0.04242656  0.05768544
#> 17 -0.17818705 -0.04661435 -0.21192824 -0.01846491 -0.10829349
#> 18  0.02339854 -0.39677720 -0.09889424 -0.30823261 -0.21222769
#> 19 -0.27805997 -0.17448804  0.13839135  0.07075069 -0.44703061
#> 20 -0.17656361 -0.17757239 -0.28465918 -0.24756393 -0.19459274