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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  -0.02109235 -0.140739408  0.010823556  0.01806206 -0.05110977
#> 2   0.14588185  0.091759165  0.068309989  0.05755257  0.18283599
#> 3  -0.05487255  0.077422004 -0.012675127  0.02696307 -0.13568006
#> 4   0.10853875 -0.001283618 -0.072722095  0.05298505  0.11295735
#> 5  -0.06478269  0.138391833 -0.362364089  0.40363357  0.10911919
#> 6   0.09019382  0.194883374 -0.001348993  0.05592522  0.16678436
#> 7   0.21724053 -0.266879229 -0.144403097 -0.25589451 -0.14627001
#> 8   0.22510286  0.008260928 -0.019835272 -0.36236677 -0.07183367
#> 9  -0.37584676 -0.454570789  0.147485635 -0.19841825  0.05077730
#> 10  0.04163826 -0.247817825  0.013331446 -0.25535965  0.38747633
#> 11 -0.14788543  0.091346852 -0.109785506  0.34868582  0.19650240
#> 12  0.09067731 -0.327098774 -0.259974109 -0.10841294 -0.01491237
#> 13  0.60450542  0.008600252  0.107774317  0.33476565 -0.21483769
#> 14  0.06390106  0.259902512  0.074019023  0.29485704  0.04191463
#> 15  0.46498191  0.084644064 -0.053223378 -0.06935557  0.06030606
#> 16  0.13544878 -0.274485054 -0.014056309  0.14610438  0.55437835
#> 17 -0.11075052  0.394074119  0.022836355 -0.01314795 -0.43411612
#> 18 -0.45074566 -0.149666190 -0.058476962  0.02640080  0.40533566
#> 19  0.30505878 -0.294654861 -0.407430825 -0.35976141  0.08148388
#> 20  0.40794095  0.160414120 -0.005786607  0.18361045  0.08666650

## 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  -0.08599746 -0.18037270  0.16219734 -0.248126837 -0.056951948
#> 2  -0.37603754 -0.17943883 -0.16181820  0.033185691  0.106276480
#> 3  -0.02694760  0.09865591  0.07134926  0.102603574 -0.094826440
#> 4  -0.43174872 -0.12827359  0.10996979 -0.134688730 -0.055209858
#> 5  -0.03489172 -0.44147218 -0.43219126 -0.136251934 -0.336296462
#> 6  -0.02246601 -0.16839153 -0.46226065  0.003729659 -0.060401416
#> 7   0.22822012  0.37131764  0.28451952 -0.100882346 -0.244113998
#> 8  -0.43999200 -0.22107390  0.20903380 -0.157304491 -0.306004156
#> 9  -0.50992219 -0.21089404 -0.02392420 -0.059752601  0.293844903
#> 10 -0.23787372 -0.49026660 -0.19144661  0.104980497  0.205309407
#> 11  0.07616158  0.45799089 -0.16879876  0.133274301 -0.044222735
#> 12 -0.40031974 -0.30721225 -0.03542759 -0.539513751 -0.348314592
#> 13 -0.37345206 -0.10298770  0.11250036  0.082216497  0.119373208
#> 14 -0.18026230 -0.21698006  0.04097709  0.031122360 -0.001512048
#> 15 -0.22143108  0.12768699 -0.07089876 -0.532103907  0.051120890
#> 16 -0.04448354  0.10322996 -0.25937396 -0.427309465 -0.170463811
#> 17 -0.19323160  0.07433083  0.20903571  0.254430455  0.130681696
#> 18  0.12606937 -0.11701547 -0.14932989 -0.057484554 -0.156470202
#> 19 -0.55855625 -0.20264704 -0.29904960  0.158152600 -0.228738473
#> 20 -0.21960653 -0.31580880  0.09637874 -0.194373025 -0.291984132