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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  -0.0956879341  0.0279979689  0.254553127 -0.20193311  0.372619988
#> 2   0.0833929632 -0.4962188602  0.232072255 -0.40007762 -0.048277292
#> 3  -0.2209111593 -0.3789578210 -0.265130232  0.41749397  0.090635860
#> 4   0.2056488661  0.1183189413  0.125561003  0.28356081  0.283164381
#> 5   0.1055928740  0.2860169593 -0.118665530  0.06447895  0.086311413
#> 6  -0.2800987448 -0.1953040474  0.117424803  0.10605155  0.060383866
#> 7   0.3258235954 -0.0008825829  0.106083368 -0.04049953  0.042144895
#> 8  -0.2672798067 -0.0456541609  0.004593870 -0.02989406  0.098173656
#> 9   0.3402559564  0.2650799157  0.242686546 -0.26431395 -0.037504956
#> 10 -0.0732469533 -0.0225050075  0.009060602  0.08987267 -0.129424799
#> 11  0.1757378735  0.0446432909 -0.140156236 -0.12059411  0.193073919
#> 12 -0.0141047669 -0.0965732821 -0.250113519 -0.02371874  0.002093166
#> 13  0.5361230139  0.5099582182 -0.430786720  0.32686317  0.316156692
#> 14 -0.1942302739 -0.2284916675 -0.274899267  0.18733778  0.452279998
#> 15  0.1166171266  0.4224989673  0.115167960  0.07222169 -0.068633781
#> 16  0.0001191487  0.0622526785 -0.155207884 -0.19403751  0.338600563
#> 17  0.1940186001  0.1460002237  0.346695430 -0.04615080 -0.055123668
#> 18 -0.0572369158  0.1526957226  0.265280816  0.10603388 -0.266327155
#> 19  0.1058543734  0.0063985693  0.014993709 -0.03616636 -0.072305034
#> 20 -0.3283488641  0.3544576498 -0.206644429  0.35365572  0.355073545

## 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   0.29654499 -0.2289097501 -0.24870323  0.25668396 -0.03602424
#> 2  -0.25291961 -0.1054244998  0.43237819  0.38718899 -0.19563702
#> 3  -0.12574015  0.1662635067  0.09964988 -0.10556479 -0.26069253
#> 4   0.17395135 -0.3613262412  0.01810161 -0.18113925 -0.10867544
#> 5   0.61980868 -0.0764134636 -0.17550525  0.34994980  0.17345943
#> 6  -0.25721389 -0.3803768988  0.11581624  0.48689718  0.04374536
#> 7   0.05827802  0.3848274396  0.20863267 -0.16814351 -0.32815504
#> 8   0.05655776 -0.3991275826 -0.14189122  0.01944858 -0.27069860
#> 9   0.27318270 -0.3249309127 -0.29138191  0.09613743 -0.43745504
#> 10  0.09479748 -0.0760288619  0.11154036  0.15948610 -0.64532101
#> 11 -0.01357717  0.0904929742  0.31194586  0.20623745 -0.10969948
#> 12 -0.09470302 -0.4647730897  0.01713416  0.29708088 -0.08656292
#> 13  0.17628526 -0.0006131745  0.13122977  0.38354656 -0.01406942
#> 14 -0.13269737 -0.1231373438 -0.30069322 -0.06986251  0.14452971
#> 15  0.08444287  0.1869853387  0.15658105 -0.02870837 -0.18504246
#> 16 -0.05848652  0.1871203992 -0.31101087 -0.09131797 -0.26675384
#> 17 -0.03561985 -0.1259309775 -0.03464557 -0.54798701 -0.06772222
#> 18  0.42250528 -0.1923639005  0.07124868  0.50485144 -0.15047237
#> 19  0.44168501 -0.2089583229  0.18275110  0.12302977 -0.44598080
#> 20  0.41244359  0.0658082667  0.05747569 -0.01678837 -0.40353352