Predict method for experimental plsRmulti models

Prediction method for "plsRmultiModel" objects.

Usage

# S3 method for class 'plsRmultiModel'
predict(
  object,
  newdata,
  comps = object$computed_nt,
  type = c("response", "scores"),
  verbose = TRUE,
  ...
)

Arguments

object: An object of class "plsRmultiModel".
newdata: Optional predictor matrix or data frame. For formula-fitted models, a data frame with the predictor variables used at fit time.
comps: Number of extracted components to use.
type: Either "response" or "scores".
verbose: Should informational messages be displayed?
...: Not used.

Value

If type = "response", a matrix of predicted responses. If type = "scores", a matrix of latent score coordinates.

Examples

set.seed(123)
X <- matrix(rnorm(40 * 3), ncol = 3)
Y <- cbind(
  y1 = X[, 1] + rnorm(40, sd = 0.1),
  y2 = X[, 2] - X[, 3] + rnorm(40, sd = 0.1)
)
fit <- plsRmulti(Y, X, nt = 2, verbose = FALSE)

predict(fit, type = "response")
#>            y1          y2
#> 1  -0.5995617 -0.71354327
#> 2  -0.2467747 -0.59806489
#> 3   1.5072780 -0.92110100
#> 4   0.1446257  1.57138687
#> 5   0.1619663  1.45885644
#> 6   1.6749922 -1.48261272
#> 7   0.4462620 -1.51431413
#> 8  -1.2963435 -0.91151638
#> 9  -0.6750579  1.12835257
#> 10 -0.4530946 -1.23870005
#> 11  1.2368173 -0.74030324
#> 12  0.3542017 -0.57953909
#> 13  0.3925190 -0.28264847
#> 14  0.1460771  2.03310150
#> 15 -0.5674901 -1.59733716
#> 16  1.8369683  2.15431874
#> 17  0.4496116 -3.78338153
#> 18 -1.9548598 -0.93985156
#> 19  0.6972566  0.36483413
#> 20 -0.4858052  1.25615994
#> 21 -1.0756581  1.10634607
#> 22 -0.2462684 -0.76994014
#> 23 -1.0562498 -0.08925102
#> 24 -0.7834233 -0.68900337
#> 25 -0.6855195 -0.13572313
#> 26 -1.6956333  0.35980188
#> 27  0.8421715  1.24895692
#> 28  0.1329203  1.73424207
#> 29 -1.1239659  1.32908574
#> 30  1.3320211  1.17197022
#> 31  0.3957792  0.07817801
#> 32 -0.3873740 -2.97075232
#> 33  0.9139757  2.65715976
#> 34  0.8458043 -0.66852086
#> 35  0.7940612 -1.22532145
#> 36  0.7215805  0.74673853
#> 37  0.5366181 -0.39632924
#> 38 -0.1224726 -0.60158833
#> 39 -0.3180145  1.04270803
#> 40 -0.4059788  0.89088806
predict(fit, type = "scores")
#>         Comp_1       Comp_2
#> 1  -0.80979817 -0.356072659
#> 2  -0.54057735 -0.054431798
#> 3   0.20504266  1.784050716
#> 4   1.10719975 -0.490464220
#> 5   1.04231170 -0.431009756
#> 6  -0.07434708  2.160170621
#> 7  -0.76716365  0.969698956
#> 8  -1.32163379 -0.963202968
#> 9   0.36620424 -1.125345984
#> 10 -1.07677928 -0.014470769
#> 11  0.17664245  1.451118197
#> 12 -0.19972200  0.526691907
#> 13  0.01744068  0.452100778
#> 14  1.41313417 -0.663360398
#> 15 -1.37634865  0.008981811
#> 16  2.41781857  0.945587827
#> 17 -2.26492733  1.829644444
#> 18 -1.70043463 -1.596931932
#> 19  0.61198246  0.505866527
#> 20  0.55415315 -0.988395076
#> 21  0.13261361 -1.509066559
#> 22 -0.65389045  0.010953815
#> 23 -0.64692758 -1.038685783
#> 24 -0.89411498 -0.545265166
#> 25 -0.47492624 -0.658342427
#> 26 -0.69976747 -1.833925177
#> 27  1.27552594  0.313887582
#> 28  1.20842801 -0.563403816
#> 29  0.25340463 -1.640434270
#> 30  1.49249460  0.822322029
#> 31  0.25768853  0.319064113
#> 32 -2.18553286  0.703765593
#> 33  2.24544998 -0.147499849
#> 34  0.01027780  1.041370422
#> 35 -0.38599693  1.200949900
#> 36  0.87767802  0.385485160
#> 37  0.02110353  0.636035958
#> 38 -0.47493794  0.068541051
#> 39  0.50483312 -0.743607446
#> 40  0.35639878 -0.772371357

Usage

Arguments

Value

See also

Examples