Predicts the response for new data using a fitted SDForest.
# S3 method for class 'SDForest'
predict(object, newdata, ...)
A vector of predictions for the new data.
set.seed(1)
n <- 50
X <- matrix(rnorm(n * 5), nrow = n)
y <- sign(X[, 1]) * 3 + rnorm(n)
model <- SDForest(x = X, y = y, Q_type = 'no_deconfounding', nTree = 5, cp = 0.5)
predict(model, newdata = data.frame(X))
#> 1 2 3 4 5 6 7 8
#> -1.680850 2.056027 -1.680850 2.056027 2.056027 -1.680850 2.056027 2.056027
#> 9 10 11 12 13 14 15 16
#> 2.056027 -1.680850 2.056027 2.056027 -1.680850 -1.680850 2.056027 -1.680850
#> 17 18 19 20 21 22 23 24
#> -1.680850 2.056027 2.056027 2.056027 2.056027 2.056027 2.056027 -1.680850
#> 25 26 27 28 29 30 31 32
#> 2.056027 -1.680850 -1.680850 -1.680850 -1.680850 2.056027 2.056027 -1.680850
#> 33 34 35 36 37 38 39 40
#> 2.056027 -1.680850 -1.680850 -1.680850 -1.680850 -1.680850 2.056027 2.056027
#> 41 42 43 44 45 46 47 48
#> -1.680850 -1.680850 2.056027 2.056027 -1.680850 -1.680850 2.056027 2.056027
#> 49 50
#> -1.680850 2.056027