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to predict outcome values at testing points by feeding the results obtained from gp_train()

Usage

gp_predict(gp, Xtest)

Arguments

gp

a list-form object obtained from gp_train()

Xtest

a data frame or a matrix of testing data set

Value

Xtest_scaled

testing data in a scaled form

Xtest

the original testing data set

Ys_mean_scaled

the predicted values of Y in a scaled form

Ys_mean_orig

the predicted values of Y in the original scale

Ys_cov_scaled

covariance of predicted Y in a scaled form

Ys_cov_orig

covariance of predicted Y in the original scale

f_cov_orig

covariance of target function in the original scale

b

the bandwidth value obtained from gp_train()

s2

the s2 value obtained from gp_train()

Examples

data(lalonde)
cat_vars <- c("race_ethnicity", "married")
all_vars <- c("age","educ","re74","re75","married", "race_ethnicity")

X <- lalonde[,all_vars]
Y <- lalonde$re78
D <- lalonde$nsw

X_train <- X[D==0,]
Y_train <- Y[D==0]
X_test <- X[D == 1,]
Y_test <- Y[D == 1]

gp_train.out <- gp_train(X = X_train, Y = Y_train, optimize=TRUE, mixed_data = TRUE, cat_columns = cat_vars)
gp_predict.out <- gp_predict(gp_train.out, X_test)