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