gp_predict

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

Usage

gp_predict(gp, Xtest, prior_mean = NULL)

Arguments

gp

a list-form object obtained from gp_train()

Xtest

a data frame or a matrix of testing data set

prior_mean

a numeric vector of prior mean values for Y at each test point. Required when the model was trained with a prior_mean; added to Ys_mean_orig to recover predictions on the original Y scale. Must be the same length as nrow(Xtest). (default = NULL)

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

# \donttest{
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)
# }