Skip to contents

gp_train

Source: R/gp_functions.R
gp_train.Rd

to train GP model with training data set

Usage

gp_train(
  X,
  Y,
  b = NULL,
  s2 = 0.3,
  optimize = FALSE,
  scale = TRUE,
  mixed_data = FALSE,
  cat_columns = NULL,
  Xtest = NULL
)

Arguments

X

a set of covariate data frame or matrix

Y

Y vector (outcome variable)

b

bandwidth (default = NULL)

s2

noise or a fraction of Y not explained by X (default = 0.3)

optimize

a logical value to indicate whether an automatic optimized value of S2 should be used. If FALSE, users must define s2. (default = FALSE)

scale

a logical value to indicate whether covariates should be scaled. (dafault = TRUE)

mixed_data

a logical value to indicate whether the covariates contain a categorical/binary variable (default = FALSE)

cat_columns

a character or a numerical vector indicating categorical variables (default = NULL)

Xtest

a data frame or a matrix of testing covariates. This is necessary when a non-overlapping categorical value exists between training and testing data sets. (default = NULL)

Value

post_mean_scaled

posterior distribution of Y in a scaled form

post_mean_orig

posterior distribution of Y in an original scale

post_cov_scaled

posterior covariance matrix in a scaled form

post_cov_orig

posterior covariance matrix in an original scale

K

a kernel matrix of X

prior_mean_scaled

prior distribution of mean in a scaled form

X.orig

the original matrix or data set of X

X.init

the original matrix or data set of X with categorical variables in an expanded form

X.init.mean

the initial mean values of X

X.init.sd

the initial standard deviation values of X

Y.init.mean

the initial mean value of Y

Y.init.sd

the initial standard deviation value of Y

K

the kernel matrix of X

Y

scaled Y

X

scaled X

b

bandwidth

s2

sigma squared

alpha

alpha value in Rasmussen and Williams (2006) p.19

L

L value in Rasmussen and Williams (2006) p.19

mixed_data

a logical value indicating whether X contains a categorical/binary variable

cat_columns

a character or a numerical vector indicating the location of categorical/binary variables in X

cat_num

a numerical vector indicating the location of categorical/binary variables in an expanded version of X

Xcolnames

column names of X

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)