compute_rl_deriv_gp {GPCERF}

Detect change-point in standard GP

Description

Calculates the posterior mean of the difference between left- and right-derivatives at an exposure level for the detection of change points.

Usage

compute_rl_deriv_gp(
  w,
  w_obs,
  y_obs,
  gps_m,
  hyperparam,
  kernel_fn = function(x) exp(-x),
  kernel_deriv_fn = function(x) -exp(-x)
)

Arguments

w

A scalar of exposure level of interest.

w_obs

A vector of observed exposure levels of all samples.

y_obs

A vector of observed outcome values of all samples.

gps_m

An S3 gps object including: gps: A data.frame of GPS vectors. - Column 1: GPS - Column 2: Prediction of exposure for covariate of each data sample (e_gps_pred). - Column 3: Standard deviation of e_gps (e_gps_std) used_params: - dnorm_log: TRUE or FLASE

hyperparam

A vector of hyper-parameters in the GP model.

kernel_fn

The covariance function.

kernel_deriv_fn

The partial derivative of the covariance function.

Value

A numeric value of the posterior mean of the difference between two one-sided derivatives.

Examples

set.seed(847)
data <- generate_synthetic_data(sample_size = 100)
gps_m <- estimate_gps(cov_mt = data[,-(1:2)],
                      w_all = data$treat,
                      sl_lib = c("SL.xgboost"),
                      dnorm_log = FALSE)

wi <- 8.6

val <- compute_rl_deriv_gp(w = wi,
                           w_obs = data$treat,
                           y_obs = data$Y,
                           gps_m = gps_m,
                           hyperparam = c(1,1,2))