compute_m_sigma {GPCERF} | R Documentation |
Calculates the induced covariate balance associated with one hyper-parameter configuration in standard GP.
compute_m_sigma(
hyperparam,
outcome_data,
treatment_data,
covariates_data,
w,
gps_m,
tuning,
kernel_fn = function(x) exp(-x^2)
)
hyperparam |
A vector of values of hyper-parameters.
|
outcome_data |
A vector of outcome data. |
treatment_data |
A vector of treatment data. |
covariates_data |
A data frame of covariates data. |
w |
A vector of exposure levels at which the CERF is estimated. |
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 |
tuning |
The function is used for parameter tuning (default = TRUE) or estimation (FALSE) |
kernel_fn |
The covariance function of GP. |
A list containing two elements:
A vector of absolute weighted correlation of each covariate to the exposure, which is the metric for covariate balance
An estimated CERF at w_all
based on the hyper-parameter values in
param
.