find_optimal_nn {GPCERF} | R Documentation |
Find the optimal hyper-parameter for the nearest neighbor Gaussian process
Description
Computes covariate balance for each combination of provided hyper-parameters and selects the hyper-parameter values that minimizes the covariate balance.
Usage
find_optimal_nn(
w_obs,
w,
y_obs,
gps_m,
design_mt,
hyperparams = expand.grid(seq(0.5, 4.5, 1), seq(0.5, 4.5, 1), seq(0.5, 4.5, 1)),
kernel_fn = function(x) exp(-x^2),
n_neighbor = 50,
block_size = 2000,
nthread = 1
)
Arguments
w_obs |
A vector of the observed exposure levels. |
w |
A vector of exposure levels at which CERF will be estimated. |
y_obs |
A vector of observed outcomes |
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 |
design_mt |
The covariate matrix of all samples (intercept excluded). |
hyperparams |
A matrix of candidate values of the hyper-parameters, each row contains a set of values of all hyper-parameters. |
kernel_fn |
The covariance function of the GP. |
n_neighbor |
The number of nearest neighbors on one side. |
block_size |
The number of samples included in a computation block.
Mainly used to balance the speed and memory requirement. Larger
|
nthread |
An integer value that represents the number of threads to be used by internal packages. |
Value
Estimated covariate balance scores for the grid of hyper-parameter values
considered in hyperparams
.