mse: Mean Squared Errors for Interaction Classification

In qvirus: Quantum Computing for Analyzing CD4 Lymphocytes and Antiretroviral Therapy

Mean Squared Errors for Interaction Classification

Description

Mean squared errors (MSE) for viral load differences and CD4 count differences by comparing the actual values with the group means from the classification.

Computes the mean squared error (MSE) between observed CD4 and viral load differences and their corresponding predicted payoff values within each interaction classification.

Usage

mse(object, ...)

mse(object, ...)

Arguments

object	An object of class payoffs.
...	Additional arguments passed to other methods (currently not used).

Value

A data.frame containing the MSE for CD4 count differences (mse_cds_diff) and (mse_vlogs_diff) for viral load differences.

Examples

set.seed(42)
data(cd_3)
cd_data <- cd_3[,-1]
cd_result <- cds_diff(cd_data)
data(vl_3)
vl_data <- vl_3[,-1]
vl_result <- vlogs_diff(vl_data)
result <- InteractionClassification(cd_result = cd_result, vl_result = vl_result)
mse(result)

set.seed(42)
data(cd_3)
cd_data <- cd_3[,-1]
cd_result <- cds_diff(cd_data)
data(vl_3)
vl_data <- vl_3[,-1]
vl_result <- vlogs_diff(vl_data)
result <- InteractionClassification(cd_result = cd_result, vl_result = vl_result)
data(preds)
payoffs_results <- estimate_payoffs(result, preds)
mse(payoffs_results)

qvirus documentation built on June 8, 2025, 10:06 a.m.