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#' Mean Squared Errors for Payoff Predictions
#'
#' Computes the mean squared error (MSE) between observed CD4 and viral load differences
#' and their corresponding predicted payoff values within each interaction classification.
#'
#' @param object An object of class `payoffs`.
#' @param ... Additional arguments passed to other methods (currently not used).
#'
#' @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)
#' data(preds)
#' payoffs_results <- estimate_payoffs(result, preds)
#' mse(payoffs_results)
#'
#' @export
mse <- function(object, ...) {
UseMethod("mse")
}
#' Mean Squared Errors for Payoff Predictions
#'
#' Computes the mean squared error (MSE) between observed CD4 and viral load differences
#' and their corresponding predicted payoff values within each interaction classification.
#'
#' @param object An object of class `payoffs`.
#' @param ... Additional arguments passed to other methods (currently not used).
#' @exportS3Method mse payoffs
mse.payoffs <- function(object, ...){
merged_result <-
magrittr::`%>%`(magrittr::`%>%`(object,
dplyr::mutate(
payoffs_cds_e = cds3_diff - predictions,
payoffs_vlogs_e = vlogs3_diff - predictions,
payoffs_cds_se = (payoffs_cds_e)^2,
payoffs_vlogs_se = (payoffs_vlogs_e)^2
)
),
dplyr::summarise(
mse_pi_cds = mean(payoffs_cds_se),
mse_pi_vlogs = mean(payoffs_vlogs_se),
n = dplyr::n(),
.groups = 'drop'
))
class(merged_result) <- c("mse.payoffs", "data.frame")
return(merged_result)
}
utils::globalVariables(c("cds3_diff", "payoffs_cds_e", "payoffs_cds_se", "vlogs3_diff", "payoffs_vlogs_e", "payoffs_vlogs_se", "mse_pi_cds", "mse_pi_vlogs"))
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