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#' @title Calculate marginal correlations with response
#' @description Calculates marginal correlations between a functional covariate and a scalar response.
#' @param object An object of type \code{funeigen} or \code{funreg}. One or the other of these is
#' needed in order to provide a smoothed reconstructed curves for the functional covariate
#' for each subject.
#' @param id The vector of subject id's. These tell which responses in \code{response}
#' correspond to which curves in \code{object}.
#' @param response The vector of responses
#' @param alpha The alpha level for confidence intervals (one minus the two-sided coverage)
#' @return Returns a list with one component for each functional
#' covariate. Each such component contains the between-subjects correlations
#' between the fitted smoothed latent values of the functional covariate,
#' and the response variable. We call this a marginal correlation because
#' it simply ignores the other functional covariates (rather than trying to
#' adjust or control for them). Both the functional regression coefficient
#' and the marginal correlation can be useful, although they have different
#' substantive interpretations.
#'@export
marginal.cor <- function(object,
id=NULL,
response=NULL,
alpha=.05
) {
if ((class(object)=="funeigen")) {
return(marginal.cor.funeigen(object=object,
id=id,
response=response,
alpha=alpha));
}
if ((class(object)=="funreg")) {
if (!is.null(id)) {
stop(paste("A separate id vector should not be specified in",
"this context because it is already included in",
"the funreg object."));
}
if (!is.null(response)) {
stop(paste("A separate response vector should not be specified in",
"this context because it is already included in",
"the funreg object."));
}
answer <- list();
for (j in 1:length(object$object.for.x.functions)) {
answer[[j]] <- marginal.cor.funeigen(object=object$object.for.x.functions[[j]],
id=object$data$id,
response=object$data$response,
alpha=alpha)
}
return(answer);
}
}
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