Nothing
fit_summary = function(y, x, beta, var_hat, delta_em, var_delta, conserve) {
n_taxa = nrow(y)
beta_hat = beta
beta_hat[, -1] = t(t(beta_hat[, -1]) - delta_em)
if (conserve) {
# Account for the variance of delta_hat
se_hat = sqrt(sweep(var_hat, 2, c(0, var_delta), "+") +
2 * sqrt(sweep(var_hat, 2, c(0, var_delta), "*")))
}else{ se_hat = sqrt(var_hat) }
d_hat = matrix(NA, nrow = nrow(y), ncol = ncol(y))
for (i in seq_len(n_taxa)) {
d_hat[i, ] = y[i, ] - x %*% beta_hat[i, ]
}
d_hat = colMeans(d_hat, na.rm = TRUE)
# Remove uninformative intercept column
beta_hat = beta_hat[, setdiff(colnames(beta_hat), "(Intercept)"),
drop = FALSE]
se_hat = se_hat[, setdiff(colnames(se_hat), "(Intercept)"),
drop = FALSE]
fiuo_fit = list(beta_hat = beta_hat, se_hat = se_hat, d_hat = d_hat)
return(fiuo_fit)
}
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