fit <- mmrm(
formula = FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID),
data = fev_data
)
# Applying tidy method to return summary table of covariate estimates.
fit |> tidy()
fit |> tidy(conf.int = TRUE, conf.level = 0.9)
# Applying glance method to return summary table of goodness of fit statistics.
fit |> glance()
# Applying augment method to return merged `tibble` of model data, fitted and residuals.
fit |> augment()
fit |> augment(interval = "confidence")
fit |> augment(type.residuals = "pearson")
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