In openpharma/mmrm: Mixed Models for Repeated Measures

library(mmrm)

Similar to model objects created in other packages, components of mmrm and mmrm_tmb objects can be accessed with standard methods. Additionally, component() is provided to allow deeper and more precise access for those interested in digging through model output. Complete documentation of standard model output methods supported for mmrm_tmb objects can be found at the package website.

Summary

The summary method for mmrm objects provides easy access to frequently needed model components.

fit <- mmrm(
  formula = FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data
)
fit_summary <- summary(fit)

From this summary object, you can easily retrieve the coefficients table.

fit_summary$coefficients

Other model parameters and metadata available in the summary object is as follows:

str(fit_summary)

Residuals

The residuals method for mmrm objects can be used to provide three different types of residuals:

1. Response or raw residuals - the difference between the observed and fitted or predicted value. MMRMs can allow for heteroscedasticity, so these residuals should be interpreted with caution.

fit <- mmrm(
  formula = FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data
)
residuals_resp <- residuals(fit, type = "response")

1. Pearson residuals - the raw residuals scaled by the estimated standard deviation of the response. This residual type is better suited to identifying outlying observations and the appropriateness of the covariance structure, compared to the raw residuals.

residuals_pearson <- residuals(fit, type = "pearson")

1. Normalized or scaled residuals - the raw residuals are 'de-correlated' based on the Cholesky decomposition of the variance-covariance matrix. These residuals should approximately follow the standard normal distribution, and therefore can be used to check for normality (@galecki2013linear).

residuals_norm <- residuals(fit, type = "normalized")

broom extensions

mmrm also contains S3 methods methods for tidy, glance and augment which were introduced by broom. Note that these methods will work also without loading the broom package. Please see ?mmrm_tidiers for the detailed documentation.

For example, we can apply the tidy method to return a summary table of coefficient estimates:

fit |>
  tidy()

We can also specify some details to request confidence intervals of specific confidence level:

fit |>
  tidy(conf.int = TRUE, conf.level = 0.9)

Or we can apply the glance method to return a summary table of goodness of fit statistics:

fit |>
  glance()

Finally, we can use the augment method to return a merged tibble of the data, fitted values and residuals:

fit |>
  augment()

Also here we can specify details for the prediction intervals and type of residuals via the arguments:

fit |>
  augment(interval = "confidence", type.residuals = "normalized")

Other components

Specific model quantities not supported by methods can be retrieved with the component() function. The default will output all supported components.

For example, a user may want information about convergence:

component(fit, name = c("convergence", "evaluations", "conv_message"))

or the original low-level call:

component(fit, name = "call")

the user could also ask for all provided components by not specifying the name argument.

component(fit)

openpharma/mmrm documentation built on April 14, 2025, 2:10 a.m.