group_mm: Fit Multiple Michaelis-Menten Curves by Group

In inferMM: Variance-Aware Michaelis-Menten Estimation and Inference

Fit Multiple Michaelis-Menten Curves by Group

Description

Clean a data frame, fit one or more working variance models within each group, and compare the competing fits using quasi-AIC as the primary criterion, with quasi-BIC and RMSE reported alongside it.

Usage

group_mm(data, s, v, groups = NULL,
  variance_models = default_variance_models(), power_values = NULL,
  include_auto = FALSE, power_selection = c("quasi_aic", "quasi_bic"),
  power_grid = default_power_grid(), interval_level = 0.95,
  invalid_response_values = c(-9999),
  invalid_substrate_values = numeric(0), allow_zero_substrate = TRUE,
  sort_x = TRUE, quiet = FALSE, variance_functions = NULL)

## S3 method for class 'group_mm'
print(x,
  digits = max(3L, getOption("digits") - 2L), ...)

## S3 method for class 'group_mm'
plot(x, which_groups = NULL, model = NULL,
  interval = TRUE, interval_type = c("prediction", "confidence"),
  level = NULL, ncol = NULL, main = NULL,
  xlab = "Substrate concentration", ylab = "Reaction velocity", ...)

Arguments

data	A data frame containing Michaelis-Menten observations.
s	Name of the substrate concentration column.
v	Name of the response column.
groups	Optional character vector of grouping columns.
variance_models	Character vector of built-in variance models. The special value "auto" adds a candidate that compares log(S + 1) with a grid of power functions and keeps the better fit.
power_values	Optional numeric vector of fixed exponents for additional S^power candidates.
include_auto	Logical; if TRUE, add one automatic candidate that selects between log(S + 1) and the supplied power_grid.
power_selection	Criterion used by the automatic candidate: "quasi_aic" or "quasi_bic".
power_grid	Candidate exponents used by the automatic candidate.
interval_level	Confidence level forwarded to fit_mm.
invalid_response_values	Values to remove before fitting.
invalid_substrate_values	Values to remove before fitting.
allow_zero_substrate	Logical; if FALSE, substrate concentrations must be strictly positive.
sort_x	Logical; sort observations within each group by concentration.
quiet	Logical; if TRUE, suppress progress messages.
variance_functions	Optional named list of fixed, vectorized positive functions h(S), forwarded to screen_mm within every group. Use variance_models = character(0) for custom-only comparisons.
x	A grouped fit object for the print and plot methods.
which_groups	Optional subset of groups to plot, matched against group_id or group_label.
model	Optional model name to use for every group. When NULL, the plotting method uses the best model recorded for each group.
interval	Logical; if TRUE, add interval bands to each panel.
interval_type	Either "prediction" or "confidence".
level	Optional interval level. Defaults to the level stored in each fit.
ncol	Optional number of panel columns in the grouped display.
main	Optional panel title or titles.
xlab	Label for the x-axis.
ylab	Label for the y-axis.
digits	Number of printed digits.
...	Additional arguments passed to S3 methods.

Details

Custom variance functions use the same definition in every group and must obey the contract in variance_function. Group-specific scales are estimated, but shape parameters in the functions are treated as fixed. Ranking criteria and intervals omit uncertainty from external shape estimation or model selection. This is a set of separate single-curve fits, not a clustered model.

Value

An object of class "group_mm" containing cleaned data, fit summaries, augmented residual output, grouped rankings, and fitted objects.

Examples

grouped <- group_mm(
  data = sdl_demo,
  s = "s_uM",
  v = "v_uM_per_min",
  groups = "enzyme",
  variance_models = c("constant", "sqrt"),
  power_values = 0.4,
  include_auto = TRUE,
  quiet = TRUE
)

grouped
head(
  grouped$comparison$best_by_group[
    ,
    c("group_label", "model", "selected_model", "quasi_aic", "quasi_bic", "rmse")
  ]
)

plot(grouped, interval_type = "confidence")

inferMM documentation built on Oct. 30, 2026, 9:06 a.m.