ordinalMIC

Minimum Inhibitory Concentration (MIC*) Estimation and groupwise comparisons from proportional-odds ordinal regression models.

Package website: https://clstacy.github.io/ordinalMIC/

Companion Website: https://LewisLabUARK.github.io/MICalculator/

Purpose

Microbiological assays often record growth on an ordered score scale (e.g., 0–4) across concentrations. Treating these scores as binary or continuous can lead to inaccurate conclusions. ordinalMIC turns cumulative-link models (ordinal::clm) into interpretable concentration-scale summaries:

• MIC* per group (the concentration where predicted probability of “no growth” is 50%)
• Δ-MIC* (additive differences)
• MIC* ratio (fold-change)
• Difference-of-differences (DoD) on additive and ratio scales to test interactions

It also provides compact output tables, a ggplot2::autoplot() for quick figures generation.

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Installation

Stable (from r-universe)

install.packages("ordinalMIC", repos = c("https://clstacy.r-universe.dev"))

From source tarball

install.packages("ordinalMIC_1.0.0.tar.gz", repos = NULL, type = "source")

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Dependencies

• Required: ggplot2, stats, utils, cli, tibble, R (>= 4.1)
• Suggested: ordinal for modeling

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Core Idea

Fit a proportional-odds ordinal regression to your scored outcomes across concentration. Then use mic_solve() to:

1. Compute group-specific MIC* on the original concentration scale (using your chosen transform/inverse).
2. Compare groups by ΔMIC* and MIC ratios with CIs and p-values.
3. Test interactions via difference-of-differences (additive and ratio scales).

Default transform is log1p for concentration; override if your model uses a different link-side transform.

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Quick Start

library(ordinalMIC)

# Example data included with the package
data(yeast_df)

# Fit a proportional-odds model (requires 'ordinal' package)
fit <- ordinal::clm(score ~ strain * treatment + log1p(conc), data = yeast_df)

# Estimate MIC* and compare groups
res <- mic_solve(
  clm_fit    = fit,
  conc_name  = "conc",
  transform_fun     = log1p,
  inv_transform_fun = expm1,
  alpha      = 0.05,
  compare_pairs = "all"
)

# Tables
head(tidy.mic_solve(res, table = "mic"))
head(tidy.mic_solve(res, table = "ratio"))

# Plots
ggplot2::autoplot(res, type = "mic")

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Main functions

mic_solve()

• Inputs: fitted ordinal::clm, newdata, conc_name, transform/inverse functions
• Outputs: MICs, Δ-MIC, ratios, DoD summaries, all with CIs and p-values

ggplot2::autoplot.mic_solve()

• Quick visualization of MICs, Δ-MICs, ratios, DoDs

tidy.mic_solve()

• Extracts summary tables for MICs, Δ-MICs, ratios, DoDs as tibble objects

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Data

Example dataset yeast_df included with the package:

Column	Type	Description
strain	factor	Yeast strain (WT, Mut)
treatment	factor	Treatment type (None, Salt)
conc	numeric	Concentration (e.g., drug concentration)
score	ordered	Growth score (0 = no growth, 4 = full growth)
rep	factor	Replicate identifier

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Modeling guidance

• Outcome must be an ordered factor with levels increasing in severity
• Transform concentration (default log1p); supply both transform and inverse
• newdata gives groups to estimate MIC*, automatically generated if not given.
• Use DoD (Difference of Difference) summaries to interpret interaction effects on MIC*

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MIC definition

MIC is the concentration where the predicted probability of “no growth” equals 0.5, computed from the fitted clm.

• MIC ratios: differences in predicted log-MIC

• DoD ratios: ratio-of-ratios (interaction on log scale)

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Troubleshooting

• Outcome not ordered: convert with ordered()
• Transform mismatch: supply correct transform/inverse
• No Estimate for MIC: reconsider dose range (censored results/no change observed)
• Comparisons: restrict with compare_pairs = "share_any"

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Citation

If you use ordinalMIC in published research, please cite: Stacy C (2025). ordinalMIC: Minimum Inhibitory Concentration Estimation from Ordinal Regression Models. R package version 1.0.0, https://clstacy.github.io/ordinalMIC/.

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Contributing

• Issues/PRs: https://github.com/clstacy/ordinalMIC/
• Include sessionInfo() and minimal example for bug reports

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License

MIT License. See LICENSE file for details.