Fitting Predictive Microbiology Models

Provides predictive microbiology model functions and convenience wrappers for fitting primary growth, microbial inactivation, dynamic, omnibus, and cardinal parameter models to experimental data using nonlinear least squares and related mixed-effects or time-varying workflows. Includes helper functions for extracting fitted values, calculating model diagnostics, and comparing fitted models. Implemented model families include those described by: Zwietering et al. (1990) , Baranyi and Roberts (1994) , Baranyi and Roberts (1995) , Buchanan et al. (1997) , Richards (1959) , Fang et al. (2012) , Fang et al. (2013) , Huang (2008) , Huang (2009) , Huang (2013) , Geeraerd et al. (2005) , van Boekel (2002) , Peleg (1999) , Mafart et al. (2002) , Albert and Mafart (2005) , Rosso et al. (1993) , Rosso et al. (1995) , and Rosso et al. (1996) .

predmicror

predmicror provides predictive microbiology model functions and convenience wrappers for fitting primary growth, microbial inactivation, and cardinal parameter models to experimental data. It also supports omnibus (nonlinear mixed-effects) and dynamic (ODE-based time-varying) modelling.

The package can be used in two complementary ways:

call the exported model equations directly inside gslnls::gsl_nls();
use the higher-level fit_*() wrappers for routine fitting, diagnostics, and model comparison.

Installation

install.packages("devtools")
devtools::install_github("fsqanalytics/predmicror")

Model catalogue

List all supported models with:

library(predmicror)

predmicror_models()
predmicror_models("growth")
predmicror_models("inactivation")
predmicror_models("cardinal")

Workflow	Wrapper	Response scale	Examples
Primary growth	`fit_growth()`	natural log (`lnN`)	`HuangFM`, `BaranyiFM`, `RossoFM`
Microbial inactivation	`fit_inactivation()`	base-10 log (`logN`)	`WeibullM`, `GeeraerdST`, `WeibullPH`
Cardinal parameters	`fit_cardinal()`	sqrt growth rate (`sqrtGR`)	`CMTI`, `CMPH`, `CMAW`, `CMInh`
Dynamic (time-varying)	`fit_dynamic_growth/inactivation()`	natural log (`lnN`)	Huang ODE, Weibull-Peleg ODE
Omnibus (mixed-effects)	`fit_omnibus_growth/inactivation()`	per primary model	any primary + secondary covariates

Quick start: growth

library(predmicror)

data(growthfull)

fit <- fit_growth(
  data = growthfull,
  model = "HuangFM",
  time = "Time",
  response = "lnN",
  start = list(Y0 = 0, Ymax = 22, MUmax = 1.7, lag = 5)
)

summary(fit)
coef(fit)
plot(fit)

Inactivation

data(bixina)

fit <- fit_inactivation(
  data = bixina,
  model = "WeibullM",
  time = "Time",
  response = "lnN",
  start = list(Y0 = 5.6, sigma = 12, alpha = 1)
)

fit_metrics(fit)
plot(fit)

Cardinal parameter model

data(salmonella)

fit <- fit_cardinal(
  data = salmonella,
  model = "CMTI",
  x = Temp,
  response = sqrtGR,
  start = list(Tmax = 42, Tmin = 1, MUopt = 1, Topt = 37)
)

coef(fit)

Model comparison

huang <- fit_growth(growthfull, model = "HuangFM", time = "Time",
  response = "lnN", start = list(Y0 = 0, Ymax = 22, MUmax = 1.7, lag = 5))
baranyi <- fit_growth(growthfull, model = "BaranyiFM", time = "Time",
  response = "lnN", start = list(Y0 = 0, Ymax = 22, MUmax = 1.7, lag = 5))

compare_models(HuangFM = huang, BaranyiFM = baranyi, sort_by = "AIC")

Dynamic (time-varying environment)

profile <- dynamic_profile(
  time = c(0, 5, 10),
  temperature = c(12, 20, 25)
)

pred <- predict_dynamic_growth(
  profile = profile,
  start = list(logN0 = 2, logNmax = 8, a = 0.08, Tmin = 5, lag = 1),
  times = seq(0, 10, length.out = 50)
)

plot(pred$time, pred$logN, type = "l")

Omnibus (mixed-effects)

# Each group (Condition) has its own curve
dat <- data.frame(
  Condition = rep(1:3, each = 7),
  Time = rep(seq(0, 6), 3),
  lnN = HuangNLM(rep(seq(0, 6), 3), Y0 = 2, Ymax = 8, MUmax = 0.6)
)

fit <- fit_omnibus_growth(
  data = dat,
  primary = "HuangNLM",
  time = "Time",
  response = "lnN",
  group = "Condition",
  random = Y0 ~ 1,
  start = c(Y0 = 2, Ymax = 8, MUmax = 0.6)
)