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predmicror

R-CMD-check pkgdown License: GPL v3

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:

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)
)

Response scale conventions

The fitting wrappers do not transform the response automatically:

  • Growth models: natural logarithm of concentration (lnN)
  • Inactivation models: base-10 logarithm (logN)
  • Cardinal models: square root of growth rate (sqrtGR)
  • Dynamic models: natural logarithm (lnN)

Documentation

Full reference and vignettes:

https://fsqanalytics.github.io/predmicror/

Citation 

citation("predmicror")

Bugs

https://github.com/fsqanalytics/predmicror/issues