Type:	Package
Title:	Estimates Growth Parameters from Models and Plots the Curve
Version:	1.0.0
Maintainer:	Florentin L'Homme <florentin.lhomme@gmail.com>
Description:	Fit growth curves to various known microbial growth models automatically to estimate growth parameters. Growth curves can be plotted with their uncertainty band. Growth models are: modified Gompertz model (Zwietering et al. (1990) <doi:10.1128/aem.56.6.1875-1881.1990>), Baranyi model (Baranyi and Roberts (1994) <doi:10.1016/0168-1605%2894%2990157-0>), Rosso model (Rosso et al. (1993) <doi:10.1006/jtbi.1993.1099>) and linear model (Dantigny (2005) <doi:10.1016/j.ijfoodmicro.2004.10.013>).
License:	GPL (≥ 3)
Encoding:	UTF-8
Imports:	graphics, grDevices, methods, nlstools, stats, utils
RoxygenNote:	7.3.2
Depends:	R (≥ 3.5.0)
LazyData:	true
Collate:	'utils.R' 'base.R' 'acide.R' 'baranyi.R' 'data.R' 'gompertz.R' 'linear.R' 'rosso.R'
Suggests:	knitr, rmarkdown
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-03-08 21:08:17 UTC; florentin
Author:	Florentin L'Homme [aut, cre], Clarisse Breard [aut]
Repository:	CRAN
Date/Publication:	2025-03-10 16:40:08 UTC

Baranyi regression function

Description

Regression function for Baranyi's model

Usage

.MicrobialGrowth.baranyi(
  x,
  y,
  clip = c(-Inf, Inf),
  start = list(),
  lower = list(),
  upper = list(),
  nls.args = list(),
  callbackError = NULL
)

Arguments

Details

The default values for clip, start, lower and upper are calculated based on the given data. These default values can be known through the call member of the returned value.

The nls.args argument is a list that can contain any nls function argument except formula, algorithm, start, lower and upper which are already fixed (via a homonymous or hard-coded argument).

For the callbackError argument, prefer the stop function to block or warning to not be blocking.

Value

a MicrobialGrowth-object composed of

See Also

MicrobialGrowth, .baranyi.formula

Gompertz regression function

Description

Regression function for Gompertz's model

Usage

.MicrobialGrowth.gompertz(
  x,
  y,
  clip = c(-Inf, Inf),
  start = list(),
  lower = list(),
  upper = list(),
  nls.args = list(),
  callbackError = NULL
)

Arguments

Details

The default values for clip, start, lower and upper are calculated based on the given data. These default values can be known through the call member of the returned value.

The nls.args argument is a list that can contain any nls function argument except formula, algorithm, start, lower and upper which are already fixed (via a homonymous or hard-coded argument).

For the callbackError argument, prefer the stop function to block or warning to not be blocking.

Value

a MicrobialGrowth-object composed of

See Also

MicrobialGrowth, .gompertz.formula

Linear regression function

Description

Regression function for Linear's model

Usage

.MicrobialGrowth.linear(
  x,
  y,
  clip = c(-Inf, Inf),
  start = list(),
  lower = list(),
  upper = list(),
  nls.args = list(),
  callbackError = NULL
)

Arguments

Details

The default values for clip, start, lower and upper are calculated based on the given data. These default values can be known through the call member of the returned value.

The nls.args argument is a list that can contain any nls function argument except formula, algorithm, start, lower and upper which are already fixed (via a homonymous or hard-coded argument).

For the callbackError argument, prefer the stop function to block or warning to not be blocking.

Value

a MicrobialGrowth-object composed of

See Also

MicrobialGrowth, .linear.formula

Rosso regression function

Description

Regression function for Rosso's model

Usage

.MicrobialGrowth.rosso(
  x,
  y,
  clip = c(-Inf, Inf),
  start = list(),
  lower = list(),
  upper = list(),
  nls.args = list(),
  callbackError = NULL
)

Arguments

Details

The default values for clip, start, lower and upper are calculated based on the given data. These default values can be known through the call member of the returned value.

The nls.args argument is a list that can contain any nls function argument except formula, algorithm, start, lower and upper which are already fixed (via a homonymous or hard-coded argument).

For the callbackError argument, prefer the stop function to block or warning to not be blocking.

Value

a MicrobialGrowth-object composed of

See Also

MicrobialGrowth, .rosso.formula

Baranyi equation.

Description

Baranyi equation.

Usage

.baranyi.formula(N0, Nmax, mu, lambda, base = exp(1))

Arguments

Details

The output result is by default in the form ln(N_t/N0) (with N_t the population at time t). The base used can be modified by specifying the desired base in the base argument. For example, specifying base=10 corresponds to output in the form ⁠log_{10}(N_t/N0)⁠. It is possible to specify base = NULL to retrieve the normal N_t output.

Value

a function taking as input x (the time) and outputting the value of the Baranyi equation.

Examples

f <- .baranyi.formula(0.1, 2, 0.2, 5)
f(4)
## [1] 0.3498583
f(20)
## [1] 2.344923

Check MicrobialGrowth arguments (regression function)

Description

Check the arguments passed to the regression function. Tests are generic for all models. For example, the same length for x and y, the type of the different arguments, the values inserted in start, lower and upper, etc. are tested

Usage

.checkMicrobialGrowthArgs(
  x,
  y,
  clip,
  start,
  lower,
  upper,
  nls.args,
  callbackError
)

Arguments

Details

During the check, the clip value is also updated. If the lower bound of clip is -Inf (default value), then this value is replaced by the smallest value greater than zero found in y.

Value

the modified clip value and raises an error if something is wrong.

Check MicrobialGrowth arguments (create function)

Description

Check the arguments passed to the create function. Tests are generic for all models. For example, the type and value xlim, the values of N0, Nmax, etc. are tested.

Usage

.checkMicrobialGrowthCreateArgs(N0, Nmax, mu, lambda, xlim, n)

Arguments

Value

raise an error if something is wrong.

Model Integrity Checker

Description

Function to check the integrity of a given model. Used only for development.

Usage

.checkModelIntegrity(model, verbose = TRUE)

Arguments

Value

No return value, called to check the integrity of a (new) model for the package (raises an error if the model is invalid).

Examples

# Auto-run on package build
models = listAvailableModels()
for (model in models) {
  .checkModelIntegrity(model)
}

Default NLS values

Description

Gives default values for NLS regression from x and y values. The method of calculating default values differs depending on the amount of data available in y. Default values can be pre-set by providing them in the start, lower and upper arguments.

Usage

.getDefaultNlsValues(x, y, start = list(), lower = list(), upper = list())

Arguments

Details

default values are calculated as follows:

• start

  • N0: the minimum value of y

  • Nmax: the maximum value of y

  • mu:

    • ⁠if length(y) <= ⁠THRESHOLD_FEW_DATA: the greatest slope between two contiguous points (on logged y values)

    • ⁠else⁠: the linear regression on data positioned in the middle ±25% of the amplitude on logged y

  • lambda: the highest value of x which is within the lowest 5% of amplitude of y

• lower

  • N0: the smallest value greater than zero calculated with 1/.Machine$double.xmax

  • Nmax: the mean value of y

  • mu: the amplitude on y divided by the amplitude on x

  • lambda:the minimum value of x

• upper

  • N0: the mean value of y

  • Nmax: twice the max value of y

  • mu:

    • ⁠if length(y) <= ⁠THRESHOLD_FEW_DATA: the amplitude on logged y divided by the smallest step between two contiguous x values

    • ⁠else⁠: the greatest slope between two contiguous points (on logged y values)

  • lambda: the maximum value of x

Note that it is possible, particularly when there is little data, that linear regression for start$mu is not possible, hence the presence of condition with THRESHOLD_FEW_DATA.

Value

the default values of start, lower and upper for NLS regression.

Examples

# Example data
x = c(0.00, 5.26, 10.53, 15.79, 21.05, 26.32, 31.58, 36.84, 42.11, 47.37, 52.63,
      57.89, 63.16, 68.42, 73.68, 78.95, 84.21, 89.47, 94.74, 100.00)
y = c(0.15, 0.15, 0.15, 0.16, 0.19, 0.26, 0.38, 0.58, 0.85, 1.18, 1.53, 1.86,
      2.15, 2.38, 2.55, 2.66, 2.78, 2.85, 2.89, 2.93)

# Simple example
values = .getDefaultNlsValues(x, y)
cat("N0=", values$start$N0, " with limits [", values$lower$N0, ", ", values$upper$N0,"]", sep="")
## N0=0.15 with limits [5.562685e-309, 1.4315]

# Example with specifying a starting value (which will therefore not be calculated)
values = .getDefaultNlsValues(x, y, start=list(N0=0.1))
cat("N0=", values$start$N0, " with limits [", values$lower$N0, ", ", values$upper$N0,"]", sep="")
## N0=0.1 with limits [5.562685e-309, 1.4315]

Gompertz equation.

Description

Gompertz equation.

Usage

.gompertz.formula(N0, Nmax, mu, lambda, base = exp(1))

Arguments

Details

The output result is by default in the form ln(N_t/N0) (with N_t the population at time t). The base used can be modified by specifying the desired base in the base argument. For example, specifying base=10 corresponds to output in the form ⁠log_{10}(N_t/N0)⁠. It is possible to specify base = NULL to retrieve the normal N_t output.

Value

a function taking as input x (the time) and outputting the value of the Gompertz equation.

Examples

f <- .gompertz.formula(0.1, 2, 0.2, 5)
f(4)
## [1] 0.1150952
f(20)
## [1] 2.505549

Linear equation.

Description

Linear equation.

Usage

.linear.formula(N0, Nmax, mu, lambda, base = NULL)

Arguments

Value

a function taking as input x (the time) and outputting the value of the linear equation.

Examples

f <- .linear.formula(0.1, 2, 0.2, 5)
f(4)
## [1] 0
f(20)
## [1] 3

MicrobialGrowth object

Description

Provide the skeleton of the MicrobialGrowth object. Must be completed for each model.

Usage

.new.MicrobialGrowth.core(...)

Arguments

Details

the three dots ... are passed to new.env function.

Value

a MicrobialGrowth object skeleton.

Examples

# First, create the skeleton.
model.object = .new.MicrobialGrowth.core()

# Then complete with data, functions, etc.
model.object$data$x = c(1,2,3)
model.object$data$y = c(1,2,3)
model.object$coefficients = list(N0 = 0, Nmax=0, mu=0, lambda=0)
model.object$f$formula = function(x){ return(x) }
model.object$f$confint.lower = function(x){ return(x - 1) }
model.object$f$confint.upper = function(x){ return(x + 1) }

# Specialize the object by adding a class name at first position.
class(model.object) = c("specialized.model", class(model.object))

# You can print, plot, etc., with the generic functions of MicrobialGrowth.
print(model.object)
##MicrobialGrowth, model specialized.model:
##    N0   Nmax     mu lambda
##     0      0      0      0
plot(model.object)

# Don't forget to change `isValid` to TRUE to confirm the success of the regression.
model.object$isValid = TRUE # Not a good idea here, since we have no `reg` value.

Baranyi object

Description

A MicrobialGrowth object specialized for the baranyi model. Most of the methods are pre-implemented (some of these can be overwritten for a specific regression/create function). Must be completed for data, isValid (regression successful), etc.

Usage

.new.baranyi.core(...)

Arguments

Details

the three dots ... are passed to the .new.MicrobialGrowth.core function.

Value

a Baranyi object skeleton.

Examples

# First, create the skeleton.
model.object = .new.baranyi.core()

# Then complete with data, functions, etc.
model.object$data$x = c(1,2,3)
model.object$data$y = c(1,2,3)
model.object$coefficients = list(N0 = 0, Nmax=0, mu=0, lambda=0)

# You can print, plot, etc., with the generic functions of MicrobialGrowth.
print(model.object)
##MicrobialGrowth, model specialized.model:
##    N0   Nmax     mu lambda
##     0      0      0      0
plot(model.object)

# Don't forget to change `isValid` to TRUE to confirm the success of the regression.
model.object$isValid = TRUE # Not a good idea here, since we have no `reg` value.

Gompertz object

Description

A MicrobialGrowth object specialized for the gompertz model. Most of the methods are pre-implemented (some of these can be overwritten for a specific regression/create function). Must be completed for data, isValid (regression successful), etc.

Usage

.new.gompertz.core(...)

Arguments

Details

the three dots ... are passed to the .new.MicrobialGrowth.core function.

Value

a Gompertz object skeleton.

Examples

# First, create the skeleton.
model.object = .new.gompertz.core()

# Then complete with data, functions, etc.
model.object$data$x = c(1,2,3)
model.object$data$y = c(1,2,3)
model.object$coefficients = list(N0 = 0, Nmax=0, mu=0, lambda=0)

# You can print, plot, etc., with the generic functions of MicrobialGrowth.
print(model.object)
##MicrobialGrowth, model specialized.model:
##    N0   Nmax     mu lambda
##     0      0      0      0
plot(model.object)

# Don't forget to change `isValid` to TRUE to confirm the success of the regression.
model.object$isValid = TRUE # Not a good idea here, since we have no `reg` value.

Linear object

Description

A MicrobialGrowth object specialized for the linear model. Most of the methods are pre-implemented (some of these can be overwritten for a specific regression/create function). Must be completed for data, isValid (regression successful), etc.

Usage

.new.linear.core(...)

Arguments

Details

the three dots ... are passed to the .new.MicrobialGrowth.core function.

Value

a linear object skeleton.

Examples

# First, create the skeleton.
model.object = .new.linear.core()

# Then complete with data, functions, etc.
model.object$data$x = c(1,2,3)
model.object$data$y = c(1,2,3)
model.object$coefficients = list(N0 = 0, Nmax=0, mu=0, lambda=0)

# You can print, plot, etc., with the generic functions of MicrobialGrowth.
print(model.object)
##MicrobialGrowth, model specialized.model:
##    N0   Nmax     mu lambda
##     0      0      0      0
plot(model.object)

# Don't forget to change `isValid` to TRUE to confirm the success of the regression.
model.object$isValid = TRUE # Not a good idea here, since we have no `reg` value.

Rosso object

Description

A MicrobialGrowth object specialized for the rosso model. Most of the methods are pre-implemented (some of these can be overwritten for a specific regression/create function). Must be completed for data, isValid (regression successful), etc.

Usage

.new.rosso.core(...)

Arguments

Details

the three dots ... are passed to the .new.MicrobialGrowth.core function.

Value

a Rosso object skeleton.

Examples

# First, create the skeleton.
model.object = .new.rosso.core()

# Then complete with data, functions, etc.
model.object$data$x = c(1,2,3)
model.object$data$y = c(1,2,3)
model.object$coefficients = list(N0 = 0, Nmax=0, mu=0, lambda=0)

# You can print, plot, etc., with the generic functions of MicrobialGrowth.
print(model.object)
##MicrobialGrowth, model specialized.model:
##    N0   Nmax     mu lambda
##     0      0      0      0
plot(model.object)

# Don't forget to change `isValid` to TRUE to confirm the success of the regression.
model.object$isValid = TRUE # Not a good idea here, since we have no `reg` value.

Coefficient argument parser (create function)

Description

Parses the coefficients passed to the create function to obtain 3 values: one for the main curve and two for the confint curves. These values are sorted.

Usage

.parseMicrobialGrowthCreateArgs(x)

Arguments

Value

the 3 ordered values for the given coefficient.

Examples

.parseMicrobialGrowthCreateArgs(1)
## [1] 1 1 1

.parseMicrobialGrowthCreateArgs(c(1,2))
## [1] 1.0 1.5 2.0

.parseMicrobialGrowthCreateArgs(c(1,2,3))
## [1] 1 2 3

.parseMicrobialGrowthCreateArgs(c(3,1,2))
## [1] 1 2 3

Rosso equation.

Description

Rosso equation.

Usage

.rosso.formula(N0, Nmax, mu, lambda, base = exp(1))

Arguments

Details

The output result is by default in the form ln(N_t/N0) (with N_t the population at time t). The base used can be modified by specifying the desired base in the base argument. For example, specifying base=10 corresponds to output in the form ⁠log_{10}(N_t/N0)⁠. It is possible to specify base = NULL to retrieve the normal N_t output.

Value

a function taking as input x (the time) and outputting the value of the Rosso equation.

Examples

f <- .rosso.formula(0.1, 2, 0.2, 5)
f(4)
## [1] 0
f(20)
## [1] 2.32998

Acid

Description

Modeling of an acid with alpha its sensitivity and MIC its minimum inhibition concentration. The default concentration is 1g/L.

Usage

Acid(alpha, MIC, concentration = 1)

Arguments

Details

The arguments alpha and MIC can be given as one to three values.

A single value means that getCoefMin, getCoefMid and getCoefMax will return the same coefficient.

Two values symbolize some sort of uncertainty about alpha and/or MIC. The functions getCoefMin and getCoefMax will use the pair (alpha, MIC) which respectively minimizes and maximizes the coefficients. The getCoefMid function will return a coefficient based on the average of the values entered.

Three values act as for two values except that for the function getCoefMid will use this third value (middle value) for the calculation of the coefficient.

Please note, entering several values acts as a pool of available values, and not as pairs (alpha, MIC). For example, the getCoefMin function will use the minimum value alpha and the minimum value MIC. If you wish to specify pairs (alpha, MIC), see Acid.SpecificPair which will determine, for example for getCoefMin, the pair (alpha, MIC) minimizing the coefficient.

Value

the acid modeled with the following accessible attributes:

See Also

Acid.SpecificPair

Examples

# Classic instantiation
aceticAcid <- Acid(1.245, 5.47)
print(aceticAcid)
## acid {alpha=1.245, MIC=5.47g/L, concentration=1g/L}

# Classic instantiation by specifying a concentration
print( Acid(1.245, 5.47, 3) )
## acid {alpha=1.245, MIC=5.47g/L, concentration=3g/L}

# Instantiation with multiple `alpha` and `MIC` values (see details section)
print( Acid(c(0.98, 1.1, 1.51), c(5.26, 5.68)) )
## acid {alpha=[0.98, 1.1, 1.51], MIC=[5.26, 5.68]g/L, concentration=1g/L}

# Generic operators (`+`, `*`, etc.) can change the `concentration` of the acid.
print(aceticAcid / 2)
## acid {alpha=1.245, MIC=5.47g/L, concentration=0.5g/L}
print(aceticAcid + 2)
## acid {alpha=1.245, MIC=5.47g/L, concentration=3g/L}

# Without having to pre-define specific concentrations, and with the default `concentration` (1g/L),
# you can dynamically change the acid concentration as follows:
for (concentration in c(0.5, 1, 5, 10)) {
  print(concentration * aceticAcid)
}
## acid {alpha=1.245, MIC=5.47g/L, concentration=0.5g/L}
## acid {alpha=1.245, MIC=5.47g/L, concentration=1g/L}
## acid {alpha=1.245, MIC=5.47g/L, concentration=5g/L}
## acid {alpha=1.245, MIC=5.47g/L, concentration=10g/L}

try({
  # Acid can be applied to a MicrobilogicalGrowth-object with the `+` addition operator.
  # Note that the acid should be on the right side, otherwise an error is raised.
  MyMicrobialGrowthObject + aceticAcid
  ## returns the MicrobialGrowth-object affected by the acid (several acids can be applied)
})

Acid - specific pair (alpha, MIC)

Description

Modeling of an acid with alpha its sensitivity and MIC its minimum inhibition concentration. The default concentration is 1g/L.

Usage

Acid.SpecificPair(pairs, concentration = 1)

Arguments

Details

The pairs argument can be given as one to three pairs.

A single pair means that getCoefMin, getCoefMid and getCoefMax will return the same coefficient.

Two pairs means that one of them will be used for getCoefMin and the other for getCoefMax. The getCoefMid function will use an average value of the two pairs.

Three pairs acts like two pairs except that the getCoefMid function will use this third pair (middle value) to calculate the coefficient. Note that the pair (alpha, MIC) used by getCoefMid neither minimizes nor maximizes the coefficient (in other words, it is the pair which is neither used in getCoefMin nor in getCoefMax).

Please note that if you do not want to use specific pairs but ranges of values for alpha and/or MIC, use the parent function Acid.

Value

the acid modeled with the following accessible attributes:

See Also

Acid

Examples

# Classic instantiation
print(Acid.SpecificPair(list(c(1.245, 5.47))))
## acid {{alpha=1.245, MIC=5.47g/L}, concentration=1g/L}

# Classic instantiation by specifying a concentration
print(Acid.SpecificPair(list(c(1.245, 5.47)), 3))
## acid {{alpha=1.245, MIC=5.47g/L}, concentration=3g/L}

# Instantiation with multiple couples (`alpha`, `MIC`) (see details section)
aceticAcid <- Acid.SpecificPair(list(c(0.98,5.68),c(1.51,5.26)))
print(aceticAcid)
## acid {{alpha=0.98, MIC=5.68g/L},
##       {alpha=1.51, MIC=5.26g/L}, concentration=1g/L}

# Generic operators (`+`, `*`, etc.) can change the `concentration` of the acid.
print(aceticAcid / 2)
## acid {{alpha=0.98, MIC=5.68g/L},
##       {alpha=1.51, MIC=5.26g/L}, concentration=0.5g/L}
print(aceticAcid + 2)
## acid {{alpha=0.98, MIC=5.68g/L},
##       {alpha=1.51, MIC=5.26g/L}, concentration=3g/L}

# Without having to pre-define specific concentrations, and with the default `concentration` (1g/L),
# you can dynamically change the acid concentration as follows:
for (concentration in c(0.5, 1, 5, 10)) {
  print(concentration * aceticAcid)
}
## acid {{alpha=0.98, MIC=5.68g/L},
##       {alpha=1.51, MIC=5.26g/L}, concentration=0.5g/L}
## acid {{alpha=0.98, MIC=5.68g/L},
##       {alpha=1.51, MIC=5.26g/L}, concentration=1g/L}
## acid {{alpha=0.98, MIC=5.68g/L},
##       {alpha=1.51, MIC=5.26g/L}, concentration=5g/L}
## acid {{alpha=0.98, MIC=5.68g/L},
##       {alpha=1.51, MIC=5.26g/L}, concentration=10g/L}

try({
  # Acid can be applied to a MicrobilogicalGrowth-object with the `+` addition operator.
  # Note that the acid should be on the right side, otherwise an error is raised.
  MyMicrobialGrowthObject + aceticAcid
  ## returns the MicrobialGrowth-object affected by the acid (several acids can be applied)
})

MicrobialGrowth regression function

Description

Regression function to different microbial growth models.

Usage

MicrobialGrowth(
  x,
  y,
  model = "gompertz",
  clip = c(-Inf, Inf),
  start = list(),
  lower = list(),
  upper = list(),
  nls.args = list(),
  callbackError = NULL,
  ...
)

Arguments

Details

Use listAvailableModels() function to see all values accepted by model parameter.

The default values for clip, start, lower and upper are calculated based on the given data. These default values can be known through the call member of the returned value.

The nls.args argument is a list that can contain any nls function argument except formula, algorithm, start, lower and upper which are already fixed (via a homonymous or hard-coded argument).

For the callbackError argument, prefer the stop function to block or warning to not be blocking.

Value

a MicrobialGrowth-object composed of

Examples

# Using the embedded data example_data
# Simple example
g <- MicrobialGrowth(example_data$time, example_data$y1, model="gompertz")

# Multiple regression example
G <- MicrobialGrowth(example_data$time, example_data[2:ncol(example_data)], model="gompertz")

# Example of multiple parameter changes
g <- MicrobialGrowth(example_data$time, example_data$y1, model="gompertz",
                           clip = c(0.15, Inf), start = list(N0=0.1, Nmax=2,
                           mu=0.05, lambda=40), lower = list(lambda = 40))

# Example of using `nls.args` to apply weight to some data
g <- MicrobialGrowth(example_data$time, example_data$y1, model="gompertz",
nls.args = list(weights = (function(x){(x >= 50 & x <= 70)*9 + 1})(example_data$time)))

# Example of callbackError (remaining non-blocking)
g <- MicrobialGrowth(example_data$time, example_data$y15, model="gompertz",
                           callbackError = warning)

# Example of callbackError (becoming blocking)
try(
  g <- MicrobialGrowth(c(1,2,3,4,5),c(1,1,1,1,1), model="gompertz", callbackError = stop)
)

MicrobialGrowth create function

Description

MicrobialGrowth-object creator from the 4 biological meaning parameters.

Usage

MicrobialGrowth.create(
  N0,
  Nmax,
  mu,
  lambda,
  xlim,
  model = "gompertz",
  n = 101,
  ...
)

Arguments

Details

The N0, Nmax, mu and lambda parameter-coefficients can be given as one to three values.

A single value means that the coefficient and the confidence interval values will be identical.

Two values means that they will correspond to the confidence interval, and the coefficient will be calculated as the average of these two values.

Three values means that each of these values will be associated with the coefficient or the confidence interval.

Values are always sorted automatically, which means that c(2,1,3) is equivalent to c(1,2,3).

Value

a MicrobialGrowth-object composed of

Examples

# Simple example
g <- MicrobialGrowth.create(N0 = 0.14, Nmax = 1.43, mu = 0.07, lambda = 45,
     xlim = c(0, 100), model="gompertz")

# Example from a regression (whose values can be stored and then reused later)
g <- MicrobialGrowth(example_data$time, example_data$y1, model="gompertz")
c <- g$coefficients
g2 <- MicrobialGrowth.create(c$N0, c$Nmax, c$mu, c$lambda,
      xlim = c(min(g$data$x),max(g$data$x)), n = length(g$data$x), model="gompertz")

# Example with confidence intervals
g <- MicrobialGrowth.create(N0 = c(0.13, 0.15), Nmax = 1.43, mu = c(0.05, 0.07, 0.09),
lambda = c(45, 49, 43), xlim = c(0, 100), model="gompertz")
# Coefficient N0 is 0.14 and the confidence interval is (0.13, 0.15)
# Coefficient Nmax is 1.43 and the confidence interval is (1.43, 1.43)
# Coefficient mu is 0.07 and the confidence interval is (0.05, 0.09)
# Coefficient lambda is 45 and the confidence interval is (43, 49)

Operators on the Acid Class

Description

Operators for the "Acid" class.

Usage

## S3 method for class 'acid'
Ops(e1, e2)

Arguments

Details

Operations between an acid and a numeric are the most common case. In this case, the operation is carried out on the concentration member of the acid. A new acid-object is returned with the new concentration.

Operations between acids are tolerated (but not recommended). To do this, the two acids must have the same alpha sensitivity and the same MIC, and the operation is carried out between the concentrations of the two acids. A new acid-object is returned with the new concentration.

The addition operator + can be used between MicrobialGrowth-object (left side) and an acid-object (right side). This operation symbolizes the application of the acid to the MicrobialGrowth-object. A new MicrobialGrowth-object is returned with its coefficients (and confidence intervals) modified by the acid.

Value

a new acid or MicrobialGrowth-object.

Examples

# Acids and numerics
print( Acid(1.245, 5.47) * 5 )
## acid {alpha=1.245, MIC=5.47g/L, concentration=5g/L}

print( Acid(1.245, 5.47) / 3 )
## acid {alpha=1.245, MIC=5.47g/L, concentration=0.333333333333333g/L}

print( 3 / Acid(1.245, 5.47) )
## acid {alpha=1.245, MIC=5.47g/L, concentration=3g/L}

print( 3 / Acid(1.245, 5.47, 0.5) )
## acid {alpha=1.245, MIC=5.47g/L, concentration=6g/L}

# Acids and acids
print( Acid(1.245, 5.47, 0.5) + Acid(1.245, 5.47, 2) )
## acid {alpha=1.245, MIC=5.47g/L, concentration=2.5g/L}

try({
  print( Acid(1.245, 5.47, 0.5) + Acid(1, 5.47, 2) )
  ## throws an error since `alpha` and/or `MIC` are not equal
})

# Acids and MicrobialGrowth-object
g <- MicrobialGrowth.create(N0 = c(0.13, 0.15), Nmax = 1.43, mu = c(0.05, 0.07, 0.09),
lambda = c(45, 49, 43), xlim = c(0, 100), model="gompertz")
aceticAcid <- Acid(1.245, 5.47)
{
  cat("Before :\n")
  print(g)
  cat("After:\n")
  print(g + aceticAcid)
}
## Before :
## MicrobialGrowth, model gompertz:
##     N0   Nmax     mu lambda
##   0.14   1.43   0.07  45.00
## After:
## MicrobialGrowth, model gompertz:
##          N0        Nmax          mu      lambda
##  0.14000000  1.43000000  0.06156075 51.16896670

# Also works with the `acid.specific.pair` subclass
print( Acid.SpecificPair(list(c(0.98, 5.68), c(1.51, 5.26))) )
## acid {{alpha=0.98, MIC=5.68g/L},
##       {alpha=1.51, MIC=5.26g/L}, concentration=6g/L}

Threshold when little data is used in a regression

Description

Number of data below which the usual methods for choosing starting values (start) and limits (lower and upper) will not be used in favor of a secondary method more suited to the low number of data.

Usage

THRESHOLD_FEW_DATA

Format

An object of class numeric of length 1.

Baranyi create function

Description

Baranyi-object creator from the 4 biological meaning parameters.

Usage

baranyi.create(N0, Nmax, mu, lambda, xlim, n = 101)

Arguments

Value

a Baranyi-object composed of

See Also

MicrobialGrowth.create

Example data from the MicrobialGrowth package

Description

TODO : Describe them (origine, type, etc.)

Author(s)

Clarisse Breard clarisse.breard@inrae.fr

Create function name getter

Description

Returns the name of the creation function associated with the model.

Usage

getCreateFunctionName(model)

Arguments

Value

the string corresponding to the creation function of the model. Warning, this function does not check the existence of the corresponding function.

Examples

getCreateFunctionName("gompertz")
## [1] "gompertz.create"

# Note that this does not verify the existence
getCreateFunctionName("NonExistentFunction")
## [1] "NonExistentFunction.create"

Formula getter

Description

Returns the formula associated with the specified model.

Usage

getFormula(model)

Arguments

Value

the function corresponding to the formula of the model.

Examples

f <- getFormula("gompertz")
# We need to set the parameters (N0, ..., lambda)
f2 <- f(0.1, 2, 0.2, 5)
# And we can then use the function "f(x)" with x the time
f2(4)
## [1] 0.1150952

# The same, more direct
F <- getFormula("gompertz")(0.1, 2, 0.2, 5)
F(4)
## [1] 0.1150952

Regression function name getter

Description

Returns the name of the regression function associated with the model.

Usage

getFunctionName(model)

Arguments

Value

the string corresponding to the regression function of the model. Warning, this function does not check the existence of the corresponding function.

Examples

getFunctionName("gompertz")
## [1] ".MicrobialGrowth.gompertz"

# Note that this does not verify the existence
getFunctionName("NonExistentFunction")
## [1] ".MicrobialGrowth.NonExistentFunction"

Model name getter

Description

Returns the name of the model used.

Usage

getModelName(x)

Arguments

Details

scans the classes of the object which must correspond on the one hand to the generic class "MicrobialGrowth" and on the other hand to the class-model. It is this second that is returned.

Value

the name of the model used.

Examples

g <- MicrobialGrowth(example_data$time, example_data$y1, model="gompertz")
getModelName(g)
## [1] "gompertz"

Gompertz create function

Description

Gompertz-object creator from the 4 biological meaning parameters.

Usage

gompertz.create(N0, Nmax, mu, lambda, xlim, n = 101)

Arguments

Value

a Gompertz-object composed of

See Also

MicrobialGrowth.create

Graphical Example of Modified Gompertz Equation.

Description

Graphical Example of Modified Gompertz Equation.

Usage

gompertz.explain(
  N0 = 0.14,
  Nmax = 1.43,
  mu = 0.07,
  lambda = 40,
  xlim = c(0, 100)
)

Arguments

Value

No return value, called to plot a MicrobialGrowth object with the Gompertz model to illustrate the different coefficients.

Examples

gompertz.explain()

gompertz.explain(0.15, 2, 0.1, 40, c(0,100))

MicrobialGrowth class

Description

Test if a variable or variable list is/are MicrobialGrowth-object(s).

Usage

is.MicrobialGrowth(x)

Arguments

Value

TRUE if the object or all objects are of class MicrobialGrowth.

Examples

# TRUE return
g1 <- MicrobialGrowth(example_data$time, example_data$y1)
g2 <- MicrobialGrowth.create(N0 = 0.14, Nmax = 1.43, mu = 0.07, lambda = 45,
                                   xlim = c(0, 100), model="gompertz")
is.MicrobialGrowth(g1)
is.MicrobialGrowth(g2)
is.MicrobialGrowth(list(g1,g2))

# FALSE return
is.MicrobialGrowth(1)
is.MicrobialGrowth(list())
is.MicrobialGrowth(c(g1, g2))
is.MicrobialGrowth(list(g1, g2, 1))

Acid class

Description

Test if a variable is an Acid-object.

Usage

is.acid(x)

Arguments

Value

TRUE if the object is of class acid.

Examples

# TRUE return
is.acid( Acid(1.245, 5.47, 3) )
is.acid( Acid(c(0.98, 1.1, 1.51), c(5.26, 5.68)) )
# Acid.SpecificPair-objects are also Acid-objects
is.acid( Acid.SpecificPair(list(c(0.98, 5.68), c(1.51, 5.26))) )

# FALSE return
is.acid(1)
is.acid( list(Acid(1.245, 5.47, 3), Acid(1.245, 5.47, 3)) )

Acid.SpecificPair class

Description

Test if a variable is an Acid.SpecificPair-object.

Usage

is.acid.specific.pair(x)

Arguments

Value

TRUE if the object is of class acid.specific.pair.

Examples

# TRUE return
is.acid.specific.pair( Acid.SpecificPair(list(c(0.98, 5.68), c(1.51, 5.26))) )

# FALSE return
is.acid.specific.pair(1)
is.acid.specific.pair( Acid(1.245, 5.47, 3) )
is.acid.specific.pair( list(Acid(1.245, 5.47, 3), Acid(1.245, 5.47, 3)) )

Baranyi class

Description

Test if a variable or variable list is/are baranyi-object(s).

Usage

is.baranyi(x)

Arguments

Value

TRUE if the object or all objects are of class baranyi.

Examples

# TRUE return
r1 <- MicrobialGrowth(example_data$time, example_data$y1, model="baranyi")
r2 <- MicrobialGrowth.create(N0 = 0.14, Nmax = 1.43, mu = 0.07, lambda = 45,
                                   xlim = c(0, 100), model="baranyi")
is.baranyi(r1)
is.baranyi(r2)
is.baranyi(c(r1, r2))
is.baranyi(list(r1,r2))

# FALSE return
is.baranyi(1)
is.baranyi(list())
is.baranyi(c(r1, r2, 1))
is.baranyi(list(r1, r2, 1))

Gompertz class

Description

Test if a variable or variable list is/are gompertz-object(s).

Usage

is.gompertz(x)

Arguments

Value

TRUE if the object or all objects are of class gompertz.

Examples

# TRUE return
g1 <- MicrobialGrowth(example_data$time, example_data$y1)
g2 <- MicrobialGrowth.create(N0 = 0.14, Nmax = 1.43, mu = 0.07, lambda = 45,
                                   xlim = c(0, 100), model="gompertz")
is.gompertz(g1)
is.gompertz(g2)
is.gompertz(c(g1, g2))
is.gompertz(list(g1,g2))

# FALSE return
is.gompertz(1)
is.gompertz(list())
is.gompertz(c(g1, g2, 1))
is.gompertz(list(g1, g2, 1))

Linear class

Description

Test if a variable or variable list is/are linear-object(s).

Usage

is.linear(x)

Arguments

Value

TRUE if the object or all objects are of class linear.

Examples

# TRUE return
r1 <- MicrobialGrowth(example_data$time, example_data$y1, model="linear")
r2 <- MicrobialGrowth.create(N0 = 0.14, Nmax = 1.43, mu = 0.07, lambda = 45,
                                   xlim = c(0, 100), model="linear")
is.linear(r1)
is.linear(r2)
is.linear(c(r1, r2))
is.linear(list(r1,r2))

# FALSE return
is.linear(1)
is.linear(list())
is.linear(c(r1, r2, 1))
is.linear(list(r1, r2, 1))

Rosso class

Description

Test if a variable or variable list is/are rosso-object(s).

Usage

is.rosso(x)

Arguments

Value

TRUE if the object or all objects are of class rosso.

Examples

# TRUE return
r1 <- MicrobialGrowth(example_data$time, example_data$y1, model="rosso")
r2 <- MicrobialGrowth.create(N0 = 0.14, Nmax = 1.43, mu = 0.07, lambda = 45,
                                   xlim = c(0, 100), model="rosso")
is.rosso(r1)
is.rosso(r2)
is.rosso(c(r1, r2))
is.rosso(list(r1,r2))

# FALSE return
is.rosso(1)
is.rosso(list())
is.rosso(c(r1, r2, 1))
is.rosso(list(r1, r2, 1))

Linear create function

Description

Linear-object creator from the 4 biological meaning parameters.

Usage

linear.create(N0, Nmax, mu, lambda, xlim, n = 101)

Arguments

Value

a Linear-object composed of

See Also

MicrobialGrowth.create

List available models.

Description

List available models.

Usage

listAvailableModels()

Details

lists the models by scanning the available ".MicrobialGrowth.m" regression functions, with "m" the name of the model.

Value

the list of available models.

Examples

listAvailableModels()
## [1] "baranyi"  "gompertz"  "rosso"

MicrobialGrowth plot function

Description

Plot function of MicrobialGrowth-objects.

Usage

## S3 method for class 'MicrobialGrowth'
plot(
  x,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  n = 101,
  base = exp(1),
  display.coefficients = TRUE,
  display.model = TRUE,
  display.confint = FALSE,
  reg.args = list(col = "red"),
  title.args = list(line = 2),
  model.args = list(side = 4, line = 0),
  coefficients.args = list(cex = 0.9, line = 0.2, side = 3),
  confint.args = list(),
  ...
)

Arguments

Details

Similar to the curve function, the n argument corresponds to the number of points evaluated to draw the curves of regression, confidence bounds and the associated area. Increase its value for a more accurate representation.

When base is not NULL, the plot produced is log_n(N/N0), where n is the value specified in the base argument.

Value

No return value, called to plot a MicrobialGrowth-object.

Examples

# Example plot of a MicrobialGrowth-object obtained by regression
g <- MicrobialGrowth(example_data$time, example_data$y1, model="gompertz")
plot(g)

# Example plot of a user-created MicrobialGrowth-object (via MicrobialGrowth.create)
g <- MicrobialGrowth.create(N0 = 0.14, Nmax = 1.43, mu = 0.07, lambda = 45,
                                  xlim = c(0, 100), model="gompertz")
plot(g)

# Example plot with usual graphical parameters
plot(g, pch = 4, cex = 2, col = "blue", xlab = "Time (hours)", main = "Gompertz regression")

# Example of plot hiding the coefficients and customizing the curve obtained by regression
plot(g, display.coefficients = FALSE, reg.args = list(col = "green", lty = 2, lwd = 5))

# Example of a plot displaying the curves and area of the confidence interval
g <- MicrobialGrowth(example_data$time, example_data$y1, model="gompertz")
plot(g, display.confint = TRUE)

# Example of a plot customizing the confidence interval
plot(g, xlim = c(80, 100), ylim = c(1.8, 2.4), # Zoom in to see the example better
display.confint = TRUE,
confint.args = list(
  lines = list(col = "purple", lty = 2, lwd = 2),
  area = list(col = "green", opacity = 0.1)
))

# Example of a plot customizing the display of coefficients and titles
plot(g, main = "Gompertz",
coefficients.args = list(cex = 1.5, side = 4, line = 1),
title.args = list(col.main = "blue", col.lab = "red"))

Gompertz plot function

Description

Default plot.MicrobialGrowth function can be overwritten with the following function

Usage

## S3 method for class 'gompertz'
plot(x, ...)

Arguments

Value

No return value, called to plot a MicrobialGrowth-object based on the Gompertz model.

See Also

plot.MicrobialGrowth

Linear plot function

Description

Default plot.MicrobialGrowth function can be overwritten with the following function

Usage

## S3 method for class 'linear'
plot(x, base = NULL, ...)

Arguments

Value

No return value, called to plot a MicrobialGrowth-object based on the linear model.

See Also

plot.MicrobialGrowth

MicrobialGrowth print function

Description

Print function of MicrobialGrowth-objects.

Usage

## S3 method for class 'MicrobialGrowth'
print(x, ...)

Arguments

Value

No return value, called to print information about a MicrobialGrowth-object.

See Also

MicrobialGrowth, MicrobialGrowth.create

Examples

# Print from regressed MicrobialGrowth-object
g <- MicrobialGrowth(example_data$time, example_data$y1, model="gompertz")
print(g) # or just `g` in the console

# Print from a user-created MicrobialGrowth-object (via MicrobialGrowth.create)
g <- MicrobialGrowth.create(N0 = 0.14, Nmax = 1.43, mu = 0.07, lambda = 45,
                                  xlim = c(0, 100), model="gompertz")
print(g) # or just `g` in the console

Acid print function

Description

Print function of Acid-object.

Usage

## S3 method for class 'acid'
print(x, ...)

Arguments

Value

No return value, called to print information about a Acid-object.

See Also

Acid

Examples

print( Acid(1.245, 5.47, 3) )
## acid {alpha=1.245, MIC=5.47g/L, concentration=3g/L}

print( Acid(c(0.98, 1.1, 1.51), c(5.26, 5.68)) )
## acid {alpha=[0.98, 1.1, 1.51], MIC=[5.26, 5.68]g/L, concentration=1g/L}

Acid.SpecificPair print function

Description

Print function of Acid.SpecificPair-object.

Usage

## S3 method for class 'acid.specific.pair'
print(x, sep = ",\n      ", ...)

Arguments

Value

No return value, called to print information about a Acid.SpecificPair-object.

See Also

Acid.SpecificPair

Examples

print( Acid.SpecificPair(list(c(1.245, 5.47)), 3) )
## acid {{alpha=1.245, MIC=5.47g/L}, concentration=3g/L}

print( Acid.SpecificPair(list(c(0.98, 5.68), c(1.51, 5.26))) )
## acid {{alpha=0.98, MIC=5.68g/L},
##       {alpha=1.51, MIC=5.26g/L}, concentration=1g/L}

Gompertz print function

Description

Default print.MicrobialGrowth function can be overwritten with the following function

Usage

## S3 method for class 'gompertz'
print(x, ...)

Arguments

Value

No return value, called to print information about a MicrobialGrowth-object based on the Gompertz model.

See Also

print.MicrobialGrowth

Rosso create function

Description

Rosso-object creator from the 4 biological meaning parameters.

Usage

rosso.create(N0, Nmax, mu, lambda, xlim, n = 101)

Arguments

Value

a Rosso-object composed of

See Also

MicrobialGrowth.create

MicrobialGrowth summary function

Description

Summarizes the regression of an MicrobialGrowth-object.

Usage

## S3 method for class 'MicrobialGrowth'
summary(object, ...)

Arguments

Details

Equivalent to summary(MicrobialGrowthObject$reg, ...) to which we add the corresponding model member and the summary.MicrobialGrowth class.

Value

The summary of the successful regression, NULL otherwise.

Examples

# Simple example
g <- MicrobialGrowth(example_data$time, example_data$y1)
summary(g)

# Example without summary available
g <- MicrobialGrowth(example_data$time, example_data$y15)
summary(g)

g <- MicrobialGrowth.create(0.14, 1.5, 0.07, 45, c(0,100), model="gompertz")
summary(g)