, Clarisse Breard
[aut] | Title: | Estimates Growth Parameters from Models and Plots the Curve |
|---|---|
| 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>). |
| Authors: | Florentin L'Homme [aut, cre]
, Clarisse Breard
[aut] |
| Maintainer: | Florentin L'Homme <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.0.0 |
| Built: | 2026-06-08 08:41:44 UTC |
| Source: | https://github.com/cran/MicrobialGrowth |
Baranyi equation.
.baranyi.formula(N0, Nmax, mu, lambda, base = exp(1)).baranyi.formula(N0, Nmax, mu, lambda, base = exp(1))
N0 |
initial population. |
Nmax |
final/maximum population. |
mu |
growth rate. |
lambda |
latency time. |
base |
the logarithm base used for plot y-scaling. By default, the natural logarithm is used. Set |
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.
a function taking as input x (the time) and outputting the value of the Baranyi equation.
f <- .baranyi.formula(0.1, 2, 0.2, 5) f(4) ## [1] 0.3498583 f(20) ## [1] 2.344923f <- .baranyi.formula(0.1, 2, 0.2, 5) f(4) ## [1] 0.3498583 f(20) ## [1] 2.344923
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
.checkMicrobialGrowthArgs( x, y, clip, start, lower, upper, nls.args, callbackError ).checkMicrobialGrowthArgs( x, y, clip, start, lower, upper, nls.args, callbackError )
x |
index series or time series. |
y |
values or list of values to regress (should not be logged). |
clip |
a pair of values indicating in which interval to clip the data |
start |
a named list of starting estimates. When |
lower |
a named list of lower bounds. When |
upper |
a named list of upper bounds. When |
nls.args |
additional parameters to use when calling nls. |
callbackError |
function to call on error during regression. |
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.
the modified clip value and raises an error if something is wrong.
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.
.checkMicrobialGrowthCreateArgs(N0, Nmax, mu, lambda, xlim, n).checkMicrobialGrowthCreateArgs(N0, Nmax, mu, lambda, xlim, n)
N0 |
initial population. |
Nmax |
final/maximum population. |
mu |
growth rate. |
lambda |
latency time. |
xlim |
range of values to simulate |
n |
number of points to simulate in the interval |
raise an error if something is wrong.
Function to check the integrity of a given model. Used only for development.
.checkModelIntegrity(model, verbose = TRUE).checkModelIntegrity(model, verbose = TRUE)
model |
the model to check. |
verbose |
boolean indicating if the function is verbose, i.e. it indicates the different steps that it validates. If |
No return value, called to check the integrity of a (new) model for the package (raises an error if the model is invalid).
# Auto-run on package build models = listAvailableModels() for (model in models) { .checkModelIntegrity(model) }# Auto-run on package build models = listAvailableModels() for (model in models) { .checkModelIntegrity(model) }
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.
.getDefaultNlsValues(x, y, start = list(), lower = list(), upper = list()).getDefaultNlsValues(x, y, start = list(), lower = list(), upper = list())
x |
index series or time series. |
y |
values or list of values to regress (should not be logged, must be strictly greater than zero). |
start |
a named list of starting estimates. The coefficients specified in this list will not be calculated. |
lower |
a named list of lower bounds. The coefficients specified in this list will not be calculated. |
upper |
a named list of upper bounds. The coefficients specified in this list will not be calculated. |
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.
the default values of start, lower and upper for NLS regression.
# 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]# 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.
.gompertz.formula(N0, Nmax, mu, lambda, base = exp(1)).gompertz.formula(N0, Nmax, mu, lambda, base = exp(1))
N0 |
initial population. |
Nmax |
final/maximum population. |
mu |
growth rate. |
lambda |
latency time. |
base |
the logarithm base used for plot y-scaling. By default, the natural logarithm is used. Set |
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.
a function taking as input x (the time) and outputting the value of the Gompertz equation.
f <- .gompertz.formula(0.1, 2, 0.2, 5) f(4) ## [1] 0.1150952 f(20) ## [1] 2.505549f <- .gompertz.formula(0.1, 2, 0.2, 5) f(4) ## [1] 0.1150952 f(20) ## [1] 2.505549
Linear equation.
.linear.formula(N0, Nmax, mu, lambda, base = NULL).linear.formula(N0, Nmax, mu, lambda, base = NULL)
N0 |
initial radius. |
Nmax |
final/maximum radius. |
mu |
growth rate. |
lambda |
latency time. |
base |
decimal base used for plot y-scaling. |
a function taking as input x (the time) and outputting the value of the linear equation.
f <- .linear.formula(0.1, 2, 0.2, 5) f(4) ## [1] 0 f(20) ## [1] 3f <- .linear.formula(0.1, 2, 0.2, 5) f(4) ## [1] 0 f(20) ## [1] 3
Regression function for Baranyi's model
.MicrobialGrowth.baranyi( x, y, clip = c(-Inf, Inf), start = list(), lower = list(), upper = list(), nls.args = list(), callbackError = NULL ).MicrobialGrowth.baranyi( x, y, clip = c(-Inf, Inf), start = list(), lower = list(), upper = list(), nls.args = list(), callbackError = NULL )
x |
index series or time series. |
y |
values or list of values to regress (should not be logged). |
clip |
a pair of values indicating in which interval to clip the data |
start |
a named list of starting estimates. When |
lower |
a named list of lower bounds. When |
upper |
a named list of upper bounds. When |
nls.args |
additional parameters to use when calling nls. |
callbackError |
function to call on error during regression. |
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.
a MicrobialGrowth-object composed of
call |
the matched call with several components. |
coefficients |
coefficients obtained by regression. |
data |
data used for regression, once the y values are clipped |
f |
a list of functions such as |
isValid |
a boolean indicating whether the regression was successful or not. |
message |
contains the error message if the regression fails, |
reg |
the |
MicrobialGrowth, .baranyi.formula
Regression function for Gompertz's model
.MicrobialGrowth.gompertz( x, y, clip = c(-Inf, Inf), start = list(), lower = list(), upper = list(), nls.args = list(), callbackError = NULL ).MicrobialGrowth.gompertz( x, y, clip = c(-Inf, Inf), start = list(), lower = list(), upper = list(), nls.args = list(), callbackError = NULL )
x |
index series or time series. |
y |
values or list of values to regress (should not be logged). |
clip |
a pair of values indicating in which interval to clip the data |
start |
a named list of starting estimates. When |
lower |
a named list of lower bounds. When |
upper |
a named list of upper bounds. When |
nls.args |
additional parameters to use when calling nls. |
callbackError |
function to call on error during regression. |
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.
a MicrobialGrowth-object composed of
call |
the matched call with several components. |
coefficients |
coefficients obtained by regression. |
data |
data used for regression, once the y values are clipped |
f |
a list of functions such as |
isValid |
a boolean indicating whether the regression was successful or not. |
message |
contains the error message if the regression fails, |
reg |
the |
MicrobialGrowth, .gompertz.formula
Regression function for Linear's model
.MicrobialGrowth.linear( x, y, clip = c(-Inf, Inf), start = list(), lower = list(), upper = list(), nls.args = list(), callbackError = NULL ).MicrobialGrowth.linear( x, y, clip = c(-Inf, Inf), start = list(), lower = list(), upper = list(), nls.args = list(), callbackError = NULL )
x |
index series or time series. |
y |
values or list of values to regress (should not be logged). |
clip |
a pair of values indicating in which interval to clip the data |
start |
a named list of starting estimates. When |
lower |
a named list of lower bounds. When |
upper |
a named list of upper bounds. When |
nls.args |
additional parameters to use when calling nls. |
callbackError |
function to call on error during regression. |
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.
a MicrobialGrowth-object composed of
call |
the matched call with several components. |
coefficients |
coefficients obtained by regression. |
data |
data used for regression, once the y values are clipped |
f |
a list of functions such as |
isValid |
a boolean indicating whether the regression was successful or not. |
message |
contains the error message if the regression fails, |
reg |
the |
MicrobialGrowth, .linear.formula
Regression function for Rosso's model
.MicrobialGrowth.rosso( x, y, clip = c(-Inf, Inf), start = list(), lower = list(), upper = list(), nls.args = list(), callbackError = NULL ).MicrobialGrowth.rosso( x, y, clip = c(-Inf, Inf), start = list(), lower = list(), upper = list(), nls.args = list(), callbackError = NULL )
x |
index series or time series. |
y |
values or list of values to regress (should not be logged). |
clip |
a pair of values indicating in which interval to clip the data |
start |
a named list of starting estimates. When |
lower |
a named list of lower bounds. When |
upper |
a named list of upper bounds. When |
nls.args |
additional parameters to use when calling nls. |
callbackError |
function to call on error during regression. |
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.
a MicrobialGrowth-object composed of
call |
the matched call with several components. |
coefficients |
coefficients obtained by regression. |
data |
data used for regression, once the y values are clipped |
f |
a list of functions such as |
isValid |
a boolean indicating whether the regression was successful or not. |
message |
contains the error message if the regression fails, |
reg |
the |
MicrobialGrowth, .rosso.formula
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.
.new.baranyi.core(...).new.baranyi.core(...)
... |
further arguments passed to or from other methods. |
the three dots ... are passed to the .new.MicrobialGrowth.core function.
a Baranyi object skeleton.
# 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.# 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.
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.
.new.gompertz.core(...).new.gompertz.core(...)
... |
further arguments passed to or from other methods. |
the three dots ... are passed to the .new.MicrobialGrowth.core function.
a Gompertz object skeleton.
# 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.# 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.
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.
.new.linear.core(...).new.linear.core(...)
... |
further arguments passed to or from other methods. |
the three dots ... are passed to the .new.MicrobialGrowth.core function.
a linear object skeleton.
# 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.# 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.
Provide the skeleton of the MicrobialGrowth object. Must be completed for each model.
.new.MicrobialGrowth.core(...).new.MicrobialGrowth.core(...)
... |
further arguments passed to or from other methods. |
the three dots ... are passed to new.env function.
a MicrobialGrowth object skeleton.
# 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.# 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.
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.
.new.rosso.core(...).new.rosso.core(...)
... |
further arguments passed to or from other methods. |
the three dots ... are passed to the .new.MicrobialGrowth.core function.
a Rosso object skeleton.
# 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.# 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.
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.
.parseMicrobialGrowthCreateArgs(x).parseMicrobialGrowthCreateArgs(x)
x |
value(s) for a given coefficient. |
the 3 ordered values for the given coefficient.
.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.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.
.rosso.formula(N0, Nmax, mu, lambda, base = exp(1)).rosso.formula(N0, Nmax, mu, lambda, base = exp(1))
N0 |
initial population. |
Nmax |
final/maximum population. |
mu |
growth rate. |
lambda |
latency time. |
base |
the logarithm base used for plot y-scaling. By default, the natural logarithm is used. Set |
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.
a function taking as input x (the time) and outputting the value of the Rosso equation.
f <- .rosso.formula(0.1, 2, 0.2, 5) f(4) ## [1] 0 f(20) ## [1] 2.32998f <- .rosso.formula(0.1, 2, 0.2, 5) f(4) ## [1] 0 f(20) ## [1] 2.32998
Modeling of an acid with alpha its sensitivity and MIC its minimum inhibition concentration.
The default concentration is 1g/L.
Acid(alpha, MIC, concentration = 1)Acid(alpha, MIC, concentration = 1)
alpha |
sensitivity. |
MIC |
concentration minimale d'inhibition. |
concentration |
acid concentration (in g/L). |
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.
the acid modeled with the following accessible attributes:
alpha |
the |
MIC |
the |
concentration |
the acid concentration (in g/L). |
getCoefMin |
function returning the minimum coefficient to apply to a MicrobialGrowth-object (see details section). |
getCoefMid |
function returning the "middle" coefficient to apply to a MicrobialGrowth-object (see details section). |
getCoefMax |
function returning the maximum coefficient to apply to a MicrobialGrowth-object (see details section). |
# 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) })# 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) })
alpha, MIC)Modeling of an acid with alpha its sensitivity and MIC its minimum inhibition concentration.
The default concentration is 1g/L.
Acid.SpecificPair(pairs, concentration = 1)Acid.SpecificPair(pairs, concentration = 1)
pairs |
list of pairs ( |
concentration |
acid concentration (in g/L). |
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.
the acid modeled with the following accessible attributes:
pairs |
list of pairs ( |
concentration |
the acid concentration (in g/L). |
getCoefMin |
function returning the minimum coefficient to apply to a MicrobialGrowth-object (see details section). |
getCoefMid |
function returning the "middle" coefficient to apply to a MicrobialGrowth-object (see details section). |
getCoefMax |
function returning the maximum coefficient to apply to a MicrobialGrowth-object (see details section). |
# 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) })# 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) })
Baranyi-object creator from the 4 biological meaning parameters.
baranyi.create(N0, Nmax, mu, lambda, xlim, n = 101)baranyi.create(N0, Nmax, mu, lambda, xlim, n = 101)
N0 |
initial population. |
Nmax |
final/maximum population. |
mu |
growth rate. |
lambda |
latency time. |
xlim |
range of values to simulate |
n |
number of points to simulate in the interval |
a Baranyi-object composed of
call |
the matched call with several components. |
coefficients |
coefficients obtained by regression. |
data |
data used for regression, once the y values are clipped |
f |
a list of functions such as |
isValid |
a boolean indicating whether the regression was successful or not. |
message |
always with this method. |
reg |
always with this method. |
TODO : Describe them (origine, type, etc.)
Clarisse Breard [email protected]
Returns the name of the creation function associated with the model.
getCreateFunctionName(model)getCreateFunctionName(model)
model |
the model name. |
the string corresponding to the creation function of the model. Warning, this function does not check the existence of the corresponding function.
getCreateFunctionName("gompertz") ## [1] "gompertz.create" # Note that this does not verify the existence getCreateFunctionName("NonExistentFunction") ## [1] "NonExistentFunction.create"getCreateFunctionName("gompertz") ## [1] "gompertz.create" # Note that this does not verify the existence getCreateFunctionName("NonExistentFunction") ## [1] "NonExistentFunction.create"
Returns the formula associated with the specified model.
getFormula(model)getFormula(model)
model |
the model name. |
the function corresponding to the formula of the model.
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] 0.1150952f <- 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
Returns the name of the regression function associated with the model.
getFunctionName(model)getFunctionName(model)
model |
the model name. |
the string corresponding to the regression function of the model. Warning, this function does not check the existence of the corresponding function.
getFunctionName("gompertz") ## [1] ".MicrobialGrowth.gompertz" # Note that this does not verify the existence getFunctionName("NonExistentFunction") ## [1] ".MicrobialGrowth.NonExistentFunction"getFunctionName("gompertz") ## [1] ".MicrobialGrowth.gompertz" # Note that this does not verify the existence getFunctionName("NonExistentFunction") ## [1] ".MicrobialGrowth.NonExistentFunction"
Returns the name of the model used.
getModelName(x)getModelName(x)
x |
a MicrobialGrowth-object |
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.
the name of the model used.
g <- MicrobialGrowth(example_data$time, example_data$y1, model="gompertz") getModelName(g) ## [1] "gompertz"g <- MicrobialGrowth(example_data$time, example_data$y1, model="gompertz") getModelName(g) ## [1] "gompertz"
Gompertz-object creator from the 4 biological meaning parameters.
gompertz.create(N0, Nmax, mu, lambda, xlim, n = 101)gompertz.create(N0, Nmax, mu, lambda, xlim, n = 101)
N0 |
initial population. |
Nmax |
final/maximum population. |
mu |
growth rate. |
lambda |
latency time. |
xlim |
range of values to simulate |
n |
number of points to simulate in the interval |
a Gompertz-object composed of
call |
the matched call with several components. |
coefficients |
coefficients obtained by regression. |
data |
data used for regression, once the y values are clipped |
f |
a list of functions such as |
isValid |
a boolean indicating whether the regression was successful or not. |
message |
always with this method. |
reg |
always with this method. |
Graphical Example of Modified Gompertz Equation.
gompertz.explain( N0 = 0.14, Nmax = 1.43, mu = 0.07, lambda = 40, xlim = c(0, 100) )gompertz.explain( N0 = 0.14, Nmax = 1.43, mu = 0.07, lambda = 40, xlim = c(0, 100) )
N0 |
initial population. |
Nmax |
final/maximum population. |
mu |
growth rate. |
lambda |
latency time. |
xlim |
range of values to simulate |
No return value, called to plot a MicrobialGrowth object with the Gompertz model to illustrate the different coefficients.
gompertz.explain() gompertz.explain(0.15, 2, 0.1, 40, c(0,100))gompertz.explain() gompertz.explain(0.15, 2, 0.1, 40, c(0,100))
Test if a variable is an Acid-object.
is.acid(x)is.acid(x)
x |
variable to test. |
TRUE if the object is of class acid.
# 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)) )# 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)) )
Test if a variable is an Acid.SpecificPair-object.
is.acid.specific.pair(x)is.acid.specific.pair(x)
x |
variable to test. |
TRUE if the object is of class acid.specific.pair.
# 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)) )# 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)) )
Test if a variable or variable list is/are baranyi-object(s).
is.baranyi(x)is.baranyi(x)
x |
variable or list. |
TRUE if the object or all objects are of class baranyi.
# 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))# 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))
Test if a variable or variable list is/are gompertz-object(s).
is.gompertz(x)is.gompertz(x)
x |
variable or list. |
TRUE if the object or all objects are of class gompertz.
# 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))# 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))
Test if a variable or variable list is/are linear-object(s).
is.linear(x)is.linear(x)
x |
variable or list. |
TRUE if the object or all objects are of class linear.
# 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))# 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))
Test if a variable or variable list is/are MicrobialGrowth-object(s).
is.MicrobialGrowth(x)is.MicrobialGrowth(x)
x |
variable or list. |
TRUE if the object or all objects are of class MicrobialGrowth.
# 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))# 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))
Test if a variable or variable list is/are rosso-object(s).
is.rosso(x)is.rosso(x)
x |
variable or list. |
TRUE if the object or all objects are of class rosso.
# 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))# 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-object creator from the 4 biological meaning parameters.
linear.create(N0, Nmax, mu, lambda, xlim, n = 101)linear.create(N0, Nmax, mu, lambda, xlim, n = 101)
N0 |
initial radius. |
Nmax |
final/maximum radius. |
mu |
growth rate. |
lambda |
latency time. |
xlim |
range of values to simulate |
n |
number of points to simulate in the interval |
a Linear-object composed of
call |
the matched call with several components. |
coefficients |
coefficients obtained by regression. |
data |
data used for regression, once the y values are clipped |
f |
a list of functions such as |
isValid |
a boolean indicating whether the regression was successful or not. |
message |
always with this method. |
reg |
always with this method. |
List available models.
listAvailableModels()listAvailableModels()
lists the models by scanning the available ".MicrobialGrowth.m" regression functions, with "m" the name of the model.
the list of available models.
listAvailableModels() ## [1] "baranyi" "gompertz" "rosso"listAvailableModels() ## [1] "baranyi" "gompertz" "rosso"
Regression function to different microbial growth models.
MicrobialGrowth( x, y, model = "gompertz", clip = c(-Inf, Inf), start = list(), lower = list(), upper = list(), nls.args = list(), callbackError = NULL, ... )MicrobialGrowth( x, y, model = "gompertz", clip = c(-Inf, Inf), start = list(), lower = list(), upper = list(), nls.args = list(), callbackError = NULL, ... )
x |
index series or time series. |
y |
values or list of values to regress (should not be logged). |
model |
wanted growth model : "baranyi", "gompertz" or "rosso". |
clip |
a pair of values indicating in which interval to clip the data |
start |
a named list of starting estimates. When |
lower |
a named list of lower bounds. When |
upper |
a named list of upper bounds. When |
nls.args |
additional parameters to use when calling nls. |
callbackError |
function to call on error during regression. |
... |
further arguments passed to or from other methods. |
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.
a MicrobialGrowth-object composed of
call |
the matched call with several components. |
coefficients |
coefficients obtained by regression. |
data |
data used for regression, once the y values are clipped |
f |
a list of functions such as |
isValid |
a boolean indicating whether the regression was successful or not. |
message |
contains the error message if the regression fails, |
reg |
the |
# 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) )# 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-object creator from the 4 biological meaning parameters.
MicrobialGrowth.create( N0, Nmax, mu, lambda, xlim, model = "gompertz", n = 101, ... )MicrobialGrowth.create( N0, Nmax, mu, lambda, xlim, model = "gompertz", n = 101, ... )
N0 |
initial population. |
Nmax |
final/maximum population. |
mu |
growth rate. |
lambda |
latency time. |
xlim |
range of values to simulate |
model |
wanted growth model: "baranyi", "gompertz" or "rosso" |
n |
number of points to simulate in the interval |
... |
further arguments passed to or from other methods. |
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).
a MicrobialGrowth-object composed of
call |
the matched call with several components. |
coefficients |
coefficients obtained by regression. |
data |
data used for regression, once the y values are clipped |
f |
a list of functions such as |
isValid |
a boolean indicating whether the regression was successful or not. |
message |
always with this method. |
reg |
always with this method. |
# 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$x), model="gompertz") # Example with confidence intervals g <- MicrobialGrowth.create(N0 = c(0.13, 0.15), Nmax = 1.43, mu = c(0.05, 0.07, 0.09), lambda = c(45, 49, 43), xlim = c(0, 100), model="gompertz") # Coefficient N0 is 0.14 and the confidence interval is (0.13, 0.15) # Coefficient Nmax is 1.43 and the confidence interval is (1.43, 1.43) # Coefficient mu is 0.07 and the confidence interval is (0.05, 0.09) # Coefficient lambda is 45 and the confidence interval is (43, 49)# Simple example g <- MicrobialGrowth.create(N0 = 0.14, Nmax = 1.43, mu = 0.07, lambda = 45, xlim = c(0, 100), model="gompertz") # Example from a regression (whose values can be stored and then reused later) g <- MicrobialGrowth(example_data$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 for the "Acid" class.
## S3 method for class 'acid' Ops(e1, e2)## S3 method for class 'acid' Ops(e1, e2)
e1 |
acid-object, numeric or MicrobialGrowth-object. |
e2 |
acid-object or numeric. |
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.
a new acid or MicrobialGrowth-object.
# 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}# 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}
Default plot.MicrobialGrowth function can be overwritten with the following function
## S3 method for class 'gompertz' plot(x, ...)## S3 method for class 'gompertz' plot(x, ...)
x |
gompertz-object. |
... |
further arguments passed to or from other methods. |
No return value, called to plot a MicrobialGrowth-object based on the Gompertz model.
Default plot.MicrobialGrowth function can be overwritten with the following function
## S3 method for class 'linear' plot(x, base = NULL, ...)## S3 method for class 'linear' plot(x, base = NULL, ...)
x |
linear-object. |
base |
base used for plot y-scaling. |
... |
further arguments passed to or from other methods. |
No return value, called to plot a MicrobialGrowth-object based on the linear model.
Plot function of MicrobialGrowth-objects.
## 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(), ... )## 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(), ... )
x |
MicrobialGrowth-object. |
main |
main title for the plot. |
xlab |
title for the x axis. |
ylab |
title for the y axis. |
n |
the number of x values at which to evaluate. See details section. |
base |
the logarithm base used for plot y-scaling. By default, the natural logarithm is used. Set |
display.coefficients |
boolean indicating the display or not of the values of coefficients (under the main title). |
display.model |
boolean indicating the model used for regression (on right side). |
display.confint |
boolean indicating the display or not of confidence intervals (in the form of curves and area). |
reg.args |
customization parameters of the curve obtained by regression (see curve for possible parameters). |
title.args |
title customization parameters |
model.args |
model display customization parameters (see mtext for possible parameters). |
coefficients.args |
coefficient display customization parameters (see mtext for possible parameters). |
confint.args |
parameters for customizing the plotting of curves and area, corresponding to the confidence interval (see details section). |
... |
other graphical parameters (see plot). |
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 , where n is the value specified in the base argument.
No return value, called to plot a MicrobialGrowth-object.
# 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"))# 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"))
Print function of Acid-object.
## S3 method for class 'acid' print(x, ...)## S3 method for class 'acid' print(x, ...)
x |
Acid-object. |
... |
further arguments passed to or from other methods. |
No return value, called to print information about a Acid-object.
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}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}
Print function of Acid.SpecificPair-object.
## S3 method for class 'acid.specific.pair' print(x, sep = ",\n ", ...)## S3 method for class 'acid.specific.pair' print(x, sep = ",\n ", ...)
x |
Acid.SpecificPair-object. |
sep |
a character string to separate the different pairs. |
... |
further arguments passed to or from other methods. |
No return value, called to print information about a Acid.SpecificPair-object.
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}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}
Default print.MicrobialGrowth function can be overwritten with the following function
## S3 method for class 'gompertz' print(x, ...)## S3 method for class 'gompertz' print(x, ...)
x |
gompertz-object. |
... |
further arguments passed to or from other methods. |
No return value, called to print information about a MicrobialGrowth-object based on the Gompertz model.
Print function of MicrobialGrowth-objects.
## S3 method for class 'MicrobialGrowth' print(x, ...)## S3 method for class 'MicrobialGrowth' print(x, ...)
x |
MicrobialGrowth-object. |
... |
further arguments passed to or from other methods. |
No return value, called to print information about a MicrobialGrowth-object.
MicrobialGrowth, MicrobialGrowth.create
# 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# 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
Rosso-object creator from the 4 biological meaning parameters.
rosso.create(N0, Nmax, mu, lambda, xlim, n = 101)rosso.create(N0, Nmax, mu, lambda, xlim, n = 101)
N0 |
initial population. |
Nmax |
final/maximum population. |
mu |
growth rate. |
lambda |
latency time. |
xlim |
range of values to simulate |
n |
number of points to simulate in the interval |
a Rosso-object composed of
call |
the matched call with several components. |
coefficients |
coefficients obtained by regression. |
data |
data used for regression, once the y values are clipped |
f |
a list of functions such as |
isValid |
a boolean indicating whether the regression was successful or not. |
message |
always with this method. |
reg |
always with this method. |
Summarizes the regression of an MicrobialGrowth-object.
## S3 method for class 'MicrobialGrowth' summary(object, ...)## S3 method for class 'MicrobialGrowth' summary(object, ...)
object |
MicrobialGrowth-object. |
... |
additional arguments affecting the summary produced. |
Equivalent to summary(MicrobialGrowthObject$reg, ...) to which we add the corresponding model member and the summary.MicrobialGrowth class.
The summary of the successful regression, NULL otherwise.
# Simple example g <- MicrobialGrowth(example_data$time, example_data$y1) summary(g) # Example without summary available g <- MicrobialGrowth(example_data$time, example_data$y15) summary(g) g <- MicrobialGrowth.create(0.14, 1.5, 0.07, 45, c(0,100), model="gompertz") summary(g)# Simple example g <- MicrobialGrowth(example_data$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)
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.
THRESHOLD_FEW_DATATHRESHOLD_FEW_DATA
An object of class numeric of length 1.