In miLAG: Calculates Microbial Lag Duration (on the Population Level) from Provided Growth Curve Data

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This vignette introduces two functions, get_init_pars_logistic and get_init_pars_baranyi, which help find reasonable initial parameter approximations for Logistic and Baranyi growth models. These initial approximations can be used as starting values for the subsequent optimization algorithm.

The get_init_pars_logistic Function

Introduction

The get_init_pars_logistic function aims to find reasonable initial approximations for parameters of the Logistic growth model, including K (carrying capacity), lag (lag phase duration), and gr_rate (growth rate). These initial values are based on the provided growth curve and some optional initial values.

Usage

The function takes the following parameters:

data_this_curve: Data from a specific growth curve with columns "time" and "biomass."
this_n0: The initial biomass.
init_K: Initial value for the carrying capacity parameter K.
init_lag: Initial value for the lag parameter.
init_gr_rate: Initial value for the growth rate.
min_b: Minimum biomass threshold for the exponential phase (default is 0.2).
min_a: Maximum biomass threshold for the exponential phase (default is 0.8). The function returns a list of initial parameter approximations: init_K, init_lag, and init_gr_rate.

Examples

``` {r eval = FALSE}

Load required libraries

library(dplyr)

Generate example growth curve data

set.seed(123) time <- 1:10 biomass <- c(0.1, 0.3, 0.7, 1.5, 3.0, 5.0, 8.0, 12.0, 18.0, 25.0) gr_curve <- data.frame(time = time, biomass = biomass)

Get initial parameters for the Logistic model

initial_params <- get_init_pars_logistic(gr_curve, this_n0 = 0.1, init_K = 30, init_lag = 0.5, init_gr_rate = 0.5)

Print the initial parameter approximations

initial_params

## The get_init_pars_baranyi Function
### Introduction
The get_init_pars_baranyi function is designed to find reasonable initial approximations for parameters of the Baranyi growth model, including init_mumax (maximum specific growth rate) and lag (lag phase duration). These initial values are based on the provided growth curve and some optional initial values.

### Usage
The function takes the following parameters:

- **data_this_curve**: Data from a specific growth curve with columns "time" and "biomass."
- **this_n0**: The initial biomass.
- **init_lag**: Initial value for the lag parameter.
- **init_gr_rate**: Initial value for the growth rate.
- **min_b**: Minimum biomass threshold for the exponential phase (default is 0.2).
- **min_a**: Maximum biomass threshold for the exponential phase (default is 0.8).
The function returns a list of initial parameter approximations: init_mumax and init_lag.

### Examples
``` {r eval = FALSE}
# Load required libraries
library(dplyr)

# Generate example growth curve data
set.seed(123)
time <- 1:10
biomass <- c(0.1, 0.3, 0.7, 1.5, 3.0, 5.0, 8.0, 12.0, 18.0, 25.0)
gr_curve <- data.frame(time = time, biomass = biomass)

# Get initial parameters for the Baranyi model
initial_params <- get_init_pars_baranyi(gr_curve, this_n0 = 0.1, init_lag = 0.5, init_gr_rate = 0.5)

# Print the initial parameter approximations
initial_params

Conclusion

These functions are useful for obtaining initial parameter approximations for Logistic and Baranyi growth models. You can use these initial values as starting points for optimization algorithms to fit growth models to your data accurately.