The biogrowth package includes several datasets to
aid in the understanding of its functions. They can be loaded with a
call to the function data() passing the name of the dataset
as an argument.
The dataset example_cardinal includes an example of the
type of data used for estimating cardinal model parameters. It has three
columns: temperature, pH and mu. The two first represent the storage
conditions during several static growth experiments, whereas the latter
is the specific growth rate observed in those experiments. This dataset
is intended to be used for fit_secondary_growth().
data("example_cardinal")
head(example_cardinal)
#> temperature pH mu
#> 1 0.000000 5 9.768505e-04
#> 2 5.714286 5 2.624919e-03
#> 3 11.428571 5 0.000000e+00
#> 4 17.142857 5 1.530706e-04
#> 5 22.857143 5 2.301817e-05
#> 6 28.571429 5 3.895598e-04The datasets example_dynamic_growth and
example_env_conditions describe a dynamic growth
experiment, which can be used for the fit_dynamic_growth()
function. The dataset example_env_conditions describes the
experimental design; i.e. how the environmental factors vary during the
experiment. It has three columns: time (the elapsed time), temperature
(the storage temperature) and aw (the water activity).
data("example_env_conditions")
head(example_env_conditions)
#> # A tibble: 3 × 3
#> time temperature aw
#> <dbl> <dbl> <dbl>
#> 1 0 20 0.99
#> 2 5 30 0.95
#> 3 15 35 0.9The dataset example_dynamic_growth illustrates the
population size observed during the experiment described by
example_env_conditions. It has two columns: time (the
elapsed time) and logN (the decimal logarithm of the observed population
size).
data("example_dynamic_growth")
head(example_dynamic_growth)
#> # A tibble: 6 × 2
#> time logN
#> <dbl> <dbl>
#> 1 0 0.0670
#> 2 0.517 -0.00463
#> 3 1.03 -0.0980
#> 4 1.55 -0.0986
#> 5 2.07 0.111
#> 6 2.59 -0.0465The dataset growth_salmonella contains the growth of
Salmonella spp. in broth. It has been retrived from ComBase
(ID: B092_10). It has two columns: time (elapsed time) and logN (the
decimal logarithm of the observed population size).
data("growth_salmonella")
head(growth_salmonella)
#> # A tibble: 6 × 2
#> time logN
#> <dbl> <dbl>
#> 1 0 3.36
#> 2 1.95 3.4
#> 3 2.78 3.44
#> 4 3.78 3.31
#> 5 4.8 3.39
#> 6 5.7 3.65The datasets multiple_counts and
multiple_conditions simulate several growth experiments
performed for the same microorganism under dynamic conditions that vary
between experiments. The observed microbial counts are included in
multiple_counts, which is a list where each element
includes the observations of one experiment with two columns:
time (elapsed time) and logN the logarithm of
the observed population size.
data("multiple_counts")
head(multiple_counts[[1]])
#> time logN
#> 1 0.000000 -0.20801574
#> 2 1.666667 -0.03630986
#> 3 3.333333 -0.29846914
#> 4 5.000000 0.35029686
#> 5 6.666667 0.14326140
#> 6 8.333333 -0.40357904Then, multiple_conditions describes the (dynamic) values
of the environmental conditions during the experiment. In this case, the
experiment considers the effect of temperature and pH. This is reflected
in each entry including a column, time, with the elapsed
time and two additional columns: pH (observed pH) and
temperature (observed temperature. )
data("multiple_conditions")
head(multiple_conditions[[1]])
#> time temperature pH
#> 1 0 20 6.5
#> 2 15 30 7.0
#> 3 40 40 6.5The dataset arabian_tractors includes the number of
agricultural tractors in the Arab World according to the World Bank. It
is included to show the applicability of
fit_isothermal_growth for data from other fields.