This function finds the parameters that describe the input data's growth. It does so by fitting the logistic curve to your growth curve measurements.

1 2 | ```
SummarizeGrowth(data_t, data_n, t_trim = 0, bg_correct = "min",
blank = NA)
``` |

`data_t` |
A vector of timepoints (data_n must also be provided and be the same length). |

`data_n` |
A vector of cell counts or absorbance readings. |

`t_trim` |
Measurements taken after this time should not be included in fitting the curve. If stationary phase is variable, this may give you a better fit. A value of 0 means no trimming. Defaults to no trimming (0). |

`bg_correct` |
The background correction method to use. No background correction is performed for the default "none". Specifying "min" subtracts the smallest value in a column from all the rows in that column, and specifying "blank" subtracts the values from the blank vector from the data_n vector. |

`blank` |
A vector of absorbance readings from a blank well (typically contains only media) used for background correction. The corresponding blank value is subtracted from the data_n vector for each timepoint. Defaults to NA. |

The logistic curve equation is

*N_t = N_0 K / (N_0 + (K - N_0) exp(-rt))*

where *N_t* is the number
of cells (or the absorbance reading) at time t, *N_0* is the initial
cell count (or absorbance reading), K is the carrying capacity, and r is the
growth rate.

The fitness proxies returned are the parameters of the logistic equation
and the area under the curve (a measure that integrates the effects
of *N_0*, K, and r). See `gcfit`

for more documentation on these.

An object of type gcfit containing the "fitness" proxies, as well as the input data and the fitted model.

See the accompanying Vignette for an example of how to use and interpret SummarizeGrowth. bit.ly/1p7w6dJ.

See also `gcfit`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ```
# We can check that the parameters that are found are the same
# as we use to generate fake experimental data. To do so, let's first
# generate the "experimental" data using the logistic equation,
# e.g., absorbance readings from a single well in a plate reader over time.
k_in <- 0.5 # the initial carrying capacity
n0_in <- 1e-5 # the initial absorbance reading
r_in <- 1.2 # the initial growth rate
N <- 50 # the number of "measurements" collected during the growth
# curve experiment
data_t <- 0:N * 24 / N # the times the measurements were made (in hours)
data_n <- NAtT(k = k_in, n0 = n0_in, r = r_in, t = data_t) # the measurements
# Now summarize the "experimental" growth data that we just generated
gc <- SummarizeGrowth(data_t, data_n)
# Get the possible metrics for fitness proxies
gc$vals$r # growth rate is a common choice for fitness
gc$vals$t_gen # doubling time, or generation time, is also common
gc$vals$k
gc$vals$n0
gc$vals$auc_l
gc$vals$auc_e
gc$vals$t_mid
# Compare the data with the fit visually by plotting it
plot(gc)
``` |

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