View source: R/twofold_dilution.R
fit_serial_dilution | R Documentation |
Model fitting by the serial-fold dilution method
fit_serial_dilution(
TTD_data,
start,
dil_factor = 2,
mode = "intercept",
logN_det = NULL,
logN_dil0 = NULL,
max_dil = NULL
)
TTD_data |
a tibble (or data.frame) with the TTD observed for different dilutions.
It must have two columns: |
dil_factor |
dilution factor. By default, 2 |
mode |
one of "intercept" (serial dilution method with a generic intercept; default) or "lambda" (able to estimate also the value of the lag phase duration) |
logN_det |
log10 microbial concentration at the detection OD (only for mode = "lambda") |
logN_dil0 |
log10 microbial concentration at wells where dilution = 0 (only for mode = "lambda") |
max_dil |
maximum number of dilutions to include. By default, |
## We can use the example data set
data("example_od")
## We first need to estimate the TTDs
my_TTDs <- get_TTDs(example_od, target_OD = 0.2, codified = TRUE)
my_data <- filter(ttds, condition == "S/6,5/35/R1")
## Fitting using the "intercept" mode
guess <- c(a = 0, mu = .1) # we need initial guesses for the model parameters
my_fit <- fit_serial_dilution(my_data, start = guess)
## The class returned implements common S3 methods
my_fit
summary(my_fit)
plot(my_fit)
## The fitting can define a maximum number of dilutions
my_fit <- fit_serial_dilution(my_data, start = guess, max_dil = 4)
plot(my_fit)
## Fitting using the "lambda" mode
logNdet <- 7.5 # this mode requires the microbial concentration at the detection OD
logN_dil0 <- 4 # and the concentration at the well with dilution 0
guess <- c(lambda = 0, mu = .1) # the guess must be defined now on lambda instead of a
my_fit2 <- fit_serial_dilution(my_data,
start = guess,
mode = "lambda",
logN_det = logNdet,
logN_dil0 = logN_dil0)
## The instance implements the same S3 methods as before
my_fit2
summary(my_fit2)
plot(my_fit2)
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