Description Usage Arguments Details Value See Also Examples
If the data set is generated, for example by reading extinction rates or relative light units from a plate, these raw values can be converted to concentrations using data fields with known concentrations (calibrators).
1 2 3 4 | set_calc_concentrations(data, cal_names, cal_values, col_names = name,
col_values = value, col_target = conc, col_real = real,
col_recov = recovery, model_func = fit_linear,
interpolate_func = interpolate_linear)
|
data |
A tibble containing the data. |
cal_names |
A vector of strings containing the names of the samples used as calibrators. |
cal_values |
A numeric vector with the known concentrations of those samples (must be in the same order). |
col_names |
The name of the column where the |
col_values |
The name of the column holding the raw values. |
col_target |
The name of the column to created for the calculated concentration. |
col_real |
The name of the column to create for the known concentrations. |
col_recov |
The name of the column to create for the recovery of the calibrators. |
model_func |
A function generating a model to fit the calibrators,
e.g. |
interpolate_func |
A function used to interpolate the concentrations of
the other samples, based on the model, e.g.
|
If the data set contains samples with known concentrations (calibrators) those can be used to interpolate the concentrations of the other samples.
A tibble containing all original and additional columns.
Other set functions: set_calc_variability
,
set_read
, sets_read
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 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | # generate data
library("tibble")
data <- tibble(
name = c("CAL1", "CAL2", "CAL3", "A", "B", "C"),
value = c(1, 5, 10, 2, 4, 6)
)
data
# the known concentration of the calibrators
cals <- c(1, 5, 10)
names(cals) <- c("CAL1", "CAL2", "CAL3")
set_calc_concentrations(
data = data,
cal_names = names(cals),
cal_values = cals
)
# to set column names use notation like in dplyr / tidyverse
# set the name of the column holding the final concentration to "my_protein"
set_calc_concentrations(
data = data,
cal_names = names(cals),
cal_values = cals,
col_target = my_protein
)
## Not run:
# notice that col_target is given a string
# this will fail
set_calc_concentrations(
data = data,
cal_names = names(cals),
cal_values = cals,
col_target = "my_protein"
)
## End(Not run)
# simulate data which has to be transformed to get a good fit
cals <- exp(cals)
data$value <- exp(data$value)
# use ln-transformation on values and known concentrations prior to
# fitting a model
data <- set_calc_concentrations(
data = data,
cal_names = names(cals),
cal_values = cals,
model_func = fit_lnln,
interpolate_func = interpolate_lnln
)
data
# inspect goodnes of fit
plot_lnln(data$real, data$value)
rm(cals, data)
|
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