| Title: | Correct Bias in DNA Methylation Analyses |
| Version: | 0.3.5 |
| Description: | Implementation of the algorithms (with minor modifications) to correct bias in quantitative DNA methylation analyses as described by Moskalev et al. (2011) <doi:10.1093/nar/gkr213>. Publication: Kapsner et al. (2021) <doi:10.1002/ijc.33681>. |
| License: | GPL (≥ 3) |
| URL: | https://github.com/kapsner/rBiasCorrection |
| BugReports: | https://github.com/kapsner/rBiasCorrection/issues |
| Depends: | R (≥ 3.6) |
| Imports: | data.table, future, future.apply, ggplot2, magrittr, minpack.lm, nls2, polynom, stats, utils |
| Suggests: | ggpubr, knitr, lintr, microbenchmark, quarto, testthat (≥ 3.0.1) |
| VignetteBuilder: | quarto |
| Config/testthat/edition: | 3 |
| Config/testthat/parallel: | false |
| Date/Publication: | 2025-04-05 13:50:02 UTC |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2025-04-05 13:19:31 UTC; user |
| Author: | Lorenz A. Kapsner 
[cre, aut, cph],
Evgeny A. Moskalev [aut] |
| Maintainer: | Lorenz A. Kapsner <lorenz.kapsner@gmail.com> |
| Repository: | CRAN |
aggregated_input helper function
Description
Internal function to present aggregated input data on which calculations are performed. This function does only have an effect, if repeated measurements are used for calibration a/o experimental data.
Usage
aggregated_input(
datatable,
description = c("experimental", "calibration"),
vec_cal,
type = NULL
)
Arguments
datatable |
A data.table object that contains either the experimental data or the calibration data. |
description |
A character string, indicating if |
vec_cal |
The vector containing the CpG columns (output of |
type |
A single integer. Type of data to be corrected: either |
Value
A data.table in the long format with aggregated means for each CpG site of each sample and the corresponding standard deviation.
Examples
experimental <- rBiasCorrection::example.data_experimental
calibration <- rBiasCorrection::example.data_calibration
vec_cal <- calibration$vec_cal
experimental_aggregated <- aggregated_input(
datatable = experimental$dat,
description = "experimental",
vec_cal = vec_cal,
type = 1
)
dim(experimental_aggregated)
class(experimental_aggregated)
calibration_aggregated <- aggregated_input(
datatable = calibration$dat,
description = "calibration",
vec_cal = vec_cal
)
dim(calibration_aggregated)
class(calibration_aggregated)
better_model helper function
Description
Internal function to select the better model between hyperbolic regression and cubic regression.
Usage
better_model(
statstable_pre,
statstable_post_hyperbolic = NULL,
statstable_post_cubic = NULL,
selection_method = c("SSE", "RelError")
)
Arguments
statstable_pre |
A data.table object, containing the output of
|
statstable_post_hyperbolic |
A data.table object, containing
the output of |
statstable_post_cubic |
A data.table object, containing the output
of |
selection_method |
A character string. The method used to select the regression algorithm to correct the respective CpG site. This is by default the sum of squared errors ("SSE"). The second option is "RelError", which selects the regression method based on the theoretical relative error after correction. This metric is calculated by correcting the calibration data with both the hyperbolic regression and the cubic regression and using them again as input data to calculate the 'goodness of fit'-metrics. |
Value
The function returns a data.table with 4 columns, the last column
being named 'better_model', which indicates in a binary manner,
if the hyperbolic model (better_model = 0) or the cubic model
(better_model = 1) result in a 'better' SSE or RelError respectively.
Examples
# define list object to save all data
rv <- list()
rv$minmax <- TRUE
rv$selection_method <- "RelError"
rv$sample_locus_name <- "Test"
rv$seed <- 1234
# define logfilename
logfilename <- paste0(tempdir(), "/log.txt")
# import experimental file
exp_type_1 <- rBiasCorrection::example.data_experimental
rv$fileimport_experimental <- exp_type_1$dat
# import calibration file
cal_type_1 <- rBiasCorrection::example.data_calibration
rv$fileimport_calibration <- cal_type_1$dat
rv$vec_cal <- cal_type_1$vec_cal
# perform regression
regression_results <- regression_utility(
rv$fileimport_calibration,
"Testlocus",
locus_id = NULL,
rv = rv,
mode = NULL,
logfilename,
minmax = rv$minmax,
seed = rv$seed
)
# extract regression results
rv$result_list <- regression_results$result_list
# get regression statistics
rv$reg_stats <- statistics_list(
rv$result_list,
minmax = rv$minmax
)
# select the better model based on the sum of squared errrors ("SSE")
rv$choices_list <- better_model(
statstable_pre = rv$reg_stats,
selection_method = "SSE"
)
Correct PCR-Bias in Quantitative DNA Methylation Analyses.
Description
This function implements the algorithms described by Moskalev et. al in their article 'Correction of PCR-bias in quantitative DNA methylation studies by means of cubic polynomial regression', published 2011 in Nucleic acids research, Oxford University Press (doi:10.1093/nar/gkr213).
Usage
biascorrection(
experimental,
calibration,
samplelocusname,
minmax = FALSE,
correct_method = c("best", "hyperbolic", "cubic"),
selection_method = c("SSE", "RelError"),
type = 1L,
csvdir = paste0(tempdir(), "/csvdir/"),
plotdir = paste0(tempdir(), "/plotdir/"),
logfilename = paste0(tempdir(), "/log.txt"),
plot_height = 5,
plot_width = 7.5,
plot_textsize = 16,
seed = 1234,
parallel = TRUE
)
Arguments
experimental |
A character string. Path to the file containing the raw methylation values of the samples under investigation. |
calibration |
A character string. In type 1 data (one locus in many samples, e.g. pyrosequencing data): Path to the file containing the raw methylation values of the calibration samples. In type 2 data (many loci in one sample, e.g. next-generationsequencing data or microarray data): Path to the folder that contains at least 4 calibration files (one file per calibration step). Please refer to the FAQ for more detailed information on the specific file requirements (https://raw.githubusercontent.com/kapsner/PCRBiasCorrection/master/FAQ.md). |
samplelocusname |
A character string. In type 1 data: locus name - name of the gene locus under investigation. In type 2 data: sample name - name of the sample under investigation. |
minmax |
A logical, indicating which equations are used for BiasCorrection (default: FALSE). If TRUE, equations are used that include the respective minima and maxima of the provided data. |
correct_method |
A character string. Method used to correct the PCR- bias of the samples under investigation. One of "best" (default), "hyperbolic" or "cubic". If the method is set to "best" (short: "b"), the algorithm will automatically determine the best fitting type of regression for each CpG site based on selection_method (by default: sum of squared errors, SSE, https://en.wikipedia.org/wiki/Residual_sum_of_squares). If the method is set to "hyperbolic" (short: "h") or "cubic" (short: "c"), the PCR-bias correction of all samples under investigation will be performed with the hyperbolic or the cubic regression respectively. |
selection_method |
A character string. The method used to select the regression algorithm to correct the respective CpG site. This is by default the sum of squared errors ("SSE"). The second option is "RelError", which selects the regression method based on the theoretical relative error after correction. This metric is calculated by correcting the calibration data with both the hyperbolic regression and the cubic regression and using them again as input data to calculate the 'goodness of fit'-metrics. |
type |
A single integer. Type of data to be corrected: either |
csvdir |
A character string. Directory to store the resulting tables. (default = paste0(tempdir(), "/plotdir/")). CAUTION: This directory will be newly created on every call of the function - any preexisting files will be deleted without a warning. |
plotdir |
A character string. Directory to store the resulting plots (default = paste0(tempdir(), "/plotdir/")). CAUTION: This directory will be newly created on every call of the function - any preexisting files will be deleted without a warning. |
logfilename |
A character string. Path to a file to save the log messages (default = paste0(tempdir(), "/log.txt")). |
plot_height |
A integer value. The height (unit: inch) of the resulting plots (default: 5). |
plot_width |
A integer value. The width (unit: inch) of the resulting plots (default: 7.5). |
plot_textsize |
A integer value. The textsize of the resulting plots (default: 16). |
seed |
A integer value. The seed used when solving the unknowns in the hyperbolic regression equation and the cubic regression equation. Important for reproducibility (default: 1234). |
parallel |
A boolean. If TRUE (the default value), initializing
|
Value
This function is a wrapper around all of rBiasCorrection's
included functions. When executing it, it performs the whole workflow of
bias correction and writes resulting csv-files and plots, as well as a
log file to the local file system (the respective directories can be
specified with the function arguments). The return-value is TRUE, if the
correction of PCR measurement biases succeeds. If the correction fails,
an error message is returned.
Examples
data.table::fwrite(
rBiasCorrection::example.data_experimental$dat,
paste0(tempdir(), "/experimental_data.csv")
)
data.table::fwrite(
rBiasCorrection::example.data_calibration$dat,
paste0(tempdir(), "/calibration_data.csv")
)
experimental <- paste0(tempdir(), "/experimental_data.csv")
calibration <- paste0(tempdir(), "/calibration_data.csv")
results <- biascorrection(
experimental = experimental,
calibration = calibration,
samplelocusname = "BRAF",
parallel = FALSE
)
calibration_plot helper function
Description
Internal function to carry out the plotting of the calibrations curves.
Usage
calibration_plot(plotlist, coef_hyper, coef_cubic, plot_textsize, minmax)
Arguments
plotlist |
A ggplot object containing a regression plot without
regression curves (output of |
coef_hyper |
A list containing the regression parameters of the hyperbolic regression equation. |
coef_cubic |
A list containing the regression parameters of the cubic regression equation. |
plot_textsize |
A integer value. The textsize of the resulting plots (default: 16). |
minmax |
A logical, indicating which equations are used for BiasCorrection (default: FALSE). If TRUE, equations are used that include the respective minima and maxima of the provided data. |
Value
The function returns a list containing calibration plots.
clean_dt helper function
Description
Internal function, that checks the formatting of imported files and prepares them to be applicable for the following pcr-bias correction steps.
Usage
clean_dt(
datatable,
description = c("calibration", "experimental"),
type = 1L,
logfilename
)
Arguments
datatable |
A data.table object that contains either the experimental data or the calibration data. |
description |
A character string, indicating if |
type |
A single integer. Type of data to be corrected: either |
logfilename |
A character string. Path to a file to save the log messages (default = paste0(tempdir(), "/log.txt")). |
Value
If a valid file is provided, the function returns a cleaned data.table, suited for BiasCorrection.
Examples
logfilename <- paste0(tempdir(), "/log.txt")
cleaned_experimental <- clean_dt(
datatable = rBiasCorrection::example.data_experimental$dat,
description = "experimental",
type = 1,
logfilename = logfilename
)
dim(cleaned_experimental)
class(cleaned_experimental)
clean_up helper function
Description
Internal function to clean up directories.
Usage
clean_up(plotdir, csvdir)
Arguments
plotdir |
A character string. Path to the folder, where plots are saved. |
csvdir |
A character string. Path to the folder, where resulting tables are saved. |
Value
This function silently cleans up the current session and removes
both, the plotdir- and the csvdir-folders. It furthermore resets
the future-backend to plan = "sequential".
See Also
Examples
plotdir <- paste0(tempdir(), "/plots/")
csvdir <- paste0(tempdir(), "/csv/")
clean_up(plotdir, csvdir)
create_exampleplot helper function
Description
Internal function to create an example plot.
Usage
create_exampleplot(
data,
coef_hyper,
coef_cubic,
plot_height,
plot_width,
plot_textsize,
filename
)
Arguments
data |
A data.table containing the aggregated calibration data. |
coef_hyper |
A list containing the regression parameters of the hyperbolic regression equation. |
coef_cubic |
A list containing the regression parameters of the cubic regression equation. |
plot_height |
A integer value. The height (unit: inch) of the resulting plots (default: 5). |
plot_width |
A integer value. The width (unit: inch) of the resulting plots (default: 7.5). |
plot_textsize |
A integer value. The textsize of the resulting plots (default: 16). |
filename |
A character. The filename, where to store the resulting example plot. |
Value
The function creates an example plot and stores on the local filesystem.
Examples
gdat <- rBiasCorrection::example._plot.df_agg
coef_h <- rBiasCorrection::example._plot_coef_h
coef_c <- rBiasCorrection::example._plot_coef_c
create_exampleplot(
data = gdat,
coef_hyper = coef_h,
coef_cubic = coef_c,
plot_height = 5,
plot_width = 7.5,
plot_textsize = 1,
filename = paste0(tempdir(), "/exampleplot.png")
)
createbarerrorplots helper function
Description
Internal function to create relative-error bar plots.
Usage
createbarerrorplots(
statstable_pre,
statstable_post,
rv,
type,
locus_id = NULL,
plotdir,
logfilename,
mode = NULL,
plot_height = 5,
plot_width = 7.5,
plot_textsize = 16
)
Arguments
statstable_pre |
A data.table object, containing the output of
|
statstable_post |
A data.table object, containing the output of
|
rv |
A list object. A list that contains additional objects needed for the algorithms. |
type |
A single integer. Type of data to be corrected: either |
locus_id |
A character string. Default: NULL. ID of the respective locus (only used in type 2 correction). |
plotdir |
A character string. Path to the folder, where plots are saved. |
logfilename |
A character string. Path to a file to save the log messages (default = paste0(tempdir(), "/log.txt")). |
mode |
A character string. Default: NULL. Used to indicate "corrected" calibration data. |
plot_height |
A integer value. The height (unit: inch) of the resulting plots (default: 5). |
plot_width |
A integer value. The width (unit: inch) of the resulting plots (default: 7.5). |
plot_textsize |
A integer value. The textsize of the resulting plots (default: 16). |
Value
This function creates error bar-plots to visualize the relative error before and after bias correction and writes these plots to the local filesystem.
Examples
# define list object to save all data
rv <- list()
rv$minmax <- TRUE
rv$selection_method <- "RelError"
rv$sample_locus_name <- "Test"
rv$seed <- 1234
# define plotdir
rv$plotdir <- paste0(tempdir(), "/plots/")
dir.create(rv$plotdir)
# define logfilename
logfilename <- paste0(tempdir(), "/log.txt")
# import experimental file
exp_type_1 <- rBiasCorrection::example.data_experimental
rv$fileimport_experimental <- exp_type_1$dat
# import calibration file
cal_type_1 <- rBiasCorrection::example.data_calibration
rv$fileimport_calibration <- cal_type_1$dat
rv$vec_cal <- cal_type_1$vec_cal
# perform regression
regression_results <- regression_utility(
rv$fileimport_calibration,
"Testlocus",
locus_id = NULL,
rv = rv,
mode = NULL,
logfilename,
minmax = rv$minmax,
seed = rv$seed
)
# extract regression results
rv$result_list <- regression_results$result_list
# get regression statistics
rv$reg_stats <- statistics_list(
rv$result_list,
minmax = TRUE
)
# select the better model based on the sum of squared errrors ("SSE")
rv$choices_list <- better_model(
statstable_pre = rv$reg_stats,
selection_method = "SSE"
)
# correct calibration data (to show corrected calibration curves)
solved_eq_h <- solving_equations(datatable = rv$fileimport_calibration,
regmethod = rv$choices_list,
type = 1,
rv = rv,
mode = "corrected",
logfilename = logfilename,
minmax = rv$minmax)
rv$fileimport_cal_corrected_h <- solved_eq_h$results
colnames(rv$fileimport_cal_corrected_h) <- colnames(
rv$fileimport_calibration
)
# calculate new calibration curves from corrected calibration data
regression_results <- regression_utility(
data = rv$fileimport_cal_corrected_h,
samplelocusname = rv$sample_locus_name,
rv = rv,
mode = "corrected",
logfilename = logfilename,
minmax = rv$minmax,
seed = rv$seed
)
rv$result_list_hyperbolic <- regression_results$result_list
# save regression statistics to reactive value
rv$reg_stats_corrected_h <- statistics_list(
resultlist = rv$result_list_hyperbolic,
minmax = rv$minmax
)
createbarerrorplots(
statstable_pre = rv$reg_stats,
statstable_post = rv$reg_stats_corrected_h,
rv = rv,
type = 1,
locus_id = NULL,
plotdir = rv$plotdir,
logfilename = logfilename,
mode = "corrected_h",
plot_height = 5,
plot_width = 7.5,
plot_textsize = 1
)
example._plot.df_agg
Description
A data.table containing the aggregated calibration data for CpG site 1 to create an example plot.
example._plot_coef_c
Description
A list containing exemplary coefficients of the cubic regression equation for CpG site 1 to create an example plot.
example._plot_coef_h
Description
A list containing exemplary coefficients of the hyperbolic regression equation for CpG site 1 to create an example plot.
example.data_calibration
Description
A list containing the calibration data ($dat) and the colnames ($vec_cal) needed by the algorithms to perform the bias correction.
example.data_experimental
Description
A list containing the experimental data ($dat) and the colnames ($vec_cal) needed by the algorithms to perform the bias correction.
get_timestamp helper function
Description
Internal function to get the current timestamp to write it to filenames.
Usage
get_timestamp()
Value
This function takes no argument and returns a formatted timestamp of the current system time, which can be integrated e.g. into a filename.
See Also
Examples
get_timestamp()
handle_text_input helper function
Description
Internal function to remove punctuation and unneeded stuff from user inputs with regular expressions.
Usage
handle_text_input(textinput)
Arguments
textinput |
A character string with the textinput to perform these predefined regular expressions on. |
Value
This function returns a cleaned up character string, limited to a maximum of 15 chars.
Examples
textinput <- "This is a dirty! text."
handle_text_input(textinput)
on_start helper function
Description
Internal function, that initializes plotdir, csvdir and logfilename.
Usage
on_start(plotdir, csvdir, logfilename, parallel)
Arguments
plotdir |
A character string. Path to the folder, where plots are saved. |
csvdir |
A character string. Path to the folder, where resulting tables are saved. |
logfilename |
A character string. Path to a file to save the log messages (default = paste0(tempdir(), "/log.txt")). |
parallel |
A boolean. If TRUE (the default value), initializing
|
Value
This function silently creates the directoriesplotdir and csvdir
on the local filesystem and initializes the logfile, specified with
logfilename. Furthermore, if parallel = TRUE, the future-backend is
initialized.
See Also
Examples
# runtime > 5 sec.
plotdir <- paste0(tempdir(), "/plots/")
csvdir <- paste0(tempdir(), "/csv/")
logfilename <- paste0(tempdir(), "/log.txt")
parallel <- FALSE
on_start(plotdir, csvdir, logfilename, parallel)
plotting_utility helper function
Description
Internal function to carry out the plotting of the calibrations curves.
Usage
plotting_utility(
data,
plotlist_reg,
type,
samplelocusname,
locus_id = NULL,
rv,
mode = NULL,
plotdir,
logfilename,
minmax,
plot_height = 5,
plot_width = 7.5,
plot_textsize = 1
)
Arguments
data |
A data.table object that contains the calibration data. |
plotlist_reg |
A list object contating regression plots without
regression curves (output of |
type |
A single integer. Type of data to be corrected: either |
samplelocusname |
A character string. In type 1 data: locus name - name of the gene locus under investigation. In type 2 data: sample name - name of the sample under investigation. |
locus_id |
A character string. Default: NULL. ID of the respective locus (only used in type 2 correction). |
rv |
A list object. A list that contains additional objects needed for the algorithms. |
mode |
A character string. Default: NULL. Used to indicate "corrected" calibration data. |
plotdir |
A character string. Path to the folder, where plots are saved. |
logfilename |
A character string. Path to a file to save the log messages (default = paste0(tempdir(), "/log.txt")). |
minmax |
A logical, indicating which equations are used for BiasCorrection (default: FALSE). If TRUE, equations are used that include the respective minima and maxima of the provided data. |
plot_height |
A integer value. The height (unit: inch) of the resulting plots (default: 5). |
plot_width |
A integer value. The width (unit: inch) of the resulting plots (default: 7.5). |
plot_textsize |
A integer value. The textsize of the resulting plots (default: 16). |
Value
This function creates calibration plots and writes them to the local filesystem.
Examples
# define list object to save all data
rv <- list()
rv$minmax <- TRUE
rv$selection_method <- "RelError"
rv$sample_locus_name <- "Test"
rv$seed <- 1234
# define logfilename
logfilename <- paste0(tempdir(), "/log.txt")
# define plotdir
rv$plotdir <- paste0(tempdir(), "/plots/")
dir.create(rv$plotdir)
# import experimental file
exp_type_1 <- rBiasCorrection::example.data_experimental
rv$fileimport_experimental <- exp_type_1$dat
# import calibration file
cal_type_1 <- rBiasCorrection::example.data_calibration
rv$fileimport_calibration <- cal_type_1$dat
rv$vec_cal <- cal_type_1$vec_cal
# perform regression
regression_results <- regression_utility(
rv$fileimport_calibration,
"Testlocus",
locus_id = NULL,
rv = rv,
mode = NULL,
logfilename,
minmax = rv$minmax,
seed = rv$seed
)
# extract the plotlist
plotlist_reg <- regression_results$plot_list
plotting_utility(
data = rv$fileimport_calibration,
plotlist_reg = plotlist_reg,
type = 1,
samplelocusname = rv$sample_locus_name,
locus_id = NULL,
rv = rv,
mode = NULL,
plotdir = rv$plotdir,
logfilename = logfilename,
minmax = rv$minmax,
plot_height = 5,
plot_width = 7.5,
plot_textsize = 1
)
regression_utility helper function
Description
Internal function to carry out the regression calculations.
Usage
regression_utility(
data,
samplelocusname,
locus_id = NULL,
rv,
mode = NULL,
logfilename,
minmax,
seed = 1234
)
Arguments
data |
A data.table object that contains the calibration data. |
samplelocusname |
A character string. In type 1 data: locus name - name of the gene locus under investigation. In type 2 data: sample name - name of the sample under investigation. |
locus_id |
A character string. Default: NULL. ID of the respective locus (only used in type 2 correction). |
rv |
A list object. A list that contains additional objects needed for the algorithms. |
mode |
A character string. Default: NULL. Used to indicate "corrected" calibration data. |
logfilename |
A character string. Path to a file to save the log messages (default = paste0(tempdir(), "/log.txt")). |
minmax |
A logical, indicating which equations are used for BiasCorrection (default: FALSE). If TRUE, equations are used that include the respective minima and maxima of the provided data. |
seed |
A integer value. The seed used when solving the unknowns in the hyperbolic regression equation and the cubic regression equation. Important for reproducibility (default: 1234). |
Value
The function performs the regression calculations and returns the results in a list.
Examples
# define list object to save all data
rv <- list()
rv$minmax <- TRUE
rv$selection_method <- "RelError"
rv$sample_locus_name <- "Test"
rv$seed <- 1234
# define logfilename
logfilename <- paste0(tempdir(), "/log.txt")
# import experimental file
exp_type_1 <- rBiasCorrection::example.data_experimental
rv$fileimport_experimental <- exp_type_1$dat
# import calibration file
cal_type_1 <- rBiasCorrection::example.data_calibration
rv$fileimport_calibration <- cal_type_1$dat
rv$vec_cal <- cal_type_1$vec_cal
# perform regression
regression_results <- regression_utility(
rv$fileimport_calibration,
"Testlocus",
locus_id = NULL,
rv = rv,
mode = NULL,
logfilename,
minmax = rv$minmax,
seed = rv$seed
)
length(regression_results)
class(regression_results)
solving_equations helper function
Description
Internal function to solve the hyperbolic and cubic regression.
Usage
solving_equations(
datatable,
regmethod,
type,
rv,
mode = NULL,
logfilename,
minmax
)
Arguments
datatable |
A data.table object that contains either the experimental data or the calibration data. |
regmethod |
A data.table object, with 2 columns, containing the names of the samples to correct (columns 1) and a binary variable better_model that indicates, if the data should be corrected with the hyperbolic regression parameters (better_model = 0) or with the cubic regression parameters (better_model = 1). |
type |
A single integer. Type of data to be corrected: either |
rv |
A list object. A list that contains additional objects needed for the algorithms. |
mode |
A character string. Default: NULL. Used to indicate "corrected" calibration data. |
logfilename |
A character string. Path to a file to save the log messages (default = paste0(tempdir(), "/log.txt")). |
minmax |
A logical, indicating which equations are used for BiasCorrection (default: FALSE). If TRUE, equations are used that include the respective minima and maxima of the provided data. |
Value
This function solves the equations of the hyperbolic and the
cubic regression and returns the respectively interpolated values of the
provided datatable.
Examples
# define list object to save all data
rv <- list()
rv$minmax <- TRUE
rv$selection_method <- "RelError"
rv$sample_locus_name <- "Test"
rv$seed <- 1234
# define logfilename
logfilename <- paste0(tempdir(), "/log.txt")
# import experimental file
exp_type_1 <- rBiasCorrection::example.data_experimental
rv$fileimport_experimental <- exp_type_1$dat
# import calibration file
cal_type_1 <- rBiasCorrection::example.data_calibration
rv$fileimport_calibration <- cal_type_1$dat
rv$vec_cal <- cal_type_1$vec_cal
# perform regression
regression_results <- regression_utility(
rv$fileimport_calibration,
"Testlocus",
locus_id = NULL,
rv = rv,
mode = NULL,
logfilename,
minmax = rv$minmax,
seed = rv$seed
)
# extract regression results
rv$result_list <- regression_results$result_list
# get regression statistics
rv$reg_stats <- statistics_list(
rv$result_list,
minmax = TRUE
)
# select the better model based on the sum of squared errrors ("SSE")
rv$choices_list <- better_model(
statstable_pre = rv$reg_stats,
selection_method = "SSE"
)
# correct calibration data (to show corrected calibration curves)
solved_eq_h <- solving_equations(datatable = rv$fileimport_calibration,
regmethod = rv$choices_list,
type = 1,
rv = rv,
mode = "corrected",
logfilename = logfilename,
minmax = rv$minmax)
rv$fileimport_cal_corrected_h <- solved_eq_h$results
colnames(rv$fileimport_cal_corrected_h) <- colnames(
rv$fileimport_calibration
)
statistics_list helper function
Description
Internal function that converts the results_list (output of
regression_utility()) into a data.table object.
Usage
statistics_list(resultlist, minmax = FALSE)
Arguments
resultlist |
A list object. The results_list output of
|
minmax |
A logical, indicating which equations are used for BiasCorrection (default: FALSE). If TRUE, equations are used that include the respective minima and maxima of the provided data. |
Value
The function takes the resultslist and converts it to a
statistics list, which is basically a data.table with the results
of the hyperbolic and the cubic regression.
Examples
# define list object to save all data
rv <- list()
rv$minmax <- TRUE
rv$selection_method <- "RelError"
rv$sample_locus_name <- "Test"
rv$seed <- 1234
# define logfilename
logfilename <- paste0(tempdir(), "/log.txt")
# import experimental file
exp_type_1 <- rBiasCorrection::example.data_experimental
rv$fileimport_experimental <- exp_type_1$dat
# import calibration file
cal_type_1 <- rBiasCorrection::example.data_calibration
rv$fileimport_calibration <- cal_type_1$dat
rv$vec_cal <- cal_type_1$vec_cal
# perform regression
regression_results <- regression_utility(
rv$fileimport_calibration,
"Testlocus",
locus_id = NULL,
rv = rv,
mode = NULL,
logfilename,
minmax = rv$minmax,
seed = rv$seed
)
# extract regression results
rv$result_list <- regression_results$result_list
# get regression statistics
rv$reg_stats <- statistics_list(
rv$result_list,
minmax = rv$minmax
)
substitutions_create helper function
Description
Internal function to initialize a data.table object to store the substitutions.
Usage
substitutions_create()
Value
This function takes no argument and initializes an empty
data.table to hold the substituted values, if substitutions occur during
BiasCorrection.
Examples
substitutions <- substitutions_create()
class(substitutions)
write_csv helper function
Description
Internal function to store the created tables in csv files.
Usage
write_csv(table, filename)
Arguments
table |
A data.table object to store on the local file system |
filename |
The file name (including the path) to store |
Value
This function silently writes a data.table object to a CSV file.
See Also
Examples
table <- data.table::data.table(
a = stats::runif(1000),
b = stats::runif(1000)
)
write_csv(table, paste0(tempdir(), "/example.csv"))
write_log helper function
Description
Internal function to write log-messages to the file specified in logfilename.
Usage
write_log(message, logfilename)
Arguments
message |
A character string containing the log message. |
logfilename |
A character string. Path to a file to save the log messages (default = paste0(tempdir(), "/log.txt")). |
Value
The function prints the logging message to the console and writes it
to the local logfile, specified with logfilename.
Examples
message <- "This is a logmessage"
logfilename <- paste0(tempdir(), "/log.txt")
write_log(message, logfilename)