solving_equations {rBiasCorrection}

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 "1" (one locus in many samples, e.g. pyrosequencing data) or "2" (many loci in one sample, e.g. next-generation sequencing data or microarray data).

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 the logfile to save the log messages.

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
)