| Type: | Package |
| Version: | 1.0.2 |
| Date: | 2022-11-25 |
| Title: | Benchmark Suite for Indirect Methods for RI Estimation |
| Author: | Tatjana Ammer [aut, cre], Christopher Rank [aut], Andre Schuetzenmeister [aut] |
| Maintainer: | Tatjana Ammer <tatjana.ammer@roche.com> |
| Depends: | R (≥ 3.3.0) |
| Imports: | stats, optparse, digest, data.table, graphics, grDevices, RColorBrewer |
| Suggests: | knitr, rmarkdown |
| Description: | The provided benchmark suite enables the automated evaluation and comparison of any existing and novel indirect method for reference interval ('RI') estimation in a systematic way. Indirect methods take routine measurements of diagnostic tests, containing pathological and non-pathological samples as input and use sophisticated statistical methods to derive a model describing the distribution of the non-pathological samples, which can then be used to derive reference intervals. The benchmark suite contains 5,760 simulated test sets with varying difficulty. To include any indirect method, a custom wrapper function needs to be provided. The package offers functions for generating the test sets, executing the indirect method and evaluating the results. See ?RIbench or vignette("RIbench_package") for a more comprehensive description of the features. A detailed description and application is described in Ammer T., Schuetzenmeister A., Prokosch H.-U., Zierk J., Rank C.M., Rauh M. "RIbench: A Proposed Benchmark for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation". Clinical Chemistry (2022) <doi:10.1093/clinchem/hvac142>. |
| License: | GPL (≥ 3) |
| VignetteBuilder: | knitr, rmarkdown |
| NeedsCompilation: | no |
| Packaged: | 2022-11-25 15:10:44 UTC; ammert |
| Repository: | CRAN |
| Date/Publication: | 2022-11-27 17:40:02 UTC |
RIbench: Benchmark Suite for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation
Description
RIbench enables the automated evaluation and comparison of any existing and novel indirect method in a systematic way.
Indirect methods take routine measurements of diagnostic tests, containing pathological and non-pathological samples
as input and use sophisticated statistical methods to derive a model describing the distribution of the non-pathological
samples, which can then be used to derive reference intervals. The benchmark suite contains 5,760 simulated data sets
with varying difficulty.
To include any indirect method, a custom wrapper function needs to be provided.
The package offers functions for generating the test sets generateBiomarkerTestSets,
executing the indirect method evaluateBiomarkerTestSets and evaluating the results evaluateAlgorithmResults.
Details
| Package: | RIbench |
| Type: | Package |
| Version: | 1.0.2 |
| Date: | 2022-11-25 |
| License: | GPL (>=3) |
| LazyLoad: | yes |
Author(s)
Tatjana Ammer tatjana.ammer@roche.com, Christopher M Rank christopher.rank@roche.com, Andre Schuetzenmeister andre.schuetzenmeister@roche.com
References
Ammer, T., Schuetzenmeister, A., Prokosch, HU., Zierk, J., Rank, C.M., Rauh, M. RIbench: A Proposed Benchmark for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation. Clin Chem (2022) [Accepted, July 12].
One-parameter Box-Cox transformation.
Description
One-parameter Box-Cox transformation.
Usage
BoxCox(x, lambda)
Arguments
x |
(numeric) data to be transformed |
lambda |
(numeric) Box-Cox transformation parameter |
Value
(numeric) vector with Box-Cox transformation of x
Author(s)
Andre Schuetzenmeister andre.schuetzenmeister@roche.com
Add a grid to an existing plot.
Description
It is possible to use automatically determined grid lines (x=NULL, y=NULL) or specifying the number
of cells x = 3, y = 4 as done by grid. Additionally, x- and y-locations of grid-lines can be specified,
e.g. x = 1:10, y = seq(0,10,2).
Usage
addGrid(x = NULL, y = NULL, col = "lightgray", lwd = 1L, lty = 3L)
Arguments
x |
(integer, numeric) single integer specifies number of cells, numeric vector specifies vertical grid-lines |
y |
(integer, numeric) single integer specifies number of cells, numeric vector specifies horizontal grid-lines |
col |
(character) color of grid-lines |
lwd |
(integer) line width of grid-lines |
lty |
(integer) line type of grid-lines |
Author(s)
Andre Schuetzenmeister andre.schuetzenmeister@roche.com
Convert color-names or RGB-code to possibly semi-transparent RGB-code.
Description
Function takes the name of a color and converts it into the rgb space. Parameter "alpha" allows to specify the transparency within [0,1], 0 meaning completey transparent and 1 meaning completey opaque. If an RGB-code is provided and alpha != 1, the RGB-code of the transparency adapted color will be returned.
Usage
as.rgb(col = "black", alpha = 1)
Arguments
col |
(character) name of the color to be converted/transformed into RGB-space (code). Only those colors can be used which are part of the set returned by function colors(). Defaults to "black". |
alpha |
(numeric) value specifying the transparency to be used, 0 = completely transparent, 1 = opaque. |
Value
RGB-code
Author(s)
Andre Schuetzenmeister andre.schuetzenmeister@roche.com
Function to simulate the direct method
Description
Function to simulate the direct method
Usage
computeDirect(
N = 120,
analyte,
params,
seed = 123,
NIter = 10000,
RIperc = c(0.025, 0.975)
)
Arguments
N |
(integer) specifying the number of samples used as sample size for the direct method, default: 120 |
analyte |
(character) specifying the biomarker that is currently simulated |
params |
(list) of parameters for non-pathological distribution (nonp_mu, nonp_sigma, nonp_lambda, and nonp_shift) |
seed |
(integer) specifying the seed used for the simulation, default: 123 |
NIter |
(integer) specifiyng the number of times N samples should be drawn out of the simulated non-pathological distribution (default: 10,000) |
RIperc |
(numeric) value specifying the percentiles, which define the reference interval |
Value
(data frame) with the estimated reference intervals for NIter iterations
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Function for computing performance measurements
Description
Function for computing performance measurements
Usage
computePerfMeas(
analyte,
algo,
resRIs,
subTable,
RIperc = c(0.025, 0.975),
cutoffZ = 5
)
Arguments
analyte |
(character) specifiyng current analyzed analyte |
algo |
(character) specifying used algorithm |
resRIs |
(data.frame) with all calculated reference intervals |
subTable |
(data.frame) containing all information about the simulated test sets |
RIperc |
(numeric) vector specifying the percentiles for the reference interval, default: 0.025 and 0.975 |
cutoffZ |
(numeric) specifying if a cutoff should be used to classify results as implausible and exclude from analysis |
Value
updated data frame with computed performance measures
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Function for computing reference intervals for all markers
Description
Function for computing reference intervals for all markers
Usage
computePerfMeasAll(analytes, algo, risIn, tableTCs, cutoffZ = 5)
Arguments
analytes |
(character) listing all analytes for which the result files should be parsed |
algo |
(character) specifying used algorithm |
risIn |
(list) with data frame of all calculated reference intervals |
tableTCs |
(data.frame) containing all information about the simulated test sets |
cutoffZ |
(integer) specifying if and if so which cutoff should be used to classify results as implausible (default: 5) |
Value
list with the calculated errors as data frame for each marker
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Function for computing reference intervals
Description
Function for computing reference intervals
Usage
computeRIs(
analyte,
algo,
results,
tableTCs,
RIperc = c(0.025, 0.975),
truncNormal = FALSE
)
Arguments
analyte |
(character) specifiyng analyte |
algo |
(character) specifying used algorithm |
results |
(list) with all calculated results as RWDRI objects |
tableTCs |
(data frame) containing all information about the simulated test sets |
RIperc |
(numeric) vector specifying the percentiles for the reference interval, default: 0.025 and 0.975 |
truncNormal |
(logical) specifying if a normal distribution truncated at zero shall be assumed |
Value
data frame with computed reference intervals
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Function for computing reference intervals for all markers
Description
Function for computing reference intervals for all markers
Usage
computeRIsAll(analytes, algo, resIn, tableTCs, truncNormal = FALSE)
Arguments
analytes |
(character) listing all markers for which the result files should be parsed |
algo |
(character) specifying used algorithm |
resIn |
(list) with all calculated results for all markers as RWDRI objects |
tableTCs |
(data.frame) containing all information about the simulated test sets |
truncNormal |
(logical) specifying if a normal distribution truncated at zero shall be assumed |
Value
list with the calculated reference intervals as data frame for each marker
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Function to compute runtime statistics for all analytes
Description
Function to compute runtime statistics for all analytes
Usage
computeRuntimeAll(analytes, algo, risIn, tableTCs)
Arguments
analytes |
(character) listing all analytes for which the result files should be parsed |
algo |
(character) specifying used algorithm |
risIn |
(list) with data frame of all calculated reference intervals and runtime |
tableTCs |
(data.frame) containing all information about the simulated test cases |
Value
(list) wit runtime statistics per analyte and data frames with raw runtime overall and per analyte
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Helper function to compute the subscores for the distribution types and the mentioned categories
Description
Helper function to compute the subscores for the distribution types and the mentioned categories
Usage
computeSubResults(
errorDf,
tableTCs,
distCat,
errorParam,
catList,
catLabels,
perfCombination = "mean"
)
Arguments
errorDf |
(data frame) containing the estimate reference intervals and all computed error measures |
tableTCs |
(data.frame) containing all information about the simulated test sets |
distCat |
(character) specifying the distribution category |
errorParam |
(character) specifiying for which error parameter the data frame should be generated |
catList |
(character) vector containing the categories to split the dataset |
catLabels |
(character) vector containing the labels that will be used for the categories |
perfCombination |
(character) specifying if mean (default), median or sum should be computed |
Value
(data frame) containing the computed subscores
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Function for defining a subset that is used for analyizing the computation time and can be used for other subanalyses.
Description
Function for defining a subset that is used for analyizing the computation time and can be used for other subanalyses.
Usage
defineSubset(tableTCs = NULL, N = 50, seed = 123)
Arguments
tableTCs |
(data frame) describing the pre-defined testcases |
N |
(integer) describing the number of testcases per biomarker contained in the subset (default: 50) |
seed |
(integer) specifying the seed used for defining the subset, default: 123 |
Value
(data frame) describing the updated table with all test case definitions.
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Convenience Function to generate all result plots and calculate the benchmark score
Description
Convenience Function to generate all result plots and calculate the benchmark score
Usage
evaluateAlgorithmResults(
workingDir = "",
algoNames = NULL,
subset = "all",
evalFolder = "Evaluation",
withDirect = TRUE,
withMean = TRUE,
outline = TRUE,
errorParam = c("zzDevAbs_Ov", "AbsPercError_Ov", "AbsError_Ov"),
cutoffZ = 5,
cols = NULL,
...
)
Arguments
workingDir |
(character) specifying the working directory: Plots will be stored in 'workingDir/evalFolder' and results will be used from 'workingDir/Results/algoName/biomarker' |
algoNames |
(character) vector specifying all algorithms that should be part of the evaluation |
subset |
(character, numeric, or data.frame) to specify for which subset the algorithm should be evaluated. character options: 'all' (default) for all test sets, a distribution type: 'normal', 'skewed', 'heavilySkewed', 'shifted'; a biomarker: 'Hb', 'Ca', 'FT4', 'AST', 'LACT', 'GGT', 'TSH', 'IgE', 'CRP', 'LDH'; 'Runtime' for runtime analysis subset; numeric option: number of test sets per biomarker, e.g. 10; data.frame: customized subset of table with test set specifications |
evalFolder |
(character) specifying the name of the ouptut directory, Plots will be stored in workingDir/evalFolder, default: 'Evaluation' |
withDirect |
(logical) indicating whether the direct method should be simulated for comparison (default:TRUE) |
withMean |
(logical) indicating whether the mean should be plotted as well (default: TRUE) |
outline |
(logical) indicating whether outliers should be drawn (TRUE, default), or not (FALSE) |
errorParam |
(character) specifying for which error parameter the data frame should be generated, choose between absolute z-score deviation ("zzDevAbs_Ov"), absolute percentage error ("AbsPercError_Ov"), and absolute error ("AbsError_Ov") |
cutoffZ |
(integer) specifying if and if so which cutoff for the absolute z-score deviation should be used to classify results as implausible and exclude them from the overall benchmark score (default: 5) |
cols |
(character) vector specifying the colors used for the different algorithms |
... |
additional arguments to be passed to the method, e.g. "truncNormal" (logical) vector specifying if a normal distribution truncated at zero shall be assumed, can be either TRUE/FALSE or a vector with TRUE/FALSE for each algorithm; "colDirect" (character) specifying the color used for the direct method, default: "grey" "ylab" (character) specifying the label for the y-axis |
Value
(data frame) containing the computed benchmark results
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Examples
## Not run:
# Ensure that 'generateBiomarkerTestSets()' and 'evaluateBiomarkerTestSets() is called
# with the same workingDir and for all mentioned algorithms before calling this function.
# first example, evaluation for several algorithms
benchmarkScore <- evaluateAlgorithmResults(workingDir=tempdir(),
algoNames=c("Hoffmann", "TML", "kosmic", "TMC", "refineR"))
# The function will create several plots saved in workingDir/Evaluation.
# second example, evaluation for only one algorithm and a defined subset
benchmarkScore <- evaluateAlgorithmResults(workingDir = tempdir(),
algoNames = "refineR", subset = 'Ca')
# third example, saving the results in a different folder, and setting a different cutoff
# for the absolute z-score deviation
benchmarkScore <- evaluateAlgorithmResults(workingDir = tempdir(), algoNames = "refineR",
subset = 'Ca', cutoffZ = 4, evalFolder = "Eval_Test")
## End(Not run)
Wrapper function to evaluate all test sets or a specified subset for a specified algorithm.
Description
Wrapper function to evaluate all test sets or a specified subset for a specified algorithm.
Usage
evaluateBiomarkerTestSets(
workingDir = "",
algoName = "refineR",
algoFunction = "findRI",
libs = "refineR",
sourceFiles = NULL,
params = NULL,
requireDecimals = FALSE,
requirePercentiles = FALSE,
subset = "all",
timeLimit = 14400,
verbose = TRUE,
showWarnings = FALSE,
...
)
Arguments
workingDir |
(character) specifying the working directory: Results will be stored in 'workingDir/Results/algo/biomarker' and data will be used from 'workingDir/Data/biomarker' |
algoName |
(character) specifying the name of the algorithm that is evaluated |
algoFunction |
(character) specifying the name of the function needed for estimating RIs |
libs |
(list) containing all libraries needed for executing the algorithm |
sourceFiles |
(list) containing all source files needed for executing the algorithm |
params |
(list) with additional parameters needed for calling algoFunction |
requireDecimals |
(logical) indicating whether the algorithm needs the number of decimal places (TRUE) or not (FALSE, default) |
requirePercentiles |
(logical) indicating whether only percentiles and no model is estimated |
subset |
(character, numeric, or data.frame) to specify for which subset the algorithm should be executed. character options: 'all' (default) for all test sets; a distribution type: 'normal', 'skewed', 'heavilySkewed', 'shifted'; a biomarker: 'Hb', 'Ca', 'FT4', 'AST', 'LACT', 'GGT', 'TSH', 'IgE', 'CRP', 'LDH'; 'Runtime' for runtime analysis subset; numeric option: number of test sets per biomarker, e.g. 10; data.frame: customized subset of table with test set specifications |
timeLimit |
(integer) specifying the maximum amount of time in seconds allowed to execute one single estimation (default: 14400 sec (4h)) |
verbose |
(logical) indictaing if the progress counter should be shown (default: TRUE) |
showWarnings |
(logical) indicating whether warnings from the call to the indirect method/algorithm should be shown (default: FALSE) |
... |
additional arguments to be passed to the method, e.g. specified in- and output directory ('inputDir', 'outputDir') |
Value
(data frame) containing information about the test sets where the algorithm terminated the R session or failed to report a result
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Examples
## Not run:
# The evaluation of all test sets can take several hours depending on
# the computation time of the algorithm.
# Wrapper function for indirect method required, see vignette("RIbench_package")
# Ensure that 'generateBiomarkerTestSets()' is called with the same workingDir
# before calling this function.
# first generic example
evaluateBiomarkerTestSets(workingDir = tempdir(), algoName = 'myOwnAlgo',
algoFunction = 'estimateModel', libs = c('myOwnAlgo'),
sourceFiles = list("C:\\Temp\\MyAlgoWrapper.R"),
requireDecimals = FALSE, requirePercentiles = FALSE,
subset ='all', timeLimit = 14400)
# second example, evaluation for only 'Calcium' test sets.
progress <- evaluateBiomarkerTestSets(workingDir = tempdir(), algoName = 'myOwnAlgo',
algoFunction = 'estimateModel', libs = c('myOwnAlgo'), subset = "Ca")
# third example, evaluation for only a subset testsets that follow a skewed distribution.
progress <- evaluateBiomarkerTestSets(workingDir = tempdir(), algoName = 'myOwnAlgo',
algoFunction = 'estimateModel', libs = c('myOwnAlgo'), subset = "skewed")
# forth example, evaluation for a subset of 3 testsets per biomarker.
progress <- evaluateBiomarkerTestSets(workingDir = tempdir(), algoName = 'myOwnAlgo',
algoFunction = 'estimateModel', libs = c('myOwnAlgo'), subset = 3)
# fifth example, evaluation for a customized subset with all test sets that have
# a pathological fraction <= 30%.
testsets <- loadTestsetDefinition()
progress <- evaluateBiomarkerTestSets(workingDir = tempdir(), algoName = 'myOwnAlgo',
algoFunction = 'estimateModel', libs = c('myOwnAlgo'),
subset = testsets[testsets$fractionPathol <= 0.3,] )
# sixth example, evaluation forwarding additional parameters to the 'algoFunction'
progress <- evaluateBiomarkerTestSets(workingDir = tempdir(), algoName = 'myOwnAlgo',
algoFunction = 'estimateModel', libs = c('myOwnAlgo'),
sourceFiles = list("Test_RIEst_2pBoxCox"), params = list("model='2pBoxCox'"))
# seventh example, evaluation for indirect method that requires the number of
# decimal points as input
evaluateBiomarkerTestSets(workingDir = tempdir(), algoName = 'myOwnAlgo',
algoFunction = 'estimateModelDec', libs = c('myOwnAlgo'),
sourceFiles = "C:\\Temp\\Test_RIEst_dec.R", requireDecimals = TRUE)
# eigth example, evaluation for indirect method that directly estimates the percentiles
evaluateBiomarkerTestSets(workingDir = tempdir(), algoName="myOwnAlgo",
algoFunction="estimateRIs", libs="myOwnAlgo",
sourceFiles = "C:\\Temp\\Test_RIEst.R", requirePercentiles=TRUE)
## End(Not run)
Rounding method with trailing zeros.
Description
Rounding method with trailing zeros.
Usage
formatNumber(x, digits)
Arguments
x |
(numeric) value that is rounded |
digits |
(integer) indicating the number of decimal places to be used |
Value
Rounded value with trailing zeros
Author(s)
Christopher Rank christopher.rank@roche.com
Convenience function to generate simulated data and save each test set as a separate file
Description
Convenience function to generate simulated data and save each test set as a separate file
Usage
generateBiomarkerTestSets(
workingDir = "",
subset = "all",
rounding = TRUE,
verbose = TRUE
)
Arguments
workingDir |
(character) specifying the working directory where 'workingDir/Data/biomarker' folders will be generated containing the simulated data |
subset |
(character, numeric, or data.frame) to specify for which subset the data should be generated and the algorithms later applied to. character options: 'all' (default) for all test sets; a distribution type: 'normal', 'skewed', 'heavilySkewed', 'shifted'; a biomarker: 'Hb', 'Ca', 'FT4', 'AST', 'LACT', 'GGT', 'TSH', 'IgE', 'CRP', 'LDH'; 'Runtime' for runtime analysis subset; numeric option: number of test sets per biomarker, e.g. 10; data.frame: customized subset of table with test set specification |
rounding |
(logical) indicating whether decimal places stated in test set specification should be applied (default, TRUE), if FALSE, data will be rounded to 5 decimal places to mimic unrounded data |
verbose |
(logical) indicating if the progress counter should be shown (default: TRUE) |
Value
No return value, instead the data files are generated and saved in the workingDir
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Examples
## Not run:
workingDir <- "C:\\Temp\\RIbench\\"
generateBiomarkerTestSets(workingDir = workingDir)
## End(Not run)
# example generating a subset of 2 test sets per biomarker
generateBiomarkerTestSets(workingDir = tempdir(), subset = 2)
Wrapper function to generate one boxplot for a specified analyte
Description
Wrapper function to generate one boxplot for a specified analyte
Usage
generateBoxPlotOneAnalyte(
errorListAll,
colList,
nameList,
catList,
catLabels,
a,
errorParam,
outline = TRUE,
withMean = TRUE,
withCats = TRUE,
withDirect = TRUE,
titlePart = NULL,
outputDir,
filenamePart = NULL,
ylim1 = c(0, 100),
ylim2 = c(100, 1000),
...
)
Arguments
errorListAll |
(list) containing the overall benchmark results per algorithm |
colList |
(character) vector specifying the colors used for the different algorithms (should correspond to columns of benchmark results) |
nameList |
(character) vector specifying the names used in the legend (should correspond to columns of benchmark results), if NULL, colnames will be used |
catList |
(character) vector specifying the categories for which the boxes should be drawn |
catLabels |
(character) vector specifying the labels to the associated categories used for the x-axis |
a |
(character) specifying the analyte for which the boxplot should be generated |
errorParam |
(charcter) specifying for which error measure the plot should be generated |
outline |
(logical) indicating whether outliers should be drawn (TRUE, default), or not (FALSE) |
withMean |
(logical) indicating whether the mean should be plotted as well (default: TRUE) |
withCats |
(logical) set to TRUE if categories (e.g. pathological fraction) should be plotted (default: FALSE) |
withDirect |
(logical) indicating whether the box of the direct method should be elongated to facilitate comparison (default:TRUE) |
titlePart |
(character) specifying the latter part of the title |
outputDir |
(character) specifying a output directory |
filenamePart |
(character) specifying a filename for the plot |
ylim1 |
(numeric) vector specifying the limits in y-direction for the first granular scale |
ylim2 |
(numeric) vector specifying the limits in y-direction for the second less detailed scale |
... |
additional arguments passed forward to other functions |
Value
No return value. Instead, a plot is generated.
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Wrapper function to generate all boxplots for the specified distribution types split by defined categories
Description
Wrapper function to generate all boxplots for the specified distribution types split by defined categories
Usage
generateBoxplotsDistTypes(
errorListAll,
colList,
nameList,
catList,
catLabels,
errorParam = "zzDevAbs_Ov",
outline = TRUE,
withMean = TRUE,
withDirect = TRUE,
withCats = TRUE,
titlePart = NULL,
outputDir = NULL,
filenamePart = NULL,
ylim1Vec = NULL,
ylim2Vec = NULL,
yticks1Vec = NULL,
yticks2Vec = NULL,
...
)
Arguments
errorListAll |
(list) containing the overall benchmark results per algorithm |
colList |
(character) vector specifying the colors used for the different algorithms (should correspond to columns of benchmark results) |
nameList |
(character) vector specifying the names used in the legend (should correspond to columns of benchmark results), if NULL, colnames will be used |
catList |
(character) vector specifying the categories for which the boxes should be drawn |
catLabels |
(character) vector specifying the labels to the associated categories used for the x-axis |
errorParam |
(charcter) specifying for which error measure the plot should be generated |
outline |
(logical) indicating whether outliers should be drawn (TRUE, default), or not (FALSE) |
withMean |
(logical) indicating whether the mean should be plotted as well (default: TRUE) |
withDirect |
(logical) indicating whether the box of the direct method should be elongated to facilitate comparison (default:TRUE) |
withCats |
(logical) set to TRUE if categories (e.g. pathological fraction) should be plotted (default: FALSE) |
titlePart |
(character) specifying the latter part of the title |
outputDir |
(character) specifying a output directory |
filenamePart |
(character) specifying a filename for the plot |
ylim1Vec |
(numeric) vector specifying the limits in y-direction for the first granular scale |
ylim2Vec |
(numeric) vector specifying the limits in y-direction for the second less detailed scale |
yticks1Vec |
(numeric) vector specifying the ticks in y-direction for the first granular scale |
yticks2Vec |
(numeric) vector specifying the ticks in y-direction for the second less detailed scale |
... |
additional arguments passed forward to other functions |
Value
No return value. Instead, a plot is generated.
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Wrapper function to generate all boxplots for the specified analytes split by defined categories
Description
Wrapper function to generate all boxplots for the specified analytes split by defined categories
Usage
generateBoxplotsMultipleCats(
analytes,
errorListAll,
colList,
nameList,
category = c("fractionPathol", "fractionPathol_cum", "N", "N_cum", "OvFreq",
"OvFreq_cum"),
catList = NULL,
catLabels = NULL,
errorParam = "zzDevAbs_Ov",
outline = TRUE,
withMean = TRUE,
withDirect = TRUE,
withCats = TRUE,
titlePart = NULL,
outputDir = NULL,
filenamePart = NULL,
ylim1Vec = NULL,
ylim2Vec = NULL,
yticks1Vec = NULL,
yticks2Vec = NULL,
...
)
Arguments
analytes |
(character) vector specifying for which analytes the plots should be generated |
errorListAll |
(named list) containing the overall benchmark results per algorithm (names of list elements should be the names of the algorithms) |
colList |
(character) vector specifying the colors used for the different algorithms (should correspond to columns of benchmark results) |
nameList |
(character) vector specifying the names used in the legend (should correspond to columns of benchmark results), if NULL, colnames will be used |
category |
(character) defining the category used for creating the subsets. All defined sub-features are used for the categorization. Choose from "fractionPathol" (default), "N", or "OvFreq", individual or cumulative ("_cum"); if category is set this will be used to define catList and catLabels |
catList |
(character) vector specifying the categories for which the boxes should be drawn |
catLabels |
(character) vector specifying the labels to the associated categories used for the x-axis |
errorParam |
(charcter) specifying for which error measure the plot should be generated |
outline |
(logical) indicating whether outliers should be drawn (TRUE, default), or not (FALSE) |
withMean |
(logical) indicating whether the mean should be plotted as well (default: TRUE) |
withDirect |
(logical) indicating whether the box of the direct method should be elongated to facilitate comparison (default:TRUE) |
withCats |
(logical) set to TRUE if categories (e.g. pathological fraction) should be plotted (default: FALSE) |
titlePart |
(character) specifying the latter part of the title |
outputDir |
(character) specifying an output directory |
filenamePart |
(character) specifying a filename for the plot |
ylim1Vec |
(numeric) vector specifying the limits in y-direction for the first granular scale |
ylim2Vec |
(numeric) vector specifying the limits in y-direction for the second less detailed scale |
yticks1Vec |
(numeric) vector specifying the ticks in y-direction for the first granular scale |
yticks2Vec |
(numeric) vector specifying the ticks in y-direction for the second less detailed scale |
... |
additional arguments passed forward to other functions |
Value
No return value. Instead, a plot is generated.
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Generate simulated data with one start seed for each biomarker and save each test set as separate file
Description
Generate simulated data with one start seed for each biomarker and save each test set as separate file
Usage
generateDataFiles(
tableTCs = NULL,
outputDir = NULL,
rounding = TRUE,
verbose = TRUE
)
Arguments
tableTCs |
(data.frame) containing all information about the simulated test cases |
outputDir |
(character) specifying the output directory where the data files should be written to |
rounding |
(logical) indicating whether decimal places stated in tableTCs should be applied (default, TRUE), if FALSE, data will be rounded to 5 decimal places to mimic unrounded data |
verbose |
(logical) indicating if the progress counter should be shown (default: TRUE) |
Value
No return value, instead the data files are generated
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Generate an MD5 hash sum for any R object.
Description
Generate an MD5 hash sum for any R object.
Usage
generateMD5(x)
Arguments
x |
(object) any R object. |
Value
(character) MD5 hash sum of the input object.
Author(s)
Christopher Rank christopher.rank@roche.com
Wrapper function to generate scatterplots for the specified analytes
Description
Wrapper function to generate scatterplots for the specified analytes
Usage
generateScatterplotsAll(
analytes,
errorListAll,
colList = NULL,
nameList,
tableTCs,
errorParam = "zzDevAbs",
withColorCat = NULL,
titlePart = NULL,
outputDir = NULL,
filenamePart = NULL,
ylim = NULL,
xlim = NULL,
xlab = NULL,
ylab = NULL,
...
)
Arguments
analytes |
(character) vector specifying for which analytes the scatterplot should be generated |
errorListAll |
(list) containing the overall benchmark results per algorithm |
colList |
(character) vector specifying the colors used for the different algorithms (should correspond to columns of benchmark results) |
nameList |
(character) vector specifying the names used in the legend (should correspond to columns of benchmark results), if NULL, colnames will be used |
tableTCs |
(data frame) containing all test case information |
errorParam |
(character) specifying for which error measure the plot should be generated |
withColorCat |
(character) indicating if plot should be colored according to the pathological fraction ("fractionPathol"), sample size ("N"), or "overlapPatholLeft", "overlapPatholRight" |
titlePart |
(character) specifying the latter part of the title |
outputDir |
(character) specifying a output directory |
filenamePart |
(character) specifying a filename for the plot |
ylim |
(numeric) vector specifying the limits in y-direction for the first granular scale |
xlim |
(numeric) vector specifying the limits in y-direction for the second less detailed scale |
xlab |
(character) specifying x-axis label |
ylab |
(character) specifying y-axis label |
... |
additional arguments passed forward to other functions |
Value
No return value. Instead, a plot is generated.
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Computing benchmark table with the mean overall results.
Description
Computing benchmark table with the mean overall results.
Usage
getBenchmarkResults(
errorList,
nameVec,
tableTCs,
errorParam = "zzDevAbsCutoff_Ov",
cutoffZ = 5,
catList = c("fractionPathol <= 0.20 & N <= 5000",
"fractionPathol <= 0.20 & N > 5000", "fractionPathol > 0.20 & N <= 5000",
"fractionPathol > 0.20 & N > 5000"),
catLabels = c("lowPlowN", "lowPhighN", "highPlowN", "highPhighN"),
perfCombination = c("mean", "median", "sum")
)
Arguments
errorList |
(list) containing the the computed errors for the different (indirect) methods/algorithms |
nameVec |
(character) vector specifying the names of the different (indirect) methods/algorithms |
tableTCs |
(data.frame) containing all information about the simulated test sets |
errorParam |
(character) specifying for which error parameter the data frame should be generated |
cutoffZ |
(integer) specifying if and if so which cutoff for the absolute z-score deviation should be used to classify results as implausible and exclude them from the overall benchmark score (default: 5) |
catList |
(character) vector containing the categories to split the dataset |
catLabels |
(character) vector containing the labels that will be used for the categories |
perfCombination |
(character) specifying which measure should be used to compute the overall benchmark score; choose from "mean" (default), "median", or "sum" |
Value
(data frame) containing the computed benchmark results
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Method to calculate reference intervals (percentiles) for objects of class 'RWDRI'
Description
Method to calculate reference intervals (percentiles) for objects of class 'RWDRI'
Usage
getRI(
x,
RIperc = c(0.025, 0.975),
CIprop = 0.95,
pointEst = c("fullDataEst", "medianBS", "meanBS"),
truncNormal = FALSE,
Scale = c("original", "transformed")
)
Arguments
x |
(object) of class 'RWDRI' |
RIperc |
(numeric) value specifying the percentiles, which define the reference interval |
CIprop |
(numeric) value specifying the central region for estimation of confidence intervals |
pointEst |
(character) specifying the point estimate determination: (1) using the full dataset ("fullDataEst"), (2) calculating the median from all bootstrap samples ("medianBS"), (2) works only if NBootstrap > 0 (3) calculating the mean from all bootstrap samples ("meanBS"), (3) works only if NBootstrap > 0 |
truncNormal |
(logical) specifying if a normal distribution truncated at zero shall be assumed |
Scale |
(character) specifying if percentiles are calculated on the original scale ("Or") or the transformed scale ("Tr") |
Value
(data.frame) with columns for percentile, point estimate and confidence intervals.
Author(s)
Christopher Rank christopher.rank@roche.com, Tatjana Ammer tatjana.ammer@roche.com
Function for retrieving reference intervals if directly computed
Description
Function for retrieving reference intervals if directly computed
Usage
getRIsAllwithoutModel(analytes, algo, resIn, tableTCs)
Arguments
analytes |
(character) listing all markers for which the result files should be parsed |
algo |
(character) specifying used algorithm |
resIn |
(list) with all calculated results for all markers as RWDRI objects |
tableTCs |
(data.frame) containing all information about the simulated test sets |
Value
list with the calculated reference intervals as data frame for each marker
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Helper function to compute runtime statistics
Description
Helper function to compute runtime statistics
Usage
getRuntime(x, analyte)
Arguments
x |
(data.frame) with one column specifying the Runtime |
analyte |
(character) specifying current analyzed marker |
Value
(data.frame) containing runtime statistics (min, mean, median, max)
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Function to group the data according to a specified feature.
Description
The feature can either be the pathological fraction, the sample size or the overlap (category) individually or cumulative (_cum).
For an individualized categorisation see getSubsetForDefinedCats.
Usage
getSubset(
subsetDef,
distType = FALSE,
tableTCs,
errorList,
category = c("fractionPathol", "fractionPathol_cum", "N", "N_cum", "OvFreq",
"OvFreq_cum"),
restrict = NULL
)
Arguments
subsetDef |
(character) listing either the analytes or distribution types for which the result files should be parsed |
distType |
(logical) indicating if parameter subsetDef refers to analytes (FALSE, default) or distribution types (TRUE) |
tableTCs |
(data.frame) containing all information about the simulated test sets |
errorList |
(list) containing for each method the table with the computed error measurements |
category |
(character) defining the category used for creating the subsets. All defined sub-features are used for the categorization. Choose from "fractionPathol" (default), "N", or "OvFreq", individual or cumulative ("_cum") |
restrict |
(character) indicating whether test sets should be filtered according to specified restriction, default NULL, e.g. fractionPathol <= 0.30 |
Value
(list) containing the performance measurements grouped according to specified subset definition and categories
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Function to group the data according to a specified feature.
Description
Function to group the data according to a specified feature.
Usage
getSubsetForDefinedCats(
subsetDef,
distType = FALSE,
tableTCs,
errorList,
catList = NULL,
catLabels = NULL,
restrict = NULL
)
Arguments
subsetDef |
(character) listing either the analytes or distribution types for which the result files should be parsed |
distType |
(logical) indicating if 'subsetDef' refers to analytes (FALSE, default) or distribution types (TRUE) |
tableTCs |
(data.frame) containing all information about the simulated test sets |
errorList |
(list) containing the table with the computed error measurements |
catList |
(list) containing the categories to split the dataset |
catLabels |
(list) containing the labels that will be used for the categories |
restrict |
(character) indicating whether testcases should be filtered according to specified restriction, default NULL, e.g. fractionPathol <= 0.30 |
Value
(list) containing the performance measurements grouped according to specified subset definition and categories
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Inverse of the one-parameter Box-Cox transformation.
Description
Inverse of the one-parameter Box-Cox transformation.
Usage
invBoxCox(x, lambda)
Arguments
x |
(numeric) data to be transformed |
lambda |
(numeric) Box-Cox transformation parameter |
Value
(numeric) vector with inverse Box-Cox transformation of x
Author(s)
Andre Schuetzenmeister andre.schuetzenmeister@roche.com
Convenience function to load the table with the information about the pre-defined test sets
Description
Convenience function to load the table with the information about the pre-defined test sets
Usage
loadTestsetDefinition()
Value
(data frame) containing the pre-defined parameter combinations to generate the simulations
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Examples
testsets <- loadTestsetDefinition()
str(testsets)
Function to combine analytes for defined categories
Description
The feature can either be the pathological fraction, the sample size or the overlap (category) individually or cumulative (_cum).
For a individualized categorisation see getSubsetForDefinedCats.
Usage
mergeAnalytes(
tableTCs,
errorList,
catList = NULL,
catLabels = NULL,
distTypes = TRUE
)
Arguments
tableTCs |
(data.frame) containing all information about the simulated test sets |
errorList |
(list) containing for each method the table with the computed error measurements |
catList |
(list) containing the categories to split the dataset |
catLabels |
(list) containing the labels that will be used for the categories |
distTypes |
(logical) indicating if 'catList' refers to analytes (FALSE, default) or distribution types (TRUE) |
Value
(list) containing the merged performance measurements grouped according to specified category
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Helper function to combine all computed summary errors
Description
Helper function to combine all computed summary errors
Usage
mergeSummaryErrors(
errorList,
nameVec,
errorParam = "MedianAbsPercErrorOV",
cutoffZ = FALSE
)
Arguments
errorList |
(list) of the error lists for the different methods for which the summary errors should be combined |
nameVec |
(character) vector specifying the names of the methods |
errorParam |
(character) specifying for which error parameter the data frame should be generated |
cutoffZ |
(logical) indicating if a cutoff was set, needed for CRP case |
Value
(data frame) containing the summary errors per analyte per method
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Plot method for generating a barplot out of the benchmark results
Description
Plot method for generating a barplot out of the benchmark results
Usage
plotBarplot(
benchmarkRes,
perDistType = FALSE,
colList,
nameList = NULL,
withLabels = FALSE,
withHorizLines = FALSE,
title = NULL,
xlim = NULL,
xlab = "Mean of Absolute Z-Score Deviations",
outputDir = NULL,
filename = NULL,
...
)
Arguments
benchmarkRes |
(data frame) containing the overall benchmark results |
perDistType |
(logical) indicating if one overall plot should be generated or if it should be separated by the distribution type |
colList |
(character) vector specifying the colors used for the different algorithms (should correspond to columns of benchmark results) |
nameList |
(character) vector specifying the names used in the legend (should correspond to columns of benchmark results), if NULL, colnames will be used |
withLabels |
(logical) indicating whether the corresponding values should be plotted as well (default: FALSE) |
withHorizLines |
(logical) indicating whether horizontal lines should be plotted for a better visual separation of the different categories (default:FALSE) |
title |
(character) specifying plot title |
xlim |
(numeric) vector specifying the limits in x-direction |
xlab |
(character) specifying the x-axis label |
outputDir |
(character) specifying a output directory |
filename |
(character) specifying a filename for the plot |
... |
additional arguments passed forward to other functions |
Value
No return value. Instead, a plot is generated.
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Plot method for generating a boxplot of the benchmark results
Description
Plot method for generating a boxplot of the benchmark results
Usage
plotBoxplot(
errorList,
colList,
nameList,
outline = TRUE,
withMean = TRUE,
withCats = FALSE,
withDirect = TRUE,
title = "",
outputDir = NULL,
filename = NULL,
ylim1 = c(0, 100),
ylim2 = c(100, 1000),
...
)
Arguments
errorList |
containing the overall benchmark results |
colList |
(character) vector specifying the colors used for the different algorithms (should correspond to columns of benchmark results) |
nameList |
(character) vector specifying the names used in the legend (should correspond to columns of benchmark results), if NULL, colnames will be used |
outline |
(logical) indicating whether outliers should be drawn (TRUE, default), or not (FALSE) |
withMean |
(logical) indicating whether the mean should be plotted as well (default: TRUE) |
withCats |
(logical) set to TRUE if categories (e.g. pathological fraction) should be plotted (default: FALSE) |
withDirect |
(logical) indicating whether the box of the direct method should be elongated to facilitate comparison (default:TRUE) |
title |
(character) specifying plot title |
outputDir |
(character) specifying a output directory |
filename |
(character) specifying a filename for the plot |
ylim1 |
(numeric) vector specifying the limits in y-direction for the first granular scale |
ylim2 |
(numeric) vector specifying the limits in y-direction for the second less detailed scale |
... |
additional arguments passed forward to other functions |
Value
No return value. Instead, a plot is generated.
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Plot method for generating a scatterplot
Description
Plot method for generating a scatterplot
Usage
plotScatterplot(
errorList,
colList,
nameList,
withColor = NULL,
cats = NULL,
title = "",
outputDir = NULL,
filename = NULL,
xlim = NULL,
ylim = NULL,
xlab = NULL,
ylab = NULL,
...
)
Arguments
errorList |
(data frame) containing the overall benchmark results |
colList |
(character) vector specifying the colors used for the different algorithms (should correspond to columns of benchmark results) |
nameList |
(character) vector specifying the names used in the legend (should correspond to columns of benchmark results), if NULL, colnames will be used |
withColor |
(character) indicating if plot should be colored according to pathological fraction, sample size or pathological overlap left / right |
cats |
(character) specifying the category labels |
title |
(character) specifying plot title |
outputDir |
(character) specifying a output directory |
filename |
(character) specifying a filename for the plot |
xlim |
(numeric) vector specifying the limits in y-direction for the first granular scale |
ylim |
(numeric) vector specifying the limits in y-direction for the second less detailed scale |
xlab |
(character) specifying x-axis label |
ylab |
(character) specifying y-axis label |
... |
additional arguments passed forward to other functions |
Value
No return value. Instead, a plot is generated.
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Standard print method for objects of class 'RWDRI'
Description
Standard print method for objects of class 'RWDRI'
Usage
## S3 method for class 'RWDRI'
print(
x,
RIperc = c(0.025, 0.975),
CIprop = 0.95,
pointEst = c("fullDataEst", "medianBS", "meanBS"),
truncNormal = FALSE,
...
)
Arguments
x |
(object) of class 'RWDRI' |
RIperc |
(numeric) value specifying the percentiles, which define the reference interval |
CIprop |
(numeric) value specifying the central region for estimation of confidence intervals |
pointEst |
(character) specifying the point estimate determination: (1) using the full dataset ("fullDataEst"), (2) calculating the median from the bootstrap samples ("medianBS"), (2) works only if NBootstrap > 0 (3) calculating the mean from the bootstrap samples ("meanBS"), (3) works only if NBootstrap > 0 |
truncNormal |
(logical) specifying if a normal distribution truncated at zero shall be assumed |
... |
additional arguments passed forward to other functions. |
Value
No return value. Instead, a summary is printed.
Author(s)
Christopher Rank christopher.rank@roche.com
Function for setting up the progress indicator.
Description
Function for setting up the progress indicator.
Usage
progressInd(value, maxValue, nCharMsg = 0)
Arguments
value |
(integer) indicating the current number |
maxValue |
(integer) indicating the maximum number |
nCharMsg |
(integer) indicating the number of characters the message already has |
Value
(character) returing generated progress message
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Function for reading in the result files for one marker
Description
Function for reading in the result files for one marker
Usage
readResultFiles(analyte, algo, path = NULL, tableTCs = NULL)
Arguments
analyte |
(character) specifying analyte |
algo |
(character) specifying used algorithm |
path |
(character) specifying path to Results directories |
tableTCs |
(data frame) containing all information about the simulated test sets |
Value
list with caluclated results as RWDRI objects
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Function for reading all results files.
Description
Function for reading all results files.
Usage
readResultFilesAll(analytes, algo, baseDir = NULL, inputDir = NULL, tableTCs)
Arguments
analytes |
(character) listing all analytes for which the result files should be parsed |
algo |
(character) specifying used algorithm |
baseDir |
(character) specifying the baseDir: Results will be used from baseDir/Results/algo/marker if baseDir is set, inputDir will be ignored; if baseDir is NULL, the current working directory will be used |
inputDir |
(character) specifying path directly to Results directories |
tableTCs |
(data frame) containing all information about the simulated test sets |
Value
list with all caluclated results as RWDRI objects for each marker
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Function to read the result files and compute performance measures to create customized plots afterwards
Description
Function to read the result files and compute performance measures to create customized plots afterwards
Usage
readResultsAndComputeErrors(
workingDir = getwd(),
algoName = NULL,
subset = "all",
cutoffZ = 5,
...
)
Arguments
workingDir |
(character) specifying the working directory: Plots will be stored in workingDir/evalFolder and results will be used from workingDir/Results/algoName/biomarker; |
algoName |
(character) vector specifying one algorithm for which the performance measures should be evaluated |
subset |
(character, numeric, or data.frame) to specify for which subset the algorithm should be executed. character options: 'all' (default) for all test sets, a distribution type: 'normal', 'skewed', 'heavilySkewed', 'shifted'; a biomarker: 'Hb', 'Ca', 'FT4', 'AST', 'LACT', 'GGT', 'TSH', 'IgE', 'CRP', 'LDH'; 'runtime' for runtime analysis subset; numeric option: number of test sets per biomarker, e.g. 10; data.frame: customized subset of table with test set specifications |
cutoffZ |
(integer) specifying if and if so which cutoff for the absolute z-score deviation should be used to classify results as implausible and exclude them from the overall benchmark score (default: 5) |
... |
additional arguments to be passed to the method truncNormal (logical) specifying if a normal distribution truncated at zero shall be assumed |
Value
(list) with (data frame) and a (list) with the computed performance measures
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Function to get error subsets for defined category and restriction.
Description
Function to get error subsets for defined category and restriction.
Usage
restrictSet(overallCat, tableTCs, errorList, distType = TRUE, restrict = NULL)
Arguments
overallCat |
(list) containing the categories to split the dataset |
tableTCs |
(data.frame) containing all information about the simulated test sets |
errorList |
(list) containing for each method the table with the computed error measurements |
distType |
(logical) indicating if 'overallCat' refers to analytes (FALSE, default) or distribution types (TRUE) |
restrict |
(character) indicating whether testcases should be filtered according to specified restriction, default NULL, e.g. fractionPathol <= 0.30 |
Value
(list) containing the merged performance measurements grouped according to specified category
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Convenience function to simulate the direct method for the specified subset
Description
Convenience function to simulate the direct method for the specified subset
Usage
runDirectMethod(tableTCs = NULL, N = 120, cutoffZ = 5)
Arguments
tableTCs |
(data frame) containing the pre-defined parameter combinations to generate the simulations |
N |
(integer) specifying the number of samples used as sample size for the direct method, default: 120 |
cutoffZ |
(numeric) specifying if a cutoff should be used to classify results as implausible and exclude from analysis |
Value
(data frame) with computed performance measures
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Examples
# example to run direct method only for test sets for hemoglobin (Hb)
testsets <- loadTestsetDefinition()
directRes <- runDirectMethod(tableTCs = testsets[testsets$Analyte =="Hb",], N = 120, cutoffZ = 5)
Function for running test sets per algorithm per marker with calling Rscript for each test set
Description
Function for running test sets per algorithm per marker with calling Rscript for each test set
Usage
runTC_usingRscript(
biomarker = NULL,
algoName = "myOwnAlgo",
algoFunction = "estimateModel",
sourceFiles = NULL,
libs = NULL,
params = NULL,
decimals = FALSE,
ris = FALSE,
RIperc = c(0.025, 0.975),
tableTCs = NULL,
outputDir = NULL,
inputDir = NULL,
timeLimit = 14400,
subsetDef = "all",
verbose = TRUE,
showWarnings = FALSE,
...
)
Arguments
biomarker |
(character) specifying the biomarker for which the algorithm should calculate RIs |
algoName |
(character) specifying the name of the algorithm that is evaluated |
algoFunction |
(character) specifying the name of the function needed for estimating RIs |
sourceFiles |
(list) containing all source files needed for executing the algorithm |
libs |
(list) containing all libraries needed for executing the algorithm |
params |
(list) with additional parameters needed for calling algoFunction |
decimals |
(logical) indicating whether the algorithm needs the number of decimal places (TRUE) or not (FALSE, default) |
ris |
(logical) indicating whether only percentiles and no model is estimated |
RIperc |
(numeric) value specifying the percentiles, which define the reference interval |
tableTCs |
(data.frame) with the information about the simulated test sets |
outputDir |
(character) specifying the outputDir: Results will be stored in outputDir/Results/algo/biomarker |
inputDir |
(character) specifying the inputDir: Data files should be stored in inputDir/Data/biomarker |
timeLimit |
(integer) specifying the maximum amount of time in seconds allowed to execute one single estimation (default: 14400 sec (4h)) |
subsetDef |
(character) describing the specified subset of all test sets the algorithm is applied to, used for naming the progress file |
verbose |
(logical) indictaing if the progress counter should be shown (default: TRUE) |
showWarnings |
(logical) indicating whether warnings from the call to the indirect method/algorithm should be shown (default: FALSE) |
... |
additional arguments to be passed to the method |
Value
(data frame) containing information about the test sets where the algorithm terminated the R session or failed to report a result
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Convenience function to set up the directory structure used for storing data and results.
Description
Convenience function to set up the directory structure used for storing data and results.
Usage
setupDirStructure(
outputDir = NULL,
onlyData = FALSE,
onlyResults = FALSE,
tableTCs = NULL,
algoName = NULL
)
Arguments
outputDir |
(character) specifying the base output directory. From here, Data/biomarker and Result/algoName/biomarker directories are generated |
onlyData |
(logical) if set to TRUE, only the biomarker subdirectories are generated, name of output directory is used as it is (default:FALSE) |
onlyResults |
(logical) if set to TRUE, only the algoName/biomarker subdirectories are generated, name of output directory is used as it is (default:FALSE) |
tableTCs |
(data frame) containing the pre-defined parameter combinations to generate the simulations |
algoName |
(character) specifying the name of the algorithm used for creating the subdirectory |
Value
No return value. Instead, the directory structure is set up.
Author(s)
Tatjana Ammer tatjana.ammer@roche.com
Helper function to write result file when time out occured or R session terminated
Description
Helper function to write result file when time out occured or R session terminated
Usage
writeResFile(
algoName,
biomarker,
N = 0,
error = NULL,
runtime = NULL,
filename = NULL,
outputDir = NULL
)
Arguments
algoName |
(character) specifying the name of the algorithm that is evaluated |
biomarker |
(character) specifying the biomarker for which the algorithm should calculate RIs |
N |
(numeric) specifying the number of input data points |
error |
(character) specifying the type of error (e.g. timeout, RSessionTerminated) |
runtime |
(numeric) specifying the computation time up until the error occured |
filename |
(character) specifying the filename for which the algorithm failed |
outputDir |
(character) specifying the outputDir: Data files should be stored in outputDir/Data/biomarker and Results will be stored in outputDir/Results/algo/biomarker |
Author(s)
Tatjana Ammer tatjana.ammer@roche.com