Package: dr4pl 2.0.0

dr4pl: Dose Response Data Analysis using the 4 Parameter Logistic (4pl) Model

Models the relationship between dose levels and responses in a pharmacological experiment using the 4 Parameter Logistic model. Traditional packages on dose-response modelling such as 'drc' and 'nplr' often draw errors due to convergence failure especially when data have outliers or non-logistic shapes. This package provides robust estimation methods that are less affected by outliers and other initialization methods that work well for data lacking logistic shapes. We provide the bounds on the parameters of the 4PL model that prevent parameter estimates from diverging or converging to zero and base their justification in a statistical principle. These methods are used as remedies to convergence failure problems. Gadagkar, S. R. and Call, G. B. (2015) <doi:10.1016/j.vascn.2014.08.006> Ritz, C. and Baty, F. and Streibig, J. C. and Gerhard, D. (2015) <doi:10.1371/journal.pone.0146021>.

Authors:Justin T. Landis [aut, cre], Alice Peng [ctb], Hyowon An [aut], Aubrey G. Bailey [aut], Dirk P. Dittmer [aut], James S. Marron [aut]

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dr4pl.pdf |dr4pl.html
dr4pl/json (API)
NEWS

# Install 'dr4pl' in R:
install.packages('dr4pl', repos = c('https://jtlandis.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://bitbucket.org/dittmerlab/dr4pl

Datasets:

On CRAN:

13 exports 1.12 score 32 dependencies 2 mentions 79 scripts 496 downloads

Last updated 2 years agofrom:61ee4c6740. Checks:ERROR: 7. Indexed: yes.

TargetResultDate
Doc / VignettesFAILSep 12 2024
R-4.5-winERRORSep 12 2024
R-4.5-linuxERRORSep 12 2024
R-4.4-winERRORSep 12 2024
R-4.4-macERRORSep 12 2024
R-4.3-winERRORSep 12 2024
R-4.3-macERRORSep 12 2024

Exports:augmentcalculatedr4pldr4pl_thetaFindHillBoundsFindInitialParmsFindLogisticGridsgofICLogToParmMeanResponseOutlierDetectionParmToLog

Dependencies:clicolorspacefansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6rbibutilsRColorBrewerRdpackrlangscalestensortibbleutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Obtain coefficients of a 4PL modelcoef.dr4pl
Fit a 4 parameter logistic (4PL) model to dose-response data.confint.dr4pl
Fitting 4 Parameter Logistic (4PL) models to dose-response data.dr4pl dr4pl.data.frame dr4pl.default dr4pl.formula
Private function to fit the 4PL model to dose-response datadr4plEst
Single High Outlierdrc_error_1
Multiple High Outliers at Different measurementsdrc_error_2
Support Problem and Outliers at a Single Dose Leveldrc_error_3
Support Problemdrc_error_4
FindHillBoundsFindHillBounds
FindInitialParmsFindInitialParms
FindLogisticGridsFindLogisticGrids
Obtain Inhibitory Concentrations (IC) of a dose-response curveIC
Compute an estimated mean response.MeanResponse MeanResponse.dr4pl MeanResponse.dr4pl_log10 MeanResponse.dr4pl_theta MeanResponse.numeric
Detect outliers by the method of Motulsky and Brown (2006).OutlierDetection
Make a plot of a 4PL model curve and dataplot.dr4pl
Print the dr4pl object to screen.print.dr4pl
Print the dr4pl object summary to screen.print.summary.dr4pl
sample_data_1sample_data_1
sample_data_10sample_data_10
sample_data_11sample_data_11
sample_data_12sample_data_12
sample_data_13sample_data_13
sample_data_2sample_data_2
sample_data_3sample_data_3
sample_data_4sample_data_4
sample_data_5sample_data_5
sample_data_6sample_data_6
sample_data_7sample_data_7
sample_data_8sample_data_8
sample_data_9sample_data_9
summarysummary.dr4pl
Obtain the variance-covariance matrix of the parameter estimators of a 4PL model.vcov.dr4pl vcov.dr4pl_param