Dose Response Data Analysis using the 4 Parameter Logistic (4pl) Model
Constructor for dr4pl theta parameter
Fitting 4 Parameter Logistic (4PL) models to dose-response data.
Private function to fit the 4PL model to dose-response data
FindHillBounds
FindInitialParms
FindLogisticGrids
Perform the goodness-of-fit (gof) test for the 4PL model.
Perform the goodness-of-fit (gof) test for a model.
Obtain Inhibitory Concentrations (IC) of a dose-response curve
Compute an estimated mean response.
Detect outliers by the method of Motulsky and Brown (2006).
dr4pl-calculate
Obtain coefficients of a 4PL model
Fit a 4 parameter logistic (4PL) model to dose-response data.
Augment data with dr4pl
Make a plot of a 4PL model curve and data
Print the dr4pl object to screen.
Print the dr4pl object summary to screen.
Objects exported from other packages
Compute dr4pl residuals.
summary
Obtain the variance-covariance matrix of the parameter estimators of a...
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>.
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