Methods
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anova(<drc>)
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ANOVA for dose-response model fits |
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boxcox(<drc>)
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Transform-both-sides Box-Cox transformation |
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bread.drc() estfun.drc()
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Bread and meat for the sandwich |
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coef(<drc>)
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Extract Model Coefficients |
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confint(<drc>)
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Confidence Intervals for model parameters |
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cooks.distance(<drc>) hatvalues(<drc>)
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Model diagnostics for nonlinear dose-response models |
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ED(<drc>)
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Estimating effective doses |
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fitted(<drc>)
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Extract fitted values from model |
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plot(<drc>)
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Plotting fitted dose-response curves |
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predict(<drc>)
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Prediction |
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print(<drc>)
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Printing key features |
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print(<summary.drc>)
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Printing summary of non-linear model fits |
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residuals(<drc>)
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Extracting residuals from the fitted dose-response model |
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summary(<drc>)
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Summarising non-linear model fits |
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vcov(<drc>)
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Calculating variance-covariance matrix for objects of class 'drc' |
Misc
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backfit()
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Calculation of backfit values from a fitted dose-response model |
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CIcomp() CIcompX() plotFACI()
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Calculation of combination index for binary mixtures |
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comped()
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Comparison of effective dose values |
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compParm()
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Comparison of parameters |
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drmc()
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Sets control arguments |
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ED(<drc>)
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Estimating effective doses |
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EDcomp() relpot()
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Comparison of relative potencies between dose-response curves |
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getInitial()
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Showing starting values used |
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getMeanFunctions()
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Display available dose-response models |
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isobole()
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Creating isobolograms |
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lin.test()
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Lack-of-fit test for the mean structure based on cumulated residuals |
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maED()
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Estimation of ED values using model-averaging |
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MAX()
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Maximum mean response |
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mixture()
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Fitting binary mixture models |
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modelFit()
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Assessing the model fit |
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mr.test()
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Mizon-Richard test for dose-response models |
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mselect()
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Dose-response model selection |
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neill.test()
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Neill's lack-of-fit test for dose-response models |
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noEffect()
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Testing if there is a dose effect at all |
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PR()
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Expected or predicted response |
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rdrm()
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Simulating a dose-response curve |
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searchdrc()
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Searching through a range of initial parameter values to obtain convergence |
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simDR()
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Simulating ED values under various scenarios |
Dose-response functions
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AR.2() AR.3()
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Asymptotic regression model |
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baro5()
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The modified baro5 function |
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BC.5() BC.4()
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The Brain-Cousens hormesis models |
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braincousens()
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The Brain-Cousens hormesis models |
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cedergreen() CRS.6() ucedergreen()
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The Cedergreen-Ritz-Streibig model |
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CRS.4a() UCRS.4a()
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The Cedergreen-Ritz-Streibig model |
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CRS.5a() UCRS.5a()
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Cedergreen-Ritz-Streibig dose-reponse model for describing hormesis |
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EXD.2() EXD.3()
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Exponential decay model |
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fplogistic() FPL.4()
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Fractional polynomial-logistic dose-response models |
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gompertz()
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Mean function for the Gompertz dose-response or growth curve |
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gammadr()
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Gamma dose-response model |
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gaussian() lgaussian()
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Normal and log-normal biphasic dose-response models |
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ursa()
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Model function for the universal response surface approach (URSA) for the quantitative assessment of drug interaction |
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gompertzd()
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The derivative of the Gompertz function |
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logistic() L.3() L.4() L.5()
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The logistic model |
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LL.2() l2() LL2.2()
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The two-parameter log-logistic function |
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LL.3() LL.3u() l3() l3u() LL2.3() LL2.3u()
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The three-parameter log-logistic function |
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LL.4() l4() LL2.4()
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The four-parameter log-logistic function |
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LL.5() l5() LL2.5()
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The five-parameter log-logistic function |
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llogistic() llogistic2()
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The log-logistic function |
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lnormal() LN.2() LN.3() LN.3u() LN.4()
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Log-normal dose-response model |
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MM.2() MM.3()
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Michaelis-Menten model |
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multi2()
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Multistage dose-response model with quadratic terms |
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NEC() NEC.2() NEC.3() NEC.4()
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Dose-response model for estimation of no effect concentration (NEC). |
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twophase()
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Two-phase dose-response model |
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W1.2() W2.2()
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The two-parameter Weibull functions |
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W1.3() W2.3() W2x.3() W1.3u() W2.3u()
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The three-parameter Weibull functions |
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W1.4() W2.4()
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The four-parameter Weibull functions |
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weibull1() weibull2() weibull2x()
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Weibull model functions |