fit  R Documentation 
Note this new generic function is necessary because the fitted
function only allows the first argument object
to appear in the
signature. But we need also other arguments in the signature.
fit(object, model, data, ...) ## S4 method for signature 'Samples,Model,Data' fit( object, model, data, points = data@doseGrid, quantiles = c(0.025, 0.975), middle = mean, ... ) ## S4 method for signature 'Samples,DualEndpoint,DataDual' fit(object, model, data, quantiles = c(0.025, 0.975), middle = mean, ...) ## S4 method for signature 'Samples,LogisticIndepBeta,Data' fit( object, model, data, points = data@doseGrid, quantiles = c(0.025, 0.975), middle = mean, ... ) ## S4 method for signature 'Samples,Effloglog,DataDual' fit( object, model, data, points = data@doseGrid, quantiles = c(0.025, 0.975), middle = mean, ... ) ## S4 method for signature 'Samples,EffFlexi,DataDual' fit( object, model, data, points = data@doseGrid, quantiles = c(0.025, 0.975), middle = mean, ... )
object 
the 
model 
the 
data 
the 
... 
unused 
points 
at which dose levels is the fit requested? default is the dose grid 
quantiles 
the quantiles to be calculated (default: 0.025 and 0.975) 
middle 
the function for computing the middle point. Default:

the data frame with required information (see method details)
fit(object = Samples, model = Model, data = Data)
: This method returns a data frame with dose, middle, lower
and upper quantiles for the dosetoxicity curve
fit(object = Samples, model = DualEndpoint, data = DataDual)
: This method returns a data frame with dose, and middle,
lower and upper quantiles, for both the dosetox and dosebiomarker (suffix
"Biomarker") curves, for all grid points (Note that currently only the grid
points can be used, because the DualEndpointRW models only allow that)
fit(object = Samples, model = LogisticIndepBeta, data = Data)
: This method return a data frame with dose, middle lower and upper quantiles
for the doseDLE curve using DLE samples for “LogisticIndepBeta” model class
fit(object = Samples, model = Effloglog, data = DataDual)
: This method returns a data frame with dose, middle, lower, upper quantiles for
the doseefficacy curve using efficacy samples for “Effloglog” model class
fit(object = Samples, model = EffFlexi, data = DataDual)
: This method returns a data frame with dose, middle, lower and upper
quantiles for the doseefficacy curve using efficacy samples for “EffFlexi”
model class
# Create some data data < Data(x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10), y = c(0, 0, 0, 0, 0, 0, 1, 0), cohort = c(0, 1, 2, 3, 4, 5, 5, 5), doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by=2))) # Initialize a model model < LogisticLogNormal(mean = c(0.85, 1), cov = matrix(c(1, 0.5, 0.5, 1), nrow = 2), refDose = 56) # Get posterior for all model parameters options < McmcOptions(burnin = 100, step = 2, samples = 2000) set.seed(94) samples < mcmc(data, model, options) # Extract the posterior mean (and empirical 2.5 and 97.5 percentile) # for the prob(DLT) by doses fitted < fit(object = samples, model = model, data = data, quantiles=c(0.025, 0.975), middle=mean) #  # A different example using a different model ## we need a data object with doses >= 1: data<Data(x=c(25,50,50,75,150,200,225,300), y=c(0,0,0,0,1,1,1,1), doseGrid=seq(from=25,to=300,by=25)) model < LogisticIndepBeta(binDLE=c(1.05,1.8), DLEweights=c(3,3), DLEdose=c(25,300), data=data) options < McmcOptions(burnin=100, step=2, samples=200) ## samples must be from 'Samples' class (object slot in fit) samples < mcmc(data,model,options) fitted < fit(object=samples, model=model, data=data) # Create some data data < DataDual( x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 50, 50, 50), y=c(0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1), w=c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.55, 0.6, 0.52, 0.54, 0.56, 0.43, 0.41, 0.39, 0.34, 0.38, 0.21), doseGrid=c(0.1, 0.5, 1.5, 3, 6, seq(from=10, to=80, by=2))) # Initialize the DualEndpoint model (in this case RW1) model < DualEndpointRW(mu = c(0, 1), Sigma = matrix(c(1, 0, 0, 1), nrow=2), sigma2betaW = 0.01, sigma2W = c(a=0.1, b=0.1), rho = c(a=1, b=1), smooth = "RW1") # Setup some MCMC parameters and generate samples from the posterior options < McmcOptions(burnin=100, step=2, samples=500) set.seed(94) samples < mcmc(data, model, options) # Extract the posterior mean (and empirical 2.5 and 97.5 percentile) # for the prob(DLT) by doses and the Biomarker by doses fitted < fit(object = samples, model = model, data = data, quantiles=c(0.025, 0.975), middle=mean) ##Obtain the 'fit' the middle, uppper and lower quantiles for the doseDLE curve ## at all dose levels using a DLE sample, a DLE model and the data ## samples must be from 'Samples' class (object slot) ## we need a data object with doses >= 1: data<Data(x=c(25,50,50,75,150,200,225,300), y=c(0,0,0,0,1,1,1,1), doseGrid=seq(from=25,to=300,by=25)) ## model must be from 'Model' or 'ModelTox' class e.g using 'LogisticIbdepBeta' model class model<LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data) ##options for MCMC options<McmcOptions(burnin=100,step=2,samples=200) ## samples must be from 'Samples' class (object slot in fit) samples<mcmc(data,model,options) fit(object=samples, model=model,data=data) ##Obtain the 'fit' the middle, uppper and lower quantiles for the doseefficacy curve ## at all dose levels using an efficacy sample, a pseudo efficacy model and the data ## data must be from 'DataDual' class data<DataDual(x=c(25,50,25,50,75,300,250,150), y=c(0,0,0,0,0,1,1,0), w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52), doseGrid=seq(25,300,25), placebo=FALSE) ## model must be from 'ModelEff' e.g using 'Effloglog' class Effmodel<Effloglog(c(1.223,2.513),c(25,300),nu=c(a=1,b=0.025),data=data,c=0) ## samples must be from 'Samples' class (object slot in fit) options<McmcOptions(burnin=100,step=2,samples=200) Effsamples < mcmc(data=data,model=Effmodel,options=options) fit(object=Effsamples, model=Effmodel,data=data) ##Obtain the 'fit' the middle, uppper and lower quantiles for the doseefficacy curve ## at all dose levels using an efficacy sample, the 'EffFlexi' efficacy model and the data ## data must be from 'DataDual' class data<DataDual(x=c(25,50,25,50,75,300,250,150), y=c(0,0,0,0,0,1,1,0), w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52), doseGrid=seq(25,300,25), placebo=FALSE) ## model must be from 'ModelEff' e.g using 'Effloglog' class Effmodel< EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300), sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data) ## samples must be from 'Samples' class (object slot in fit) options<McmcOptions(burnin=100,step=2,samples=200) Effsamples < mcmc(data=data,model=Effmodel,options=options) fit(object=Effsamples, model=Effmodel,data=data)
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