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.
Usage
fit(object, model, data, ...)
# S4 method for Samples,GeneralModel,Data
fit(
object,
model,
data,
points = data@doseGrid,
quantiles = c(0.025, 0.975),
middle = mean,
...
)
# S4 method for Samples,DualEndpoint,DataDual
fit(object, model, data, quantiles = c(0.025, 0.975), middle = mean, ...)
# S4 method for Samples,LogisticIndepBeta,Data
fit(
object,
model,
data,
points = data@doseGrid,
quantiles = c(0.025, 0.975),
middle = mean,
...
)
# S4 method for Samples,Effloglog,DataDual
fit(
object,
model,
data,
points = data@doseGrid,
quantiles = c(0.025, 0.975),
middle = mean,
...
)
# S4 method for Samples,EffFlexi,DataDual
fit(
object,
model,
data,
points = data@doseGrid,
quantiles = c(0.025, 0.975),
middle = mean,
...
)
# S4 method for Samples,LogisticLogNormalOrdinal,DataOrdinal
fit(
object,
model,
data,
points = data@doseGrid,
quantiles = c(0.025, 0.975),
middle = mean,
...
)Arguments
- object
the
Samplesobject- model
the
GeneralModelobject- data
the
Dataobject- ...
passed down to the
prob()method.- 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:
mean
Functions
fit(object = Samples, model = GeneralModel, data = Data): This method returns a data frame with dose, middle, lower and upper quantiles for the dose-toxicity curvefit(object = Samples, model = DualEndpoint, data = DataDual): This method returns a data frame with dose, and middle, lower and upper quantiles, for both the dose-tox and dose-biomarker (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 dose-DLE curve using DLE samples for “LogisticIndepBeta” model classfit(object = Samples, model = Effloglog, data = DataDual): This method returns a data frame with dose, middle, lower, upper quantiles for the dose-efficacy curve using efficacy samples for “Effloglog” model classfit(object = Samples, model = EffFlexi, data = DataDual): This method returns a data frame with dose, middle, lower and upper quantiles for the dose-efficacy curve using efficacy samples for “EffFlexi” model classfit(object = Samples, model = LogisticLogNormalOrdinal, data = DataOrdinal): This method returns a data frame with dose, middle, lower and upper quantiles for the dose-efficacy curve using efficacy samples for the “LogisticLogNormalOrdinal” model class
Examples
# nolint start
# 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)))
#> Used default patient IDs!
# Initialize a model
model <- LogisticLogNormal(mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 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))
#> Used default patient IDs!
#> Used best guess cohort indices!
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)
# nolint end
# nolint start
# 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)))
#> Used default patient IDs!
#> Used best guess cohort indices!
# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(mean = c(0, 1),
cov = 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),
rw1 = TRUE)
# Set-up 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)
# nolint end
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-DLE 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))
#> Used default patient IDs!
#> Used best guess cohort indices!
## 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)
#> dose middle lower upper
#> 1 25 0.2362155 0.08981218 0.4190492
#> 2 50 0.3682220 0.20111117 0.5781557
#> 3 75 0.4575540 0.27620943 0.6661194
#> 4 100 0.5223768 0.33161940 0.7239995
#> 5 125 0.5717475 0.37825701 0.7709958
#> 6 150 0.6107229 0.40917199 0.8141918
#> 7 175 0.6423542 0.42871110 0.8437866
#> 8 200 0.6685943 0.44575657 0.8651832
#> 9 225 0.6907535 0.46059389 0.8819519
#> 10 250 0.7097445 0.47392655 0.8953912
#> 11 275 0.7262234 0.48877922 0.9063659
#> 12 300 0.7406746 0.51041966 0.9148683
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-efficacy 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)
#> Used default patient IDs!
#> Used best guess cohort indices!
## 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)
#> dose middle lower upper
#> 1 25 0.465844 -0.1165332 1.007491
#> 2 50 0.822886 0.3886473 1.180275
#> 3 75 1.003435 0.5768330 1.403713
#> 4 100 1.121531 0.6228397 1.630016
#> 5 125 1.208159 0.6601782 1.787815
#> 6 150 1.276017 0.6894266 1.902756
#> 7 175 1.331491 0.7064292 2.018253
#> 8 200 1.378223 0.6925712 2.105017
#> 9 225 1.418476 0.6806345 2.173231
#> 10 250 1.453748 0.6701746 2.233005
#> 11 275 1.485081 0.6687583 2.286102
#> 12 300 1.513225 0.6801521 2.333796
# nolint start
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-efficacy 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)
#> Used default patient IDs!
#> Used best guess cohort indices!
## model must be from 'ModelEff' e.g using 'Effloglog' class
Effmodel<- EffFlexi(eff=c(1.223, 2.513),eff_dose=c(25,300),
sigma2W=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),rw1 = FALSE,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)
#> dose middle lower upper
#> 1 25 0.7036011 0.6866660 0.7158120
#> 2 50 0.4494999 0.4100730 0.5051847
#> 3 75 0.5795760 0.5125865 0.6297975
#> 4 100 0.6321164 -1.3094061 2.4041159
#> 5 125 0.5879013 -1.3602565 2.2919075
#> 6 150 0.5246635 0.5162698 0.5419775
#> 7 175 0.6355070 -1.7183340 3.1674019
#> 8 200 0.8103666 -3.1066777 5.2791132
#> 9 225 1.0235281 -4.1707667 5.8087216
#> 10 250 1.4342934 -4.1558082 6.0408674
#> 11 275 1.9401852 -1.9698781 5.1282467
#> 12 300 2.5119573 2.5075891 2.5129950
# nolint end
model <- .DefaultLogisticLogNormalOrdinal()
ordinal_data <- .DefaultDataOrdinal()
options <- .DefaultMcmcOptions()
samples <- mcmc(ordinal_data, model, options)
#> Warning: Unused variable "y" in data
grade1_fit <- fit(samples, model, ordinal_data, grade = 1L)
grade2_fit <- fit(samples, model, ordinal_data, grade = 2L)