DataStack {Mediana} | R Documentation |
DataStack object
Description
This function generates data according to the specified data model.
Usage
DataStack(data.model,
sim.parameters)
Arguments
data.model |
defines a |
sim.parameters |
defines a |
Value
This function generates a data stack according to the data model and the simulation parameters objetcs. The object returned by the function is a DataStack object containing:
description |
a description of the object. |
data.set |
a list of size |
data.scenario.grid |
a data frame indicating all data scenarios according to the |
data.structure |
a list containing the data structure according to the |
sim.parameters |
a list containing the simulation parameters according to |
A specific data.set
of a DataStack
object can be extracted using the ExtractDataStack
function.
References
http://gpaux.github.io/Mediana/
See Also
See Also DataModel
and SimParameters
and ExtractDataStack
.
Examples
## Not run:
# Generation of a DataStack object
##################################
# Outcome parameter set 1
outcome1.placebo = parameters(mean = 0, sd = 70)
outcome1.treatment = parameters(mean = 40, sd = 70)
# Outcome parameter set 2
outcome2.placebo = parameters(mean = 0, sd = 70)
outcome2.treatment = parameters(mean = 50, sd = 70)
# Data model
case.study1.data.model = DataModel() +
OutcomeDist(outcome.dist = "NormalDist") +
SampleSize(c(50, 55, 60, 65, 70)) +
Sample(id = "Placebo",
outcome.par = parameters(outcome1.placebo, outcome2.placebo)) +
Sample(id = "Treatment",
outcome.par = parameters(outcome1.treatment, outcome2.treatment))
# Simulation Parameters
case.study1.sim.parameters = SimParameters(n.sims = 1000,
proc.load = 2,
seed = 42938001)
# Generate data
case.study1.data.stack = DataStack(data.model = case.study1.data.model,
sim.parameters = case.study1.sim.parameters)
# Print the data set generated in the 100th simulation run
# for the 2nd data scenario for both samples
case.study1.data.stack$data.set[[100]]$data.scenario[[2]]
# Extract the same set of data
case.study1.extracted.data.stack = ExtractDataStack(data.stack = case.study1.data.stack,
data.scenario = 2,
simulation.run = 100)
# The same dataset can be obtained using
case.study1.extracted.data.stack$data.set[[1]]$data.scenario[[1]]$sample
# A carefull attention should be paid on the index of the result.
# As only one data.scenario has been requested
# the result for data.scenario = 2 is now in the first position (data.scenario[[1]]).
## End(Not run)
## Not run:
#Use of a DataStack object in the CSE function
##############################################
# Outcome parameter set 1
outcome1.placebo = parameters(mean = 0, sd = 70)
outcome1.treatment = parameters(mean = 40, sd = 70)
# Outcome parameter set 2
outcome2.placebo = parameters(mean = 0, sd = 70)
outcome2.treatment = parameters(mean = 50, sd = 70)
# Data model
case.study1.data.model = DataModel() +
OutcomeDist(outcome.dist = "NormalDist") +
SampleSize(c(50, 55, 60, 65, 70)) +
Sample(id = "Placebo",
outcome.par = parameters(outcome1.placebo, outcome2.placebo)) +
Sample(id = "Treatment",
outcome.par = parameters(outcome1.treatment, outcome2.treatment))
# Simulation Parameters
case.study1.sim.parameters = SimParameters(n.sims = 1000,
proc.load = 2,
seed = 42938001)
# Generate data
case.study1.data.stack = DataStack(data.model = case.study1.data.model,
sim.parameters = case.study1.sim.parameters)
# Analysis model
case.study1.analysis.model = AnalysisModel() +
Test(id = "Placebo vs treatment",
samples = samples("Placebo", "Treatment"),
method = "TTest")
# Evaluation model
case.study1.evaluation.model = EvaluationModel() +
Criterion(id = "Marginal power",
method = "MarginalPower",
tests = tests("Placebo vs treatment"),
labels = c("Placebo vs treatment"),
par = parameters(alpha = 0.025))
# Simulation Parameters
case.study1.sim.parameters = SimParameters(n.sims = 1000, proc.load = 2, seed = 42938001)
# Perform clinical scenario evaluation
case.study1.results = CSE(case.study1.data.stack,
case.study1.analysis.model,
case.study1.evaluation.model,
case.study1.sim.parameters)
## End(Not run)