EvaluationList-class | R Documentation |
Several objects of class "Evaluation" may be gathered in a list of class "EvaluationList", if they all have the same result-format and also share the same data-set.
Objects may be created by the generating function EvaluationList
, i.e.;
EvaluationList(..., name0 = "a list of \"Evaluation\" objects")
, where all arguments passed through ...
have to be objects of class "Evaluation", the corresponding result-slots have to contain
data.frames of identical dimension; the corresponding calls have to have identical object
-arguments
(for the data set), and the corresponding Data
-slots have to be identical.
name
:Object of class "character"
: the name of the EvaluationList object
Elist
:Object of class "list"
: the list of Evaluation objects
signature(object = "EvaluationList")
: returns the list with the Evaluation objects
signature(object = "EvaluationList")
: returns/modifies the name of the EvaluationList object
signature(object = "EvaluationList")
: returns the common Data
-slot
of one of the Evaluation objects
signature(object = "EvaluationList")
: returns grouped boxplots of the results
signature(object = "EvaluationList")
: for each list element returns
the name of the data object, its filename, the estimator used and the result
signature(object = "EvaluationList")
: as print
signature(object = "EvaluationList")
: returns the name of the data object, its filename, the
estimator used and a statistical summary of the result
Thomas Stabla statho3@web.de,
Florian Camphausen fcampi@gmx.de,
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de,
Matthias Kohl Matthias.Kohl@stamats.de
Dataclass-class
Simulation-class
Contsimulation-class
Evaluation-class
print-methods
plot-methods
simulate-methods
summary-methods
N <- Norm() # N is a standard normal distribution. C <- Cauchy() # C is a Cauchy distribution cs <- Contsimulation(filename = "csim", runs = 15, samplesize=500, seed=setRNG(), distribution.id = N, distribution.c = C, rate = 0.1) simulate(cs) # Each of the 25000 random numbers is ideal (N-distributed) with # probability 0.9 and contaminated (C-distributed) with probability = 0.1 summary(cs) ev1 <- evaluate(cs, mean) # estimates the data with mean ev1 # bad results ev2 <- evaluate(cs,median) # estimates the data with median ev2 # better results because median is robust savedata(ev1) # saves the EvaluationList with result as "csim.mean" and without result as # "csim.mean.comment" in the working directory # of R - "csim" is the # filename of the Contsimulation object, mean the name of the estimator rm(ev1) cload("csim.mean") # loads the EvaluationList without result - the object is called ev1.comment ev1.comment load("csim.mean") # loads the EvaluationList with result ev1 ElistObj <- EvaluationList(ev1,ev2,name0="myEvalList") plot(ElistObj,ylim=matrix(c(-0.5,0.5,0.5,4),nrow=2),main=c("location","scale")) plot(ElistObj,ylim=c(-0.5,0.5),main=c("location"),runs0=3:12,dims0=1,evals0=2) ElistObj summary(ElistObj) #clean up unlink("csim.mean") unlink("csim.mean.comment")
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