Evaluation-class {distrTEst} | R Documentation |
When an estimator is used to data of the type "Dataclass" with the method evaluate, the result is an object of class "Evaluation".
Objects could be created by calls of the form new("Evaluation", Data, estimator, [result, name, filename, call.ev])
.
It does not seem to be very useful to generate a new object this way, however.
It is to be preferred to use "evaluate" with a Dataclass object!
call.ev
:"call"
the call which created the object,
e.g.; ``evaluate(Dataclassobject,mean)'' estimator
:"OptionalFunction"
: estimation function used; this estimation function
should be able to deal with data in matrix form samplesize
x obsDim
and
should return either a univariate result or a vector (with named coordinates, if possible).filename
:"character"
: the filename of the evaluation;
by default the filename of the Dataclass object, which was called by evaluatename
:"character"
: the name of the evaluation; by default the name of the
Dataclass object, which was called by evaluate result
:"DataframeorNULL"
: the result of the evaluation of the estimation on data signature(.Object = "Evaluation")
: initialize method signature(object = "Evaluation")
: returns a boxplot of the result signature(object = "Evaluation")
: returns the name of the data object, its filename, the estimator used
and the result signature(object = "Evaluation")
: saves the object in two files in the directory of R - one with
data, one without as comment file (see example) signature(object = "Evaluation")
: returns the name of the data object, its filename, the
estimator used and a statistical summary of the result The saved "evaluation" can be loaded with the usual load-command, the saved comment with the function cload.
Thomas Stabla statho3@web.de,
Florian Camphausen fcampi@gmx.de,
Peter Ruckdeschel Peter.Ruckdeschel@itwm.fraunhofer.de,
Matthias Kohl Matthias.Kohl@stamats.de
Dataclass-class
Simulation-class
Contsimulation-class
load
cload
savedata-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 = 5, samplesize=5000, 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, resname="mean") # estimates the data with mean ev1 # bad results ev2 <- evaluate(cs,median, resname="median") # estimates the data with median ev2 # better results because median is robust savedata(ev1) # saves the evaluation 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 evaluation without result - the object is called ev1.comment ev1.comment load("csim.mean") # loads the evaluation with result ev1 plot(ev1) # #another function to be evaluated: severalThings<- function(x) {list("mean"=mean(x),"sd"=sd(x), "mad"=mad(x))} ev3 <- evaluate(cs, severalThings, resname="several") plot(ev3) plot(ev3, ylim=c(0,10), col=c("blue","green", "red"))