mte.learning | R Documentation |
These functions fit mixtures of truncated exponentials (MTEs). Least square optimization is used to minimize the quadratic error between the empirical cumulative distribution function and the estimated one.
mte.learning(X, nparam, domain) bestMTE(X, domain, maxParam = NULL)
X |
A |
nparam |
Number of parameters of the resulting density function. |
domain |
A |
maxParam |
A |
mte.learning()
:
The returned value $Function
is the only visible element which contains the algebraic expression.
Using attributes the name of the others elements are shown and also they can be abstract with $
.
The summary of the function also shows all this elements.
bestMTE()
:
The first returned value $bestPx
contains the output of the mte.learning()
function
with the number of parameters which gets the best BIC value, taking into account the
Bayesian information criterion (BIC) to penalize the functions. It evaluates the two next functions,
if the BIC doesn't improve then the function with the last best BIC is returned.
mte.lerning()
returns a list of n elements:
Function |
An |
Subclass |
|
Domain |
The range where the function is defined to be a legal density function. |
Iterations |
The number of iterations that the optimization problem employed to minimize the errors. |
Time |
The CPU time consumed. |
bestMTE()
returns a list including the MTE function with the best BIC score,
the number of parameters, the best BIC value and an array contained
the BIC values of the evaluated functions.
univMoTBF A complete function for learning MoTBFs which includes extra options.
## 1. EXAMPLE data <- rchisq(1000, df=3) ## MTE with fix number of parameters fx <- mte.learning(data, nparam=7, domain=range(data)) hist(data, prob=TRUE, main="") plot(fx, col=2, xlim=range(data), add=TRUE) ## Best MTE in terms of BIC fMTE <- bestMTE(data, domain=range(data)) attributes(fMTE) fMTE$bestPx hist(data, prob=TRUE, main="") plot(fMTE$bestPx, col=2, xlim=range(data), add=TRUE) ## 2. EXAMPLE data <- rexp(1000, rate=1/3) ## MTE with fix number of parameters fx <- mte.learning(data, nparam=8, domain=range(data)) ## Message: The nearest function with odd number of coefficients hist(data, prob=TRUE, main="") plot(fx, col=2, xlim=range(data), add=TRUE) ## Best MTE in terms of BIC fMTE <- bestMTE(data, domain=range(data), maxParam=10) attributes(fMTE) fMTE$bestPx attributes(fMTE$bestPx) hist(data, prob=TRUE, main="") plot(fMTE$bestPx, col=2, xlim=range(data), add=TRUE)
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