34 lines
1.3 KiB
R
34 lines
1.3 KiB
R
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# V Matrix (gaussian)
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#
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# Author: Francois Pelletier
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#
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# LGPL-3.0
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###############################################################################
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#' V Matrix (gaussian)
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#' @param Y Individual data sample
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#' @param param Vector of parameters of the distribution function
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#' @param meanf Mean function of the distribution
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#' @param variancef Variance function of the distribution
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#' @param skewnessf Skewness function of the distribution
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#' @param kurtosisf Kurtosis function of the distribution
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#' @param dmean Derivative in respect to the parameter vector of the mean function of the distribution
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#' @param dsd Derivative in respect to the parameter vector of the standard deviation function of the distribution
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#' @return V Matrix
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#'
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#' @author Francois Pelletier
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V.gauss <- function(param,Y,meanf,variancef,skewnessf,kurtosisf,dmean,dsd)
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{
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(variancef(param)*(a.gauss(param,variancef,dmean,dsd) %o%
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a.gauss(param,variancef,dmean,dsd) +
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sqrt(variancef(param)) * skewnessf(param) *
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(a.gauss(param,variancef,dmean,dsd) %o%
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b.gauss(param,variancef,dmean,dsd) +
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b.gauss(param,variancef,dmean,dsd) %o%
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a.gauss(param,variancef,dmean,dsd)) +
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variancef(param)*(kurtosis(param)+2) *
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b.gauss(param,variancef,dmean,dsd) %*%
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t(b.gauss(param,variancef,dmean,dsd))))
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}
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