Nothing
R/CDF_est.R
In GenHMM1d: Goodness-of-Fit for Univariate Hidden Markov Models
Defines functions
pgeometricprog
CDF_est
Documented in
CDF_est
#'@title Cumulative density function
#'
#'@description This function compute the cumulative density function of an univariate distribution (used) in the CVM calculation
#'
#'@param family distribution name; run the command distributions() for help
#'@param y observations
#'@param param parameters of the distribution
#'@param size additional parameter for some discrete distributions
#'@param u_Ros used internally for the computation of the Rosenblatt transform for univariate discrete distribution
#'
#'@return \item{f}{cdf}
#'
#'
#'@export
#'@keywords internal
CDF_est<-function(family,y,param,size=0,u_Ros=0){
switch(family,
"asymexppower" = { ## [R+, R+, 01]
f = VaRES::paep(y, q1 = param[1], q2 = param[2], alpha = param[3])
} ,
"asymlaplace" = { ## [R, R+, R+]
f = VGAM::palap(y, location = param[1], scale = param[2], kappa = param[3])
} ,
"asympower" = { ## [01, R+, R+]
f = VaRES::pasypower(y, a = param[1], lambda = param[2], delta = param[3])
} ,
"asymt" = { ## [R+, R+, 01, R]
f = VaRES::past(y-param[4], nu1 = param[1], nu2 = param[2], alpha = param[3])
} ,
"beard" = { ## [R+, R+, R+]
f = VaRES::pbeard(y, a = param[1], b = param[2], rho = param[3])
} ,
"benini" = { ## [R, R+]
f = VGAM::pbenini(y, y0 = param[1], shape = param[2])
} ,
"benford" = { ## [1 ou 2]
f = VGAM::pbenf(y, ndigits = param[1])
} ,
"bernoulli" = { ## [01]
f = extraDistr::pbern(y, prob = param[1])
} ,
"beta" = { ## [R+, R+]
f = stats::pbeta(y, shape1 = param[1], shape2 = param[2])
} ,
"betabinomial" = { ## [N+, R+, R+]
f_1 = extraDistr::pbbinom(y-0.7, size = size, alpha = param[1], beta = param[2])
f_2 = extraDistr::pbbinom(y, size = size, alpha = param[1], beta = param[2])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"betageometric" = { ## [R+, R+]
f_1 = VGAM::pbetageom(y-0.7, shape1 = param[1], shape2 = param[2])
f_2 = VGAM::pbetageom(y, shape1 = param[1], shape2 = param[2])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"betanegativebinomial" = { ## [N+, R+, R+]
f_1 = extraDistr::pbnbinom(y-0.7, size = size, alpha = param[1], beta = param[2])
f_2 = extraDistr::pbnbinom(y, size = size, alpha = param[1], beta = param[2])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"betaburr" = { ## [R+, R+, R+, R+]
f = VaRES::pbetaburr(y, a = param[1], b = param[2], c = param[3], d = param[4])
} ,
"betaburr7" = { ## [R+, R+, R+, R+]
f = VaRES::pbetaburr7(y, a = param[1], b = param[2], c = param[3], k = param[4])
} ,
"betaexponential" = { ## [R+, R+, R+]
f = VaRES::pbetaexp(y, lambda = param[1], a = param[2], b = param[3])
} ,
"betafrechet" = { ## [R+, R+, R+, R+]
f = VaRES::pbetafrechet(y, a = param[1], b = param[2], alpha = param[3], sigma = param[4])
} ,
"betagompertz" = { ## [R+, R+, R+, R+]
f = VaRES::pbetagompertz(y, b = param[1], c = param[2], d = param[3], eta = param[4])
} ,
"betagumbel" = { ## [R+, R+, R, R+]
f = VaRES::pbetagumbel(y, a = param[1], b = param[2], mu = param[3], sigma = param[4])
} ,
"betagumbel2" = { ## [R+, R+, R+, R+]
f = VaRES::pbetagumbel2(y, a = param[1], b = param[2], c = param[3], d = param[4])
} ,
"betalognormal" = { ## [R+, R+, R, R+]
f = VaRES::pbetalognorm(y, a = param[1], b = param[2], mu = param[3], sigma = param[4])
} ,
"betalomax" = { ## [R+, R+, R+, R+]
f = VaRES::pbetalomax(y, a = param[1], b = param[2], alpha = param[3], lambda = param[4])
} ,
"betanormal" = { ## [R+, R+, R, R+]
f = VGAM::pbetanorm(y, shape1 = param[1], shape2 = param[2],
mean = param[3], sd = param[4])
} ,
"betaprime" = { ## [R+, R+, R+]
f = extraDistr::pbetapr(y, shape1 = param[1], shape2 = param[2], scale = param[3])
} ,
"betaweibull" = { ## [R+, R+, R+, R+]
f = VaRES::pbetaweibull(y, a = param[1], b = param[2], alpha = param[3], sigma = param[4])
} ,
"bhattacharjee" = { ## [R, R+, R+]
f = extraDistr::pbhatt(y, mu = param[1], sigma = param[2], a = param[3])
} ,
"binomial" = { ## [N+, 01]
f_1 = stats::pbinom(y-0.7, size = size, prob = param[1])
f_2 = stats::pbinom(y, size = size, prob = param[1])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"birnbaumsaunders" = { ## [R+, R+, R]
f = extraDistr::pfatigue(y, alpha = param[1], beta = param[2], mu = param[3])
} ,
"boxcox" = { ## [R+, R+, R+]
f = rmutil::pboxcox(y, m = param[1], s = param[2], f = param[3])
} ,
"burr" = { ## [R+, R+, R+]
f = actuar::pburr(y, shape1 = param[1], shape2 = param[2], scale = param[3])
} ,
"burr2param" = { ## [R+, R+]
f = VaRES::pburr(y, a = param[1], b = param[2])
} ,
"cauchy" = { ## [R, R+]
f = stats::pcauchy(y, location = param[1], scale = param[2])
} ,
"chen" = { ## [R+, R+]
f = VaRES::pchen(y, b = param[1], lambda = param[2])
} ,
"chi" = { ## [R+]
f = EnvStats::pchi(y, df = param[1])
} ,
"chisquared" = { ## [R+]
f = stats::pchisq(y, df = param[1])
} ,
"clg" = { ## [R+, R+, R]
f = VaRES::pclg(y, a = param[1], b = param[2], param[3])
} ,
"complementarybeta" = { ## [R+, R+]
f = VaRES::pcompbeta(y, a = param[1], b = param[2])
} ,
"dagum" = { ## [R+, R+, R+]
f = VGAM::pdagum(y, scale = param[1], shape1.a = param[2], shape2.p = param[3])
} ,
"diffzeta" = { ## [R+, >1]
f = VGAM::pdiffzeta(y, shape = param[1], start = param[2])
} ,
"discretegamma" = { ## [R+, R+]
f_1 = extraDistr::pdgamma(y-0.7, shape = param[1], scale = param[2])
f_2 = extraDistr::pdgamma(y, shape = param[1], scale = param[2])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"discretelaplace" = { ## [R, 01]
f_1 = extraDistr::pdlaplace(y-0.7, location = param[1], scale = param[2])
f_2 = extraDistr::pdlaplace(y, location = param[1], scale = param[2])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"discretenormal" = { ## [R, R+]
f_1 = extraDistr::pdnorm(y-0.7, mean = param[1], sd = param[2])
f_2 = extraDistr::pdnorm(y, mean = param[1], sd = param[2])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"discreteweibull" = { ## [01, R+]
f_1 = extraDistr::pdweibull(y-0.7, shape1 = param[1], shape2 = param[2])
f_2 = extraDistr::pdweibull(y, shape1 = param[1], shape2 = param[2])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"doubleweibull" = { ## [R+, R, R+]
f = VaRES::pdweibull(y, c = param[1], mu = param[2], sigma = param[3])
} ,
"ev" = {
## [R, R+]
f = VGAM::pgev(y, location = param[1], scale = param[2], shape = 0)
} ,
"exponential" = { ## [R+]
f = stats::pexp(y, rate = param[1])
} ,
"exponentialextension" = { ## [R+, R+]
f = VaRES::pexpext(y, lambda = param[1], a = param[2])
} ,
"exponentialgeometric" = { ## [R+, 01]
f = VGAM::pexpgeom(y, scale = param[1], shape = param[2])
} ,
"exponentiallogarithmic" = { ## [R+, 01]
f = VGAM::pexplog(y, scale = param[1], shape = param[2])
} ,
"exponentialpoisson" = { ## [R+, R+]
f = VaRES::pexppois(y, b = param[1], lambda = param[2])
} ,
"exponentialpower" = { ## [R, R+, R+]
f = VaRES::pexppower(y, mu = param[1], sigma = param[2], a = param[3])
} ,
"exponentiatedexponential" = { ## [R+, R+]
f = VaRES::pexpexp(y, lambda = param[1], a = param[2])
} ,
"exponentiatedlogistic" = { ## [R+, R+]
f = VaRES::pexplogis(y, a = param[1], b = param[2])
} ,
"exponentiatedweibull" = { ## [R+, R+, R+]
f = VaRES::pexpweibull(y, a = param[1], alpha = param[2], sigma = param[3])
} ,
"F" = { ## [R+, R+]
f = stats::pf(y, df1 = param[1], df2 = param[2])
} ,
"fellerpareto" = { ## [R(mini), R+, R+, R+, R+]
f = actuar::pfpareto(y, min = param[1], shape1 = param[2],
shape2 = param[3], shape3 = param[4],
scale = param[5])
} ,
"fisk" = { ## [R+, R+]
f = VGAM::pfisk(y, scale = param[1], shape1.a = param[2])
} ,
"foldednormal" = { ## [R, R+]
f = VGAM::pfoldnorm(y, mean = param[1], sd = param[2])
} ,
"frechet" = { ## [R+, R, R+]
f = VGAM::pfrechet(y, shape = param[1], location = param[2], scale = param[3])
} ,
"gamma" = { ## [R+, R+]
f = stats::pgamma(y, shape = param[1], scale = param[2])
} ,
"gammapoisson" = { ## [R+, R+]
f_1 = extraDistr::pgpois(y-0.7, shape = param[1], scale = param[2])
f_2 = extraDistr::pgpois(y, shape = param[1], scale = param[2])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"gaussian" = { ## [R, R+]
f = stats::pnorm(y, mean = param[1], sd = param[2])
} ,
"gev" = { ## [R, R+, R]
f = VGAM::pgev(y, location = param[1], scale = param[2], shape = param[3])
} ,
"geninvbeta" = { ## [R+, R+, R+]
f = VaRES::pgeninvbeta(y, a = param[1], c = param[2], d = param[3])
} ,
"genlogis" = { ## [R+, R, R+]
f = VaRES::pgenlogis(y, a = param[1], mu = param[2], sigma = param[3])
} ,
"genlogis3" = { ## [R+, R, R+]
f = VaRES::pgenlogis3(y, a = param[1], mu = param[2], sigma = param[3])
} ,
"genlogis4" = { ## [R+, R+, R, R+]
f = VaRES::pgenlogis4(y, a = param[1], alpha = param[2], mu = param[3], sigma = param[4])
} ,
"genpowerweibull" = { ## [R+, R+]
f = VaRES::pgenpowerweibull(y, a = param[1], theta = param[2])
} ,
"generalizedhyperbolic" = { ## [R, R+, R+, R, R] [mu, delta, alpha, beta, lambda] (avec alpha^2 > beta^2)
f = GeneralizedHyperbolic::pghyp(y, mu = param[1], delta = param[2],
alpha = param[3], beta = param[4],
lambda = param[5])
} ,
"generalizedlambda" = { ## [R, R+, R, R]
f = GLDEX::pgl(y, lambda1 = param[1], lambda2 = param[2], lambda3 = param[3], lambda4 = param[4])
} ,
"generalizedt" = { ## [R, R+, R+, R+]
f = gamlss.dist::pGT(y, mu = param[1], sigma = param[2], nu = param[3], tau = param[4])
} ,
"geometric" = { ## [01]
f_1 = stats::pgeom(y-0.7, prob = param[1])
f_2 = stats::pgeom(y, prob = param[1])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"gompertz" = { ## [R+, R+]
f = ssdtools::pgompertz(y, lscale = param[1], lshape = param[2])
} ,
"gpd" = { ## [R, R+, R]
f = VGAM::pgpd(y, location = param[1], scale = param[2], shape = param[3])
} ,
"gumbel" = { ## [R, R+]
f = VGAM::pgumbel(y, location = param[1], scale = param[2])
} ,
"gumbel2" = { ## [R+, R+]
f = VGAM::pgumbelII(y, scale = param[1], shape = param[2])
} ,
"halfcauchy" = { ## [R+]
f = extraDistr::phcauchy(y, sigma = param[1])
} ,
"halflogistic" = { ## [R+]
f = VaRES::phalflogis(y, lambda = param[1])
} ,
"halfnormal" = { ## [R+]
f = extraDistr::phnorm(y, sigma = param[1])
} ,
"halft" = { ## [R+, R+]
f = extraDistr::pht(y, nu = param[1], sigma = param[2])
} ,
"hjorth" = { ## [R+, R+, R+]
f = rmutil::phjorth(y, m = param[1], s = param[2], f = param[3])
} ,
"hblaplace" = { ## [01, R, R+]
f = VaRES::pHBlaplace(y, a = param[1], theta = param[2], phi = param[3])
} ,
"hyperbolic" = { ## [R, R+, R+, R] [mu, delta, alpha, beta] (avec alpha^2 > beta^2)
f = GeneralizedHyperbolic::phyperb(y, mu = param[1], delta = param[2],
alpha = param[3], beta = param[4])
} ,
"huber" = { ## [R, R+]
f = extraDistr::phuber(y, mu = param[1], sigma = param[2])
} ,
"hzeta" = { ## [R+]
f_1 = VGAM::phzeta(y-0.7, shape = param[1])
f_2 = VGAM::phzeta(y, shape = param[1])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"inversebeta" = { ## [R+, R+]
f = VaRES::pinvbeta(y, a = param[1], b = param[2])
} ,
"inverseburr" = { ## [R+, R+, R+]
f = actuar::pinvburr(y, shape1 = param[1], shape2 = param[2], scale = param[3])
} ,
"inversechisquared" = { ## [R+]
f = extraDistr::pinvchisq(y, nu = param[1])
} ,
"inverseexponential" = { ## [R+]
f = actuar::pinvexp(y, scale = param[1])
} ,
"inverseexpexponential" = { ## [R+, R+]
f = VaRES::pinvexpexp(y, lambda = param[1], a = param[2])
} ,
"inversegamma" = { ## [R+, R+]
f = extraDistr::pinvgamma(y, alpha = param[1], beta = param[2])
} ,
"inverselomax" = { ## [R+, R+]
f = VGAM::pinv.lomax(y, scale = param[1], shape2.p = param[2])
} ,
"inverseparalogistic" = { ## [R+, R+]
f = actuar::pinvparalogis(y, shape = param[1], scale = param[2])
} ,
"inversepareto" = { ## [R+, R+]
f = actuar::pinvpareto(y, shape = param[1], scale = param[2])
} ,
"inversetransformedgamma" = { ## [R+, R+, R+]
f = actuar::pinvtrgamma(y, shape1 = param[1], shape2 = param[2], scale = param[3])
} ,
"inverseweibull" = { ## [R+, R+]
f = actuar::pinvweibull(y, shape = param[1], scale = param[2])
} ,
"kumaraswamy" = { ## [R+, R+]
f = VGAM::pkumar(y, shape1 = param[1], shape2 = param[2])
} ,
"kumaraswamyexponential" = { ## [R+, R+, R+]
f = VaRES::pkumexp(y, lambda = param[1], a = param[2], b = param[3])
} ,
"kumaraswamygamma" = { ## [R+, R+, R+, R+]
f = VaRES::pkumgamma(y, a = param[1], b = param[2], c = param[3], d = param[4])
} ,
"kumaraswamygumbel" = { ## [R+, R+, R, R+]
f = VaRES::pkumgumbel(y, a = param[1], b = param[2], mu = param[3],
sigma = param[4])
} ,
"kumaraswamyhalfnormal" = { ## [R+, R+, R+]
f = VaRES::pkumhalfnorm(y, sigma = param[1], a = param[2], b = param[3])
} ,
"kumaraswamyloglogistic" = { ## [R+, R+, R+, R+]
f = VaRES::pkumloglogis(y, a = param[1], b = param[2], alpha = param[3],
beta = param[4])
} ,
"kumaraswamynormal" = { ## [R, R+, R+, R+]
f = VaRES::pkumnormal(y, mu = param[1], sigma = param[2], a = param[3],
b = param[4])
} ,
"kumaraswamyweibull" = { ## [R+, R+, R+, R+]
f = VaRES::pkumweibull(y, a = param[1], b = param[2], alpha = param[3],
sigma = param[4])
} ,
"laplace" = { ## [R, R+]
f = extraDistr::plaplace(y, mu = param[1], sigma = param[2])
} ,
"levy" = { ## [R, R+]
f = rmutil::plevy(y, m = param[1], s = param[2])
} ,
"linearfailurerate" = { ## [R+, R+]
f = VaRES::plfr(y, a = param[1], b = param[2])
} ,
"lindley" = { ## [R+]
f = VGAM::plind(y, theta = param[1])
} ,
"libbynovickbeta" = { ## [R+, R+, R+]
f = VaRES::pLNbeta(y, lambda = param[1], a = param[2], b = param[3])
} ,
"logcauchy" = { ## [R, R+]
f = VaRES::plogcauchy(y, mu = param[1], sigma = param[2])
} ,
"loggamma" = { ## [R, R+, R+]
f = VGAM::plgamma(y, location = param[1], scale = param[2], shape = param[3])
} ,
"loggumbel" = { ## [R, R+]
f = ssdtools::plgumbel(y, llocation = param[1], lscale = param[2])
} ,
"loglaplace" = { ## [R, R+, R+]
f = VGAM::ploglap(y, location.ald = param[1], scale.ald = param[2], kappa = param[3])
} ,
"loglog" = { ## [R+, >1]
f = VaRES::ploglog(y, a = param[1], lambda = param[2])
} ,
"loglogistic" = { ## [R+, R+]
f = actuar::pllogis(y, shape = param[1], scale = param[2])
} ,
"lognormal" = { ## [R, R+]
f = stats::plnorm(y, meanlog = param[1], sdlog = param[2])
} ,
"lognormal3" = { ## [R, R+, R]
f = EnvStats::plnorm3(y, meanlog = param[1], sdlog = param[2], threshold = param[3])
} ,
"logistic" = { ## [R, R+]
f = stats::plogis(y, location = param[1], scale = param[2])
} ,
"logisticexponential" = { ## [R+, R+]
f = VaRES::plogisexp(y, lambda = param[1], a = param[2])
} ,
"logisticrayleigh" = { ## [R+, R+]
f = VaRES::plogisrayleigh(y, a = param[1], lambda = param[2])
} ,
"logseries" = { ## [01]
f_1 = extraDistr::plgser(y-0.7, theta = param[1])
f_2 = extraDistr::plgser(y, theta = param[1])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"lomax" = { ## [R+, R+]
f = VGAM::plomax(y, scale = param[1], shape3.q = param[2])
} ,
"makeham" = { ## [R+, R+, R+]
f = VGAM::pmakeham(y, scale = param[1], shape = param[2], epsilon = param[3])
} ,
"maxwell" = { ## [R+]
f = VGAM::pmaxwell(y, rate = param[1])
} ,
"mcgilllaplace" = { ## [R, R+, R+]
f = VaRES::pMlaplace(y, theta = param[1], phi = param[2], psi = param[3])
} ,
"moexponential" = { ## [R+, R+]
f = VaRES::pmoexp(y, lambda = param[1], a = param[2])
} ,
"moweibull" = { ## [R+, R+, R+]
f = VaRES::pmoweibull(y, a = param[1], b = param[2], lambda = param[3])
} ,
"nakagami" = { ## [R+, R+]
f = VGAM::pnaka(y, scale = param[1], shape = param[2])
} ,
"ncchisquared" = { ## [R+, R+]
f = stats::pchisq(y, df = param[1], ncp = param[2])
} ,
"ncF" = { ## [R+, R+, R+]
f = stats::pf(y, df1 = param[1], df2 = param[2], ncp = param[3])
} ,
"negativebinomial" = { ## [N+, 01]
f_1 = stats::pnbinom(y-0.7, size = size, prob = param[1])
f_2 = stats::pnbinom(y, size = size, prob = param[1])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"normalinversegaussian" = { ## [R, R, R+, R]
f = GeneralizedHyperbolic::pnig(y, mu = param[1], delta = param[2],
alpha = param[3], beta = param[4])
} ,
"nsbeta" = { ## [R+, R+, R(min), R(maxi)]
f = extraDistr::pnsbeta(y, shape1 = param[1], shape2 = param[2],
min = param[3], max = param[4])
} ,
"paralogistic" = { ## [R+, R+]
f = VGAM::pparalogistic(y, scale = param[1], shape1.a = param[2])
} ,
"pareto" = { ## [R+, R+]
f = extraDistr::ppareto(y, a = param[1], b = min(y))
} ,
"paretopositivestable" = { ## [R+, R+, R+]
f = VaRES::pparetostable(y, lambda = param[1], nu = param[2], sigma = param[3])
} ,
"pareto1" = { ## [R+, R+]
f = VGAM::pparetoI(y, scale = param[1], shape = param[2])
} ,
"pareto2" = { ## [R, R+, R+]
f = VGAM::pparetoII(y, location = param[1], scale = param[2], shape = param[3])
} ,
"pareto3" = { ## [R, R+, R+]
f = VGAM::pparetoIII(y, location = param[1], scale = param[2], inequality = param[3])
} ,
"pareto4" = { ## [R, R+, R+, R+]
f = VGAM::pparetoIV(y, location = param[1], scale = param[2], inequality = param[3], shape = param[4])
} ,
"perks" = { ## [R+, R+]
f = VGAM::pperks(y, scale = param[1], shape = param[2])
} ,
"pctalaplace" = { ## [R+, R]
f = VaRES::pPCTAlaplace(y, a = param[1], theta = param[2])
} ,
"poisson" = { ## [R+]
f_1 = stats::ppois(y-0.7, lambda = param[1])
f_2 = stats::ppois(y, lambda = param[1])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"power1" = { ## [R+]
f = VaRES::ppower1(y, a = param[1])
} ,
"power2" = { ## [R+]
f = VaRES::ppower2(y, b = param[1])
} ,
"powerdistribution" = { ## [R+, R+]
f = extraDistr::ppower(y, alpha = param[1], beta = param[2])
} ,
"powerexponential" = { ## [R, R+, R+]
f = rmutil::ppowexp(y, m = param[1], s = param[2], f = param[3])
} ,
"rayleigh" = { ## [R+]
f = VGAM::prayleigh(y, scale = param[1])
} ,
"reflectedgamma" = { ## [R+, R, R+]
f = VaRES::prgamma(y, a = param[1], theta = param[2], phi = param[3])
} ,
"rice" = { ## [R+, R+]
f = VGAM::price(y, sigma = param[1], vee = param[2])
} ,
"scaledchisquared" = { ## [R+, R+]
f = extraDistr::pinvchisq(y, nu = param[1], tau = param[2])
} ,
"schabe" = { ## [R+, R+]
f = VaRES::pschabe(y, gamma = param[1], theta = param[2])
} ,
"simplex" = { ## [01, R+]
f = rmutil::psimplex(y, m = param[1], s = param[2])
} ,
"skewedlaplace" = { ## [R, R+, R+]
f = rmutil::pskewlaplace(y, m = param[1], s = param[2], f = param[3])
} ,
"skewedt" = { ## [R+, R+]
f = skewt::pskt(y, df = param[1], gamma = param[2])
} ,
"skewedtfourparam" = { ## [R, R+, R, R+(<25)]
f = sn::pst(y, xi = param[1], omega = param[2], alpha = param[3], nu = param[4])
} ,
"skewednormal" = { ## [R, R+, R, R]
f = sn::psn(y, xi = param[1], omega = param[2], alpha = param[3])
} ,
"skewedgeneralizedt" = { ## [R, R+, -1+1, R+(>1), R+(>1)]
f = sgt::psgt(y, mu = param[1], sigma = param[2], lambda = param[3], p = param[4])
} ,
"skewedexponentialpower" = { ## [R, R+, R, R+]
f = gamlss.dist::pSEP(y, mu = param[1], sigma = param[2], nu = param[3], tau = param[4])
} ,
"slash" = { ## [R, R+]
f = extraDistr::pslash(y, mu = param[1], sigma = param[2])
} ,
"stable" = { ## [02, -1+1, R+, R]
f = stabledist::pstable(y, alpha = param[1], beta = param[2], gamma = param[3],
delta = param[4])
} ,
"stacy" = { ## [R+, R+, R+]
f = VaRES::pstacygamma(y, gamma = param[1], c = param[2], theta = param[3])
} ,
"t" = { ## [R, R+, R+]
f = stats::pt((y-param[1])/param[2], df = param[3])
} ,
"tobit" = { ## [R, R+]
f = VGAM::ptobit(y, mean = param[1], sd = param[2])
} ,
"topple" = { ## [01]
f = VGAM::ptopple(y, shape = param[1])
} ,
"transformedbeta" = { ## [R+, R+, R+, R+]
f = actuar::ptrbeta(y, shape1 = param[1], shape2 = param[2], shape3 = param[3],
scale = param[4])
} ,
"transformedgamma" = { ## [R+, R+, R+]
f = actuar::ptrgamma(y, shape1 = param[1], shape2 = param[2], scale = param[3])
} ,
"truncatednormal" = { ## [R, R+, R(min), R(max)]
f = extraDistr::ptnorm(y, mean = param[1], sd = param[2], a = param[3],
b = param[4])
} ,
"truncatedpareto" = { ## [R+(mini), R+(maxi), R+]
f = VGAM::ptruncpareto(y, lower = param[1], upper = param[2], shape = param[3])
} ,
"twosidedpower" = { ## [01, R+]
f = rmutil::ptwosidedpower(y, m = param[1], s = param[2])
} ,
"wald" = { ## [R, R+]
f = extraDistr::pwald(y, mu = param[1], lambda = param[2])
} ,
"weibull" = { ## [R+, R+]
f = stats::pweibull(y, shape = param[1], scale = param[2])
} ,
"xie" = { ## [R+, R+, R+]
f = VaRES::pxie(y, a = param[1], b = param[2], lambda = param[3])
} ,
"yules" = { ## [R+] mais > 0.5
f_1 = VGAM::pyules(y, shape = param[1])
f_2 = VGAM::pyules(y, shape = param[1])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
)
f[is.nan(f)] <- 0
return(f)
}
pgeometricprog<-function(y,prob){
f = matrix(0, nrow = length(y), ncol = 1)
for (i in 1:length(y)){
if (y[1]>=0){
f[i] = 1 - (1-prob)^(y[i]+1)
}
}
return(f)
}
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Defines functions pgeometricprog CDF_est
Documented in CDF_est
#'@title Cumulative density function
#'
#'@description This function compute the cumulative density function of an univariate distribution (used) in the CVM calculation
#'
#'@param family distribution name; run the command distributions() for help
#'@param y observations
#'@param param parameters of the distribution
#'@param size additional parameter for some discrete distributions
#'@param u_Ros used internally for the computation of the Rosenblatt transform for univariate discrete distribution
#'
#'@return \item{f}{cdf}
#'
#'
#'@export
#'@keywords internal
CDF_est<-function(family,y,param,size=0,u_Ros=0){
switch(family,
"asymexppower" = { ## [R+, R+, 01]
f = VaRES::paep(y, q1 = param[1], q2 = param[2], alpha = param[3])
} ,
"asymlaplace" = { ## [R, R+, R+]
f = VGAM::palap(y, location = param[1], scale = param[2], kappa = param[3])
} ,
"asympower" = { ## [01, R+, R+]
f = VaRES::pasypower(y, a = param[1], lambda = param[2], delta = param[3])
} ,
"asymt" = { ## [R+, R+, 01, R]
f = VaRES::past(y-param[4], nu1 = param[1], nu2 = param[2], alpha = param[3])
} ,
"beard" = { ## [R+, R+, R+]
f = VaRES::pbeard(y, a = param[1], b = param[2], rho = param[3])
} ,
"benini" = { ## [R, R+]
f = VGAM::pbenini(y, y0 = param[1], shape = param[2])
} ,
"benford" = { ## [1 ou 2]
f = VGAM::pbenf(y, ndigits = param[1])
} ,
"bernoulli" = { ## [01]
f = extraDistr::pbern(y, prob = param[1])
} ,
"beta" = { ## [R+, R+]
f = stats::pbeta(y, shape1 = param[1], shape2 = param[2])
} ,
"betabinomial" = { ## [N+, R+, R+]
f_1 = extraDistr::pbbinom(y-0.7, size = size, alpha = param[1], beta = param[2])
f_2 = extraDistr::pbbinom(y, size = size, alpha = param[1], beta = param[2])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"betageometric" = { ## [R+, R+]
f_1 = VGAM::pbetageom(y-0.7, shape1 = param[1], shape2 = param[2])
f_2 = VGAM::pbetageom(y, shape1 = param[1], shape2 = param[2])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"betanegativebinomial" = { ## [N+, R+, R+]
f_1 = extraDistr::pbnbinom(y-0.7, size = size, alpha = param[1], beta = param[2])
f_2 = extraDistr::pbnbinom(y, size = size, alpha = param[1], beta = param[2])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"betaburr" = { ## [R+, R+, R+, R+]
f = VaRES::pbetaburr(y, a = param[1], b = param[2], c = param[3], d = param[4])
} ,
"betaburr7" = { ## [R+, R+, R+, R+]
f = VaRES::pbetaburr7(y, a = param[1], b = param[2], c = param[3], k = param[4])
} ,
"betaexponential" = { ## [R+, R+, R+]
f = VaRES::pbetaexp(y, lambda = param[1], a = param[2], b = param[3])
} ,
"betafrechet" = { ## [R+, R+, R+, R+]
f = VaRES::pbetafrechet(y, a = param[1], b = param[2], alpha = param[3], sigma = param[4])
} ,
"betagompertz" = { ## [R+, R+, R+, R+]
f = VaRES::pbetagompertz(y, b = param[1], c = param[2], d = param[3], eta = param[4])
} ,
"betagumbel" = { ## [R+, R+, R, R+]
f = VaRES::pbetagumbel(y, a = param[1], b = param[2], mu = param[3], sigma = param[4])
} ,
"betagumbel2" = { ## [R+, R+, R+, R+]
f = VaRES::pbetagumbel2(y, a = param[1], b = param[2], c = param[3], d = param[4])
} ,
"betalognormal" = { ## [R+, R+, R, R+]
f = VaRES::pbetalognorm(y, a = param[1], b = param[2], mu = param[3], sigma = param[4])
} ,
"betalomax" = { ## [R+, R+, R+, R+]
f = VaRES::pbetalomax(y, a = param[1], b = param[2], alpha = param[3], lambda = param[4])
} ,
"betanormal" = { ## [R+, R+, R, R+]
f = VGAM::pbetanorm(y, shape1 = param[1], shape2 = param[2],
mean = param[3], sd = param[4])
} ,
"betaprime" = { ## [R+, R+, R+]
f = extraDistr::pbetapr(y, shape1 = param[1], shape2 = param[2], scale = param[3])
} ,
"betaweibull" = { ## [R+, R+, R+, R+]
f = VaRES::pbetaweibull(y, a = param[1], b = param[2], alpha = param[3], sigma = param[4])
} ,
"bhattacharjee" = { ## [R, R+, R+]
f = extraDistr::pbhatt(y, mu = param[1], sigma = param[2], a = param[3])
} ,
"binomial" = { ## [N+, 01]
f_1 = stats::pbinom(y-0.7, size = size, prob = param[1])
f_2 = stats::pbinom(y, size = size, prob = param[1])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"birnbaumsaunders" = { ## [R+, R+, R]
f = extraDistr::pfatigue(y, alpha = param[1], beta = param[2], mu = param[3])
} ,
"boxcox" = { ## [R+, R+, R+]
f = rmutil::pboxcox(y, m = param[1], s = param[2], f = param[3])
} ,
"burr" = { ## [R+, R+, R+]
f = actuar::pburr(y, shape1 = param[1], shape2 = param[2], scale = param[3])
} ,
"burr2param" = { ## [R+, R+]
f = VaRES::pburr(y, a = param[1], b = param[2])
} ,
"cauchy" = { ## [R, R+]
f = stats::pcauchy(y, location = param[1], scale = param[2])
} ,
"chen" = { ## [R+, R+]
f = VaRES::pchen(y, b = param[1], lambda = param[2])
} ,
"chi" = { ## [R+]
f = EnvStats::pchi(y, df = param[1])
} ,
"chisquared" = { ## [R+]
f = stats::pchisq(y, df = param[1])
} ,
"clg" = { ## [R+, R+, R]
f = VaRES::pclg(y, a = param[1], b = param[2], param[3])
} ,
"complementarybeta" = { ## [R+, R+]
f = VaRES::pcompbeta(y, a = param[1], b = param[2])
} ,
"dagum" = { ## [R+, R+, R+]
f = VGAM::pdagum(y, scale = param[1], shape1.a = param[2], shape2.p = param[3])
} ,
"diffzeta" = { ## [R+, >1]
f = VGAM::pdiffzeta(y, shape = param[1], start = param[2])
} ,
"discretegamma" = { ## [R+, R+]
f_1 = extraDistr::pdgamma(y-0.7, shape = param[1], scale = param[2])
f_2 = extraDistr::pdgamma(y, shape = param[1], scale = param[2])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"discretelaplace" = { ## [R, 01]
f_1 = extraDistr::pdlaplace(y-0.7, location = param[1], scale = param[2])
f_2 = extraDistr::pdlaplace(y, location = param[1], scale = param[2])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"discretenormal" = { ## [R, R+]
f_1 = extraDistr::pdnorm(y-0.7, mean = param[1], sd = param[2])
f_2 = extraDistr::pdnorm(y, mean = param[1], sd = param[2])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"discreteweibull" = { ## [01, R+]
f_1 = extraDistr::pdweibull(y-0.7, shape1 = param[1], shape2 = param[2])
f_2 = extraDistr::pdweibull(y, shape1 = param[1], shape2 = param[2])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"doubleweibull" = { ## [R+, R, R+]
f = VaRES::pdweibull(y, c = param[1], mu = param[2], sigma = param[3])
} ,
"ev" = {
## [R, R+]
f = VGAM::pgev(y, location = param[1], scale = param[2], shape = 0)
} ,
"exponential" = { ## [R+]
f = stats::pexp(y, rate = param[1])
} ,
"exponentialextension" = { ## [R+, R+]
f = VaRES::pexpext(y, lambda = param[1], a = param[2])
} ,
"exponentialgeometric" = { ## [R+, 01]
f = VGAM::pexpgeom(y, scale = param[1], shape = param[2])
} ,
"exponentiallogarithmic" = { ## [R+, 01]
f = VGAM::pexplog(y, scale = param[1], shape = param[2])
} ,
"exponentialpoisson" = { ## [R+, R+]
f = VaRES::pexppois(y, b = param[1], lambda = param[2])
} ,
"exponentialpower" = { ## [R, R+, R+]
f = VaRES::pexppower(y, mu = param[1], sigma = param[2], a = param[3])
} ,
"exponentiatedexponential" = { ## [R+, R+]
f = VaRES::pexpexp(y, lambda = param[1], a = param[2])
} ,
"exponentiatedlogistic" = { ## [R+, R+]
f = VaRES::pexplogis(y, a = param[1], b = param[2])
} ,
"exponentiatedweibull" = { ## [R+, R+, R+]
f = VaRES::pexpweibull(y, a = param[1], alpha = param[2], sigma = param[3])
} ,
"F" = { ## [R+, R+]
f = stats::pf(y, df1 = param[1], df2 = param[2])
} ,
"fellerpareto" = { ## [R(mini), R+, R+, R+, R+]
f = actuar::pfpareto(y, min = param[1], shape1 = param[2],
shape2 = param[3], shape3 = param[4],
scale = param[5])
} ,
"fisk" = { ## [R+, R+]
f = VGAM::pfisk(y, scale = param[1], shape1.a = param[2])
} ,
"foldednormal" = { ## [R, R+]
f = VGAM::pfoldnorm(y, mean = param[1], sd = param[2])
} ,
"frechet" = { ## [R+, R, R+]
f = VGAM::pfrechet(y, shape = param[1], location = param[2], scale = param[3])
} ,
"gamma" = { ## [R+, R+]
f = stats::pgamma(y, shape = param[1], scale = param[2])
} ,
"gammapoisson" = { ## [R+, R+]
f_1 = extraDistr::pgpois(y-0.7, shape = param[1], scale = param[2])
f_2 = extraDistr::pgpois(y, shape = param[1], scale = param[2])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"gaussian" = { ## [R, R+]
f = stats::pnorm(y, mean = param[1], sd = param[2])
} ,
"gev" = { ## [R, R+, R]
f = VGAM::pgev(y, location = param[1], scale = param[2], shape = param[3])
} ,
"geninvbeta" = { ## [R+, R+, R+]
f = VaRES::pgeninvbeta(y, a = param[1], c = param[2], d = param[3])
} ,
"genlogis" = { ## [R+, R, R+]
f = VaRES::pgenlogis(y, a = param[1], mu = param[2], sigma = param[3])
} ,
"genlogis3" = { ## [R+, R, R+]
f = VaRES::pgenlogis3(y, a = param[1], mu = param[2], sigma = param[3])
} ,
"genlogis4" = { ## [R+, R+, R, R+]
f = VaRES::pgenlogis4(y, a = param[1], alpha = param[2], mu = param[3], sigma = param[4])
} ,
"genpowerweibull" = { ## [R+, R+]
f = VaRES::pgenpowerweibull(y, a = param[1], theta = param[2])
} ,
"generalizedhyperbolic" = { ## [R, R+, R+, R, R] [mu, delta, alpha, beta, lambda] (avec alpha^2 > beta^2)
f = GeneralizedHyperbolic::pghyp(y, mu = param[1], delta = param[2],
alpha = param[3], beta = param[4],
lambda = param[5])
} ,
"generalizedlambda" = { ## [R, R+, R, R]
f = GLDEX::pgl(y, lambda1 = param[1], lambda2 = param[2], lambda3 = param[3], lambda4 = param[4])
} ,
"generalizedt" = { ## [R, R+, R+, R+]
f = gamlss.dist::pGT(y, mu = param[1], sigma = param[2], nu = param[3], tau = param[4])
} ,
"geometric" = { ## [01]
f_1 = stats::pgeom(y-0.7, prob = param[1])
f_2 = stats::pgeom(y, prob = param[1])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"gompertz" = { ## [R+, R+]
f = ssdtools::pgompertz(y, lscale = param[1], lshape = param[2])
} ,
"gpd" = { ## [R, R+, R]
f = VGAM::pgpd(y, location = param[1], scale = param[2], shape = param[3])
} ,
"gumbel" = { ## [R, R+]
f = VGAM::pgumbel(y, location = param[1], scale = param[2])
} ,
"gumbel2" = { ## [R+, R+]
f = VGAM::pgumbelII(y, scale = param[1], shape = param[2])
} ,
"halfcauchy" = { ## [R+]
f = extraDistr::phcauchy(y, sigma = param[1])
} ,
"halflogistic" = { ## [R+]
f = VaRES::phalflogis(y, lambda = param[1])
} ,
"halfnormal" = { ## [R+]
f = extraDistr::phnorm(y, sigma = param[1])
} ,
"halft" = { ## [R+, R+]
f = extraDistr::pht(y, nu = param[1], sigma = param[2])
} ,
"hjorth" = { ## [R+, R+, R+]
f = rmutil::phjorth(y, m = param[1], s = param[2], f = param[3])
} ,
"hblaplace" = { ## [01, R, R+]
f = VaRES::pHBlaplace(y, a = param[1], theta = param[2], phi = param[3])
} ,
"hyperbolic" = { ## [R, R+, R+, R] [mu, delta, alpha, beta] (avec alpha^2 > beta^2)
f = GeneralizedHyperbolic::phyperb(y, mu = param[1], delta = param[2],
alpha = param[3], beta = param[4])
} ,
"huber" = { ## [R, R+]
f = extraDistr::phuber(y, mu = param[1], sigma = param[2])
} ,
"hzeta" = { ## [R+]
f_1 = VGAM::phzeta(y-0.7, shape = param[1])
f_2 = VGAM::phzeta(y, shape = param[1])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"inversebeta" = { ## [R+, R+]
f = VaRES::pinvbeta(y, a = param[1], b = param[2])
} ,
"inverseburr" = { ## [R+, R+, R+]
f = actuar::pinvburr(y, shape1 = param[1], shape2 = param[2], scale = param[3])
} ,
"inversechisquared" = { ## [R+]
f = extraDistr::pinvchisq(y, nu = param[1])
} ,
"inverseexponential" = { ## [R+]
f = actuar::pinvexp(y, scale = param[1])
} ,
"inverseexpexponential" = { ## [R+, R+]
f = VaRES::pinvexpexp(y, lambda = param[1], a = param[2])
} ,
"inversegamma" = { ## [R+, R+]
f = extraDistr::pinvgamma(y, alpha = param[1], beta = param[2])
} ,
"inverselomax" = { ## [R+, R+]
f = VGAM::pinv.lomax(y, scale = param[1], shape2.p = param[2])
} ,
"inverseparalogistic" = { ## [R+, R+]
f = actuar::pinvparalogis(y, shape = param[1], scale = param[2])
} ,
"inversepareto" = { ## [R+, R+]
f = actuar::pinvpareto(y, shape = param[1], scale = param[2])
} ,
"inversetransformedgamma" = { ## [R+, R+, R+]
f = actuar::pinvtrgamma(y, shape1 = param[1], shape2 = param[2], scale = param[3])
} ,
"inverseweibull" = { ## [R+, R+]
f = actuar::pinvweibull(y, shape = param[1], scale = param[2])
} ,
"kumaraswamy" = { ## [R+, R+]
f = VGAM::pkumar(y, shape1 = param[1], shape2 = param[2])
} ,
"kumaraswamyexponential" = { ## [R+, R+, R+]
f = VaRES::pkumexp(y, lambda = param[1], a = param[2], b = param[3])
} ,
"kumaraswamygamma" = { ## [R+, R+, R+, R+]
f = VaRES::pkumgamma(y, a = param[1], b = param[2], c = param[3], d = param[4])
} ,
"kumaraswamygumbel" = { ## [R+, R+, R, R+]
f = VaRES::pkumgumbel(y, a = param[1], b = param[2], mu = param[3],
sigma = param[4])
} ,
"kumaraswamyhalfnormal" = { ## [R+, R+, R+]
f = VaRES::pkumhalfnorm(y, sigma = param[1], a = param[2], b = param[3])
} ,
"kumaraswamyloglogistic" = { ## [R+, R+, R+, R+]
f = VaRES::pkumloglogis(y, a = param[1], b = param[2], alpha = param[3],
beta = param[4])
} ,
"kumaraswamynormal" = { ## [R, R+, R+, R+]
f = VaRES::pkumnormal(y, mu = param[1], sigma = param[2], a = param[3],
b = param[4])
} ,
"kumaraswamyweibull" = { ## [R+, R+, R+, R+]
f = VaRES::pkumweibull(y, a = param[1], b = param[2], alpha = param[3],
sigma = param[4])
} ,
"laplace" = { ## [R, R+]
f = extraDistr::plaplace(y, mu = param[1], sigma = param[2])
} ,
"levy" = { ## [R, R+]
f = rmutil::plevy(y, m = param[1], s = param[2])
} ,
"linearfailurerate" = { ## [R+, R+]
f = VaRES::plfr(y, a = param[1], b = param[2])
} ,
"lindley" = { ## [R+]
f = VGAM::plind(y, theta = param[1])
} ,
"libbynovickbeta" = { ## [R+, R+, R+]
f = VaRES::pLNbeta(y, lambda = param[1], a = param[2], b = param[3])
} ,
"logcauchy" = { ## [R, R+]
f = VaRES::plogcauchy(y, mu = param[1], sigma = param[2])
} ,
"loggamma" = { ## [R, R+, R+]
f = VGAM::plgamma(y, location = param[1], scale = param[2], shape = param[3])
} ,
"loggumbel" = { ## [R, R+]
f = ssdtools::plgumbel(y, llocation = param[1], lscale = param[2])
} ,
"loglaplace" = { ## [R, R+, R+]
f = VGAM::ploglap(y, location.ald = param[1], scale.ald = param[2], kappa = param[3])
} ,
"loglog" = { ## [R+, >1]
f = VaRES::ploglog(y, a = param[1], lambda = param[2])
} ,
"loglogistic" = { ## [R+, R+]
f = actuar::pllogis(y, shape = param[1], scale = param[2])
} ,
"lognormal" = { ## [R, R+]
f = stats::plnorm(y, meanlog = param[1], sdlog = param[2])
} ,
"lognormal3" = { ## [R, R+, R]
f = EnvStats::plnorm3(y, meanlog = param[1], sdlog = param[2], threshold = param[3])
} ,
"logistic" = { ## [R, R+]
f = stats::plogis(y, location = param[1], scale = param[2])
} ,
"logisticexponential" = { ## [R+, R+]
f = VaRES::plogisexp(y, lambda = param[1], a = param[2])
} ,
"logisticrayleigh" = { ## [R+, R+]
f = VaRES::plogisrayleigh(y, a = param[1], lambda = param[2])
} ,
"logseries" = { ## [01]
f_1 = extraDistr::plgser(y-0.7, theta = param[1])
f_2 = extraDistr::plgser(y, theta = param[1])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"lomax" = { ## [R+, R+]
f = VGAM::plomax(y, scale = param[1], shape3.q = param[2])
} ,
"makeham" = { ## [R+, R+, R+]
f = VGAM::pmakeham(y, scale = param[1], shape = param[2], epsilon = param[3])
} ,
"maxwell" = { ## [R+]
f = VGAM::pmaxwell(y, rate = param[1])
} ,
"mcgilllaplace" = { ## [R, R+, R+]
f = VaRES::pMlaplace(y, theta = param[1], phi = param[2], psi = param[3])
} ,
"moexponential" = { ## [R+, R+]
f = VaRES::pmoexp(y, lambda = param[1], a = param[2])
} ,
"moweibull" = { ## [R+, R+, R+]
f = VaRES::pmoweibull(y, a = param[1], b = param[2], lambda = param[3])
} ,
"nakagami" = { ## [R+, R+]
f = VGAM::pnaka(y, scale = param[1], shape = param[2])
} ,
"ncchisquared" = { ## [R+, R+]
f = stats::pchisq(y, df = param[1], ncp = param[2])
} ,
"ncF" = { ## [R+, R+, R+]
f = stats::pf(y, df1 = param[1], df2 = param[2], ncp = param[3])
} ,
"negativebinomial" = { ## [N+, 01]
f_1 = stats::pnbinom(y-0.7, size = size, prob = param[1])
f_2 = stats::pnbinom(y, size = size, prob = param[1])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"normalinversegaussian" = { ## [R, R, R+, R]
f = GeneralizedHyperbolic::pnig(y, mu = param[1], delta = param[2],
alpha = param[3], beta = param[4])
} ,
"nsbeta" = { ## [R+, R+, R(min), R(maxi)]
f = extraDistr::pnsbeta(y, shape1 = param[1], shape2 = param[2],
min = param[3], max = param[4])
} ,
"paralogistic" = { ## [R+, R+]
f = VGAM::pparalogistic(y, scale = param[1], shape1.a = param[2])
} ,
"pareto" = { ## [R+, R+]
f = extraDistr::ppareto(y, a = param[1], b = min(y))
} ,
"paretopositivestable" = { ## [R+, R+, R+]
f = VaRES::pparetostable(y, lambda = param[1], nu = param[2], sigma = param[3])
} ,
"pareto1" = { ## [R+, R+]
f = VGAM::pparetoI(y, scale = param[1], shape = param[2])
} ,
"pareto2" = { ## [R, R+, R+]
f = VGAM::pparetoII(y, location = param[1], scale = param[2], shape = param[3])
} ,
"pareto3" = { ## [R, R+, R+]
f = VGAM::pparetoIII(y, location = param[1], scale = param[2], inequality = param[3])
} ,
"pareto4" = { ## [R, R+, R+, R+]
f = VGAM::pparetoIV(y, location = param[1], scale = param[2], inequality = param[3], shape = param[4])
} ,
"perks" = { ## [R+, R+]
f = VGAM::pperks(y, scale = param[1], shape = param[2])
} ,
"pctalaplace" = { ## [R+, R]
f = VaRES::pPCTAlaplace(y, a = param[1], theta = param[2])
} ,
"poisson" = { ## [R+]
f_1 = stats::ppois(y-0.7, lambda = param[1])
f_2 = stats::ppois(y, lambda = param[1])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
"power1" = { ## [R+]
f = VaRES::ppower1(y, a = param[1])
} ,
"power2" = { ## [R+]
f = VaRES::ppower2(y, b = param[1])
} ,
"powerdistribution" = { ## [R+, R+]
f = extraDistr::ppower(y, alpha = param[1], beta = param[2])
} ,
"powerexponential" = { ## [R, R+, R+]
f = rmutil::ppowexp(y, m = param[1], s = param[2], f = param[3])
} ,
"rayleigh" = { ## [R+]
f = VGAM::prayleigh(y, scale = param[1])
} ,
"reflectedgamma" = { ## [R+, R, R+]
f = VaRES::prgamma(y, a = param[1], theta = param[2], phi = param[3])
} ,
"rice" = { ## [R+, R+]
f = VGAM::price(y, sigma = param[1], vee = param[2])
} ,
"scaledchisquared" = { ## [R+, R+]
f = extraDistr::pinvchisq(y, nu = param[1], tau = param[2])
} ,
"schabe" = { ## [R+, R+]
f = VaRES::pschabe(y, gamma = param[1], theta = param[2])
} ,
"simplex" = { ## [01, R+]
f = rmutil::psimplex(y, m = param[1], s = param[2])
} ,
"skewedlaplace" = { ## [R, R+, R+]
f = rmutil::pskewlaplace(y, m = param[1], s = param[2], f = param[3])
} ,
"skewedt" = { ## [R+, R+]
f = skewt::pskt(y, df = param[1], gamma = param[2])
} ,
"skewedtfourparam" = { ## [R, R+, R, R+(<25)]
f = sn::pst(y, xi = param[1], omega = param[2], alpha = param[3], nu = param[4])
} ,
"skewednormal" = { ## [R, R+, R, R]
f = sn::psn(y, xi = param[1], omega = param[2], alpha = param[3])
} ,
"skewedgeneralizedt" = { ## [R, R+, -1+1, R+(>1), R+(>1)]
f = sgt::psgt(y, mu = param[1], sigma = param[2], lambda = param[3], p = param[4])
} ,
"skewedexponentialpower" = { ## [R, R+, R, R+]
f = gamlss.dist::pSEP(y, mu = param[1], sigma = param[2], nu = param[3], tau = param[4])
} ,
"slash" = { ## [R, R+]
f = extraDistr::pslash(y, mu = param[1], sigma = param[2])
} ,
"stable" = { ## [02, -1+1, R+, R]
f = stabledist::pstable(y, alpha = param[1], beta = param[2], gamma = param[3],
delta = param[4])
} ,
"stacy" = { ## [R+, R+, R+]
f = VaRES::pstacygamma(y, gamma = param[1], c = param[2], theta = param[3])
} ,
"t" = { ## [R, R+, R+]
f = stats::pt((y-param[1])/param[2], df = param[3])
} ,
"tobit" = { ## [R, R+]
f = VGAM::ptobit(y, mean = param[1], sd = param[2])
} ,
"topple" = { ## [01]
f = VGAM::ptopple(y, shape = param[1])
} ,
"transformedbeta" = { ## [R+, R+, R+, R+]
f = actuar::ptrbeta(y, shape1 = param[1], shape2 = param[2], shape3 = param[3],
scale = param[4])
} ,
"transformedgamma" = { ## [R+, R+, R+]
f = actuar::ptrgamma(y, shape1 = param[1], shape2 = param[2], scale = param[3])
} ,
"truncatednormal" = { ## [R, R+, R(min), R(max)]
f = extraDistr::ptnorm(y, mean = param[1], sd = param[2], a = param[3],
b = param[4])
} ,
"truncatedpareto" = { ## [R+(mini), R+(maxi), R+]
f = VGAM::ptruncpareto(y, lower = param[1], upper = param[2], shape = param[3])
} ,
"twosidedpower" = { ## [01, R+]
f = rmutil::ptwosidedpower(y, m = param[1], s = param[2])
} ,
"wald" = { ## [R, R+]
f = extraDistr::pwald(y, mu = param[1], lambda = param[2])
} ,
"weibull" = { ## [R+, R+]
f = stats::pweibull(y, shape = param[1], scale = param[2])
} ,
"xie" = { ## [R+, R+, R+]
f = VaRES::pxie(y, a = param[1], b = param[2], lambda = param[3])
} ,
"yules" = { ## [R+] mais > 0.5
f_1 = VGAM::pyules(y, shape = param[1])
f_2 = VGAM::pyules(y, shape = param[1])
f = (1-u_Ros)*f_1 + u_Ros*f_2
} ,
)
f[is.nan(f)] <- 0
return(f)
}
pgeometricprog<-function(y,prob){
f = matrix(0, nrow = length(y), ncol = 1)
for (i in 1:length(y)){
if (y[1]>=0){
f[i] = 1 - (1-prob)^(y[i]+1)
}
}
return(f)
}
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