View source: R/convCount_moments.R
evCount_conv_bi | R Documentation |
Compute numerically expected values and variances of renewal count processes.
evCount_conv_bi( xmax, distPars, dist = c("weibull", "gamma", "gengamma", "burr"), method = c("dePril", "direct", "naive"), nsteps = 100, time = 1, extrap = TRUE ) evCount_conv_user( xmax, distPars, extrapolPars, survR, method = c("dePril", "direct", "naive"), nsteps = 100, time = 1, extrap = TRUE )
xmax |
unsigned integer maximum count to be used. |
distPars |
TODO |
dist |
TODO |
method |
TODO |
nsteps |
unsiged integer, number of steps used to compute the integral. |
time |
double, time at wich to compute the probabilities. Set to 1 by default. |
extrap |
logical, if |
extrapolPars |
ma::vec of length 2. The extrapolation values. |
survR |
function, user supplied survival function; should have signature
|
evCount_conv_bi
computes the expected value and variance of renewal
count processes for the builtin distirbutions of inter-arrival times.
evCount_conv_user
computes the expected value and variance for a user
specified distirbution of the inter-arrival times.
a named list with components "ExpectedValue"
and "Variance"
.
pwei_user <- function(tt, distP) { alpha <- exp(-log(distP[["scale"]]) / distP[["shape"]]) pweibull(q = tt, scale = alpha, shape = distP[["shape"]], lower.tail = FALSE) } ## ev convolution Poisson count lambda <- 2.56 beta <- 1 distPars <- list(scale = lambda, shape = beta) evbi <- evCount_conv_bi(20, distPars, dist = "weibull") evu <- evCount_conv_user(20, distPars, c(2, 2), pwei_user, "dePril") c(evbi[["ExpectedValue"]], lambda) c(evu[["ExpectedValue"]], lambda ) c(evbi[["Variance"]], lambda ) c(evu[["Variance"]], lambda ) ## ev convolution weibull count lambda <- 2.56 beta <- 1.35 distPars <- list(scale = lambda, shape = beta) evbi <- evCount_conv_bi(20, distPars, dist = "weibull") evu <- evCount_conv_user(20, distPars, c(2.35, 2), pwei_user, "dePril") x <- 1:20 px <- dCount_conv_bi(x, distPars, "weibull", "dePril", nsteps = 100) ev <- sum(x * px) var <- sum(x^2 * px) - ev^2 c(evbi[["ExpectedValue"]], ev) c(evu[["ExpectedValue"]], ev ) c(evbi[["Variance"]], var ) c(evu[["Variance"]], var )
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