evCount_conv_bi {Countr} | R Documentation |
Expected value and variance of renewal count process
Description
Compute numerically expected values and variances of renewal count processes.
Usage
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
)
Arguments
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
|
Details
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.
Value
a named list with components "ExpectedValue"
and "Variance"
.
Examples
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 )