dWeibullCount_mat | R Documentation |
Univariate Weibull count probability computed using matrix techniques.
dWeibullCount_mat(x, shape, scale, time = 1, logFlag = FALSE, jmax = 50L) dWeibullCount_acc( x, shape, scale, time = 1, logFlag = FALSE, jmax = 50L, nmax = 300L, eps = 1e-10, printa = FALSE )
x |
integer (vector), the desired count values. |
shape |
numeric (length 1), shape parameter of the Weibull count. |
scale |
numeric (length 1), scale parameter of the Weibull count. |
time |
double, length of the observation window (defaults to 1). |
logFlag |
logical, if TRUE, the log of the probability will be returned. |
jmax |
integer, number of terms used to approximate the (infinite) series. |
nmax |
integer, an upper bound on the number of terms to be summed in the Euler-van Wijngaarden sum; default is 300 terms. |
eps |
numeric, the desired accuracy to declare convergence. |
printa |
logical, if |
dWeibullCount_mat
implements formulae (11) of McShane(2008) to
compute the required probabilities. For speed, the computations are
implemented in C++ and of matrix computations are used whenever possible.
This implementation is not efficient as it recomputes the alpha
matrix each time, which may slow down computation (among other things).
dWeibullCount_acc
achieves a vast (several orders of magnitude) speed
improvement over pWeibullCountOrig
. We achieve this by using Euler-van
Wijngaarden techniques for accelerating the convergence of alternating series
and tabulation of the alpha terms available in a pre-computed matrix (shipped
with the package).
When computation time is an issue, we recommend the use of
dWeibullCount_fast
. However, pWeibullCountOrig
may be more
accurate, especially when jmax
is large.
a vector of probabilities for each component of the count vector
x
.
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