Description Usage Arguments Value Note References See Also Examples
Functions to Estimate the Conditional Log-likelihood for Discrete Probability Distributions. The likelihood is calcualted condition on the count being at least the cutoff value and less than or equal to the cutabove value.
1 |
v |
A vector of parameters for the Yule (a 1-vector - the scaling exponent). |
x |
A vector of categories for counts (one per observation). The values of |
cutoff |
Calculate estimates conditional on exceeding this value. |
cutabove |
Calculate estimates conditional on not exceeding this value. |
xr |
range of count values to use to approximate the set of all realistic counts. |
hellinger |
Calculate the Hellinger distance of the parametric model from the data instead of the log-likelihood? |
the log-likelihood for the data x
at parameter value v
(or the Hellinder distance if hellinger=TRUE
).
See the working papers on http://www.csss.washington.edu/Papers for details
Jones, J. H. and Handcock, M. S. "An assessment of preferential attachment as a mechanism for human sexual network formation," Proceedings of the Royal Society, B, 2003, 270, 1123-1128.
gyulemle, llgyuleall, dyule
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | #
# Simulate a Yule distribution over 100
# observations with rho=4.0
#
set.seed(1)
s4 <- simyule(n=100, rho=4)
table(s4)
#
# Recode it as categorical
#
s4[s4 > 4 & s4 < 11] <- 5
s4[s4 > 100] <- 8
s4[s4 > 20] <- 7
s4[s4 > 10] <- 6
#
# Calculate the MLE and an asymptotic confidence
# interval for rho
#
s4est <- gyulemle(s4)
s4est
#
# Calculate the MLE and an asymptotic confidence
# interval for rho under the Waring model (i.e., rho=4, p=2/3)
#
s4warest <- gwarmle(s4)
s4warest
#
# Compare the log-likelihoods for the two models
#
llgyule(v=s4est$theta,x=s4)
llgwar(v=s4warest$theta,x=s4)
|
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