llgyule {degreenet} | R Documentation |
Calculate the Conditional log-likelihood for Count Distributions
Description
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.
Usage
llgyule(v, x, cutoff=1,cutabove=1000,xr=1:10000,hellinger=FALSE)
Arguments
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? |
Value
the log-likelihood for the data x
at parameter value v
(or the Hellinder distance if hellinger=TRUE
).
Note
See the papers on https://handcock.github.io/?q=Holland for details
References
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.
See Also
gyulemle, llgyuleall, dyule
Examples
#
# 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)