awarmle {degreenet} | R Documentation |
Functions to Estimate the Waring Discrete Probability Distribution via maximum likelihood.
awarmle(x, cutoff = 1, cutabove = 1000, guess = c(3.5,0.1), method = "BFGS", conc = FALSE, hellinger = FALSE, hessian=TRUE)
x |
A vector of counts (one per observation). |
cutoff |
Calculate estimates conditional on exceeding this value. |
cutabove |
Calculate estimates conditional on not exceeding this value. |
guess |
Initial estimate at the MLE. |
conc |
Calculate the concentration index of the distribution? |
method |
Method of optimization. See "optim" for details. |
hellinger |
Minimize Hellinger distance of the parametric model from the data instead of maximizing the likelihood. |
hessian |
Calculate the hessian of the information matrix (for use with calculating the standard errors. |
theta |
vector of MLE of the parameters. |
asycov |
asymptotic covariance matrix. |
asycor |
asymptotic correlation matrix. |
se |
vector of standard errors for the MLE. |
conc |
The value of the concentration index (if calculated). |
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.
ayulemle, awarmle, simwar
# Simulate a Waring distribution over 100 # observations with a PDf exponent of 3.5 and a # probability of including a new actor of 0.1 set.seed(1) s4 <- simwar(n=100, v=c(3.5,0.1)) table(s4) # # Calculate the MLE and an asymptotic confidence # interval for the parameters # s4est <- awarmle(s4) s4est # Calculate the MLE and an asymptotic confidence # interval for rho under the Yule model # s4yuleest <- ayulemle(s4) s4yuleest # # Compare the AICC and BIC for the two models # llwarall(v=s4est$theta,x=s4) llyuleall(v=s4yuleest$theta,x=s4)