gyulemle {degreenet} | R Documentation |
Functions to Estimate Parametric Discrete Probability Distributions via maximum likelihood Based on categorical response
gyulemle(x, cutoff = 1, cutabove = 1000, guess = 3.5, conc = FALSE, method = "BFGS", hellinger = FALSE, hessian=TRUE)
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. |
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. |
result |
vector of parameter estimates - lower 95% confidence value, upper 95% confidence value, the PDF MLE, the asymptotic standard error, and the number of data values >=cutoff and <=cutabove. |
theta |
The Yule MLE of the PDF exponent. |
value |
The maximized value of the function. |
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.
# # 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 AICC and BIC for the two models # llgyuleall(v=s4est$theta,x=s4) llgwarall(v=s4warest$theta,x=s4)