marp (version 0.1.0)

loglogis_rp: A function to fit Log-Logistics renewal model

Description

A function to fit Log-Logistics renewal model

Usage

loglogis_rp(data, t, m, y)

Value

returns list of estimates after fitting Log-Logistics renewal model

par1

Estimated shape parameter of the Log-Logistics model

par2

Estimated scale parameter of the Log-Logistics model

logL

Negative log-likelihood

AIC

Akaike information criterion (AIC)

BIC

Bayesian information criterion (BIC)

mu_hat

Estimated mean

pr_hat

Estimated (logit) probabilities

haz_hat

Estimated (log) hazard rates

Arguments

data

input inter-event times

t

user-specified time intervals (used to compute hazard rate)

m

the number of iterations in nlm

y

user-specified time point (used to compute time-to-event probability)

Examples

set.seed(42)
data <-  rgamma(100,3,0.01)

# set some parameters
m = 10  # number of iterations for MLE optimization
t = seq(100, 200, by=10)  # time intervals
y = 304  # cut-off year for estimating probablity

# fit Log-Logistic renewal model
result <- marp::loglogis_rp(data, t, m, y)

# print result
cat("par1 = ", result$par1, "\n")
cat("par2 = ", result$par2, "\n")
cat("logL = ", result$logL, "\n")
cat("AIC = ", result$AIC, "\n")
cat("BIC = ", result$BIC, "\n")
cat("mu_hat = ", result$mu_hat, "\n")
cat("pr_hat = ", result$pr_hat, "\n")