# Purpose: impliment the ideas in note by Vladas
# W-12-23-19-psuedo likelihood.pdf
n <- 100
k.tru <- 3
p.tru <- 1/4
phi.tru <- 0.5
lag.max <- 20
# only let phi be a free parameter
phi.seq <- seq(.4, .6, length.out = 50)
V <- c()
for(i in 1:length(phi.seq)){
phi <- phi.seq[i]
V[i] <- myV_negBinom(size.tru = 3,
prob.tru = 1/4,
phi.tru = 0.5,
size.free = 3,
prob.free = 1/4,
phi.free = phi)
}
plot(V~phi.seq)
phi.seq[which.min(V)]
# Only let size be a free parameter
size.seq <- seq(2.8, 3.2, length.out = 50)
V <- c()
for(i in 1:length(size.seq)){
size <- size.seq[i]
V[i] <- myV_negBinom(size.tru = 3,
prob.tru = 1/4,
phi.tru = 0.5,
size.free = size,
prob.free = 1/4,
phi.free = 0.5)
}
plot(V~size.seq)
size.seq[which.min(V)]
# Do for all Free parameters concurrently
phi.seq <- seq(.4, .6, length.out = 10)
size.seq <- seq(2.8, 3.2, length.out = 10)
prob.seq <- seq(.2, .3, length.out = 10)
M <- matrix(ncol = 4)
colnames(M) <- c("size", "prob", "phi", "V")
V.tru <- myV_negBinom(
size.tru = 3,
prob.tru = 1/4,
phi.tru = 0.5,
size.free = 3,
prob.free = 1/4,
phi.free = 0.5
)
M[1, ] <- c(3, 1/4, 0.5, V.tru)
for(k in 1:length(size.seq)){
print(k)
for(i in 1:length(prob.seq)){
for(j in 1:length(phi.seq)){
size <- size.seq[k]
prob <- prob.seq[i]
phi <- phi.seq[j]
V <- myV_negBinom(
size.tru = 3,
prob.tru = 1/4,
phi.tru = 0.5,
size.free = size,
prob.free = prob,
phi.free = phi
)
M <- rbind(M, c(size, prob, phi, V))
}
}}
M[which.min(M[, 4]), ]
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