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# Demo for Zero-Inflated Poisson.
# Far more flexible is gaitdnbinomial().
library("VGAM")
set.seed(111)
zdata <- data.frame(x2 = runif(nn <- 1000))
zdata <-
transform(zdata,
pstr01 = logitlink(-0.5 + 1*x2, inv = TRUE),
pstr02 = logitlink( 0.5 - 1*x2, inv = TRUE),
Ps01 = logitlink(-0.5 , inv = TRUE),
Ps02 = logitlink( 0.5 , inv = TRUE),
lambda1 = loglink(-0.5 + 2*x2, inv = TRUE),
lambda2 = loglink( 0.5 + 2*x2, inv = TRUE))
zdata <- transform(zdata, y1 = rzipois(nn, lambda1, pstr0 = Ps01),
y2 = rzipois(nn, lambda2, pstr0 = Ps02))
with(zdata, table(y1)) # Eyeball the data
with(zdata, table(y2))
with(zdata, stem(y2))
fit1 <- vglm(y1 ~ x2, zipoisson(zero = 1), zdata, crit = "coef")
fit2 <- vglm(y2 ~ x2, zipoisson(zero = 1), zdata, crit = "coef")
coef(fit1, matrix = TRUE) # Agrees with the above values
coef(fit2, matrix = TRUE) # Agrees with the above values
head(fit1@misc$pobs0) # The estimate of P(Y=0)
coef(fit1)
coef(fit1, matrix = TRUE)
Coef(fit1)
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