The AR-1 Autoregressive Process
Density for the AR-1 model.
dAR1(x, drift = 0, var.error = 1, ARcoef1 = 0.0,
type.likelihood = c("exact", "conditional"), log = FALSE)x, |
vector of quantiles. |
drift |
the scaled mean (also known as the drift parameter), mu^*. Note that the mean is mu^* / (1-rho). The default corresponds to observations that have mean 0. |
log |
Logical.
If |
type.likelihood, var.error, ARcoef1 |
See |
Most of the background to this function is given
in AR1.
All the arguments are converted into matrices, and then
all their dimensions are obtained. They are then coerced
into the same size: the number of rows is the maximum
of all the single rows, and ditto for the number of columns.
dAR1 gives the density.
T. W. Yee and Victor Miranda
AR1.
nn <- 100; set.seed(1)
tdata <- data.frame(index = 1:nn,
TS1 = arima.sim(nn, model = list(ar = -0.50),
sd = exp(1)))
fit1 <- vglm(TS1 ~ 1, AR1, data = tdata, trace = TRUE)
rhobitlink(-0.5)
coef(fit1, matrix = TRUE)
(Cfit1 <- Coef(fit1))
summary(fit1) # SEs are useful to know
logLik(fit1)
sum(dAR1(depvar(fit1), drift = Cfit1[1], var.error = (Cfit1[2])^2,
ARcoef1 = Cfit1[3], log = TRUE))
fit2 <- vglm(TS1 ~ 1, AR1(type.likelihood = "cond"), data = tdata, trace = TRUE)
(Cfit2 <- Coef(fit2)) # Okay for intercept-only models
logLik(fit2)
head(keep <- dAR1(depvar(fit2), drift = Cfit2[1], var.error = (Cfit2[2])^2,
ARcoef1 = Cfit2[3], type.likelihood = "cond", log = TRUE))
sum(keep[-1])Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.