# AR1UC: The AR-1 Autoregressive Process In VGAM: Vector Generalized Linear and Additive Models

 dAR1 R Documentation

## The AR-1 Autoregressive Process

### Description

Density for the AR-1 model.

### Usage

```dAR1(x, drift = 0, var.error = 1, ARcoef1 = 0.0,
type.likelihood = c("exact", "conditional"), log = FALSE)
```

### Arguments

 `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 `TRUE` then the logarithm of the density is returned. `type.likelihood, var.error, ARcoef1` See `AR1`. The argument `ARcoef1` is rho. The argument `var.error` is the variance of the i.i.d. random noise, i.e., sigma^2. If `type.likelihood = "conditional"` then the first element or row of the result is currently assigned `NA`—this is because the density of the first observation is effectively ignored.

### Details

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.

### Value

`dAR1` gives the density.

### Author(s)

T. W. Yee and Victor Miranda

`AR1`.

### Examples

```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)
coef(fit1, matrix = TRUE)
(Cfit1 <- Coef(fit1))
summary(fit1)  # SEs are useful to know
logLik(fit1)
sum(dAR1(depvar(fit1), drift = Cfit1, var.error = (Cfit1)^2,
ARcoef1 = Cfit1, 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, var.error = (Cfit2)^2,
ARcoef1 = Cfit2, type.likelihood = "cond", log = TRUE))
sum(keep[-1])
```

VGAM documentation built on July 6, 2022, 5:05 p.m.