---
title: "Simulating MCGF and RS-MCGF Processes"
output: rmarkdown::html_vignette
bibliography: mcgf.bib
vignette: >
  %\VignetteIndexEntry{simulation}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>"
)
```

# Overview

`mcgf` can simulate Markov chain Gaussian fields (MCGF) and
regime-switching Markov chain Gaussian fields (RS-MCGF) directly from
user-specified covariance parameters. Simulation is useful for method
validation, sensitivity analysis, and learning the package workflow before
working with observed data.

This vignette focuses on the arguments that are most important in practice.

# Distances

Start by defining the spatial locations and their signed distance matrices.

```{r distances}
library(mcgf)

locations <- matrix(
    c(0, 0, 2, 0, 1, 1.5),
    ncol = 2,
    byrow = TRUE,
    dimnames = list(paste0("S", 1:3), c("x", "y"))
)

d <- find_dists(locations, longlat = FALSE)
d
```

`d$h` is Euclidean distance, while `d$h1` and `d$h2` retain horizontal and
vertical signs. The signed distances are needed by the Lagrangian models.

# Simulate an MCGF

We first define a separable base covariance model.

```{r base-parameters}
par_base <- list(
    par_s = list(
        nugget = 0.05,
        c = 0.1,
        gamma = 0.5
    ),
    par_t = list(
        a = 0.2,
        alpha = 0.5
    )
)
```

A base-only first-order MCGF can be simulated by setting
`lagrangian = "none"` and `lambda = 0`.

```{r simulate-base}
set.seed(1)

x_base <- mcgf_sim(
    N = 200,
    base = "sep",
    lagrangian = "none",
    par_base = par_base,
    par_lagr = NULL,
    lambda = 0,
    dists = d,
    lag = 1
)

dim(x_base)
```

The first `lag + horizon` rows are initialization rows. If you want a sample
containing only generated observations, remove them before analysis.

```{r remove-init}
lag <- 1
horizon <- 1

x_base_use <- x_base[-seq_len(lag + horizon), , drop = FALSE]
```

# Add a Lagrangian component

The Lagrangian term models directional space-time asymmetry. For example:

```{r lagr-parameters}
par_lagr <- list(v1 = 2, v2 = 1, k = 3)
```

```{r simulate-lagr}
set.seed(2)

x_lagr <- mcgf_sim(
    N = 200,
    base = "sep",
    lagrangian = "lagr_tri",
    par_base = par_base,
    par_lagr = par_lagr,
    lambda = 0.2,
    dists = d,
    lag = 2
)

dim(x_lagr)
```

`lambda` controls the contribution of the Lagrangian term. A value close to
zero gives a model dominated by the symmetric base covariance; a larger value
places more weight on directional advection.

# Simulate an RS-MCGF

For a regime-switching process, first supply a regime label for every generated
time point.

```{r regimes}
N <- 200
label <- rep(c(1, 2), each = N / 2)
table(label)
```

Model arguments ending in `_ls` are lists. A length-one list can often be
reused across regimes; use one element per regime when parameters differ.

```{r rs-parameters}
par_base_1 <- par_base
par_base_2 <- list(
    par_s = list(
        nugget = 0.05,
        c = 0.2,
        gamma = 0.5
    ),
    par_t = list(
        a = 0.5,
        alpha = 0.7
    )
)
```

```{r simulate-rs}
set.seed(3)

x_rs <- mcgf_rs_sim(
    N = N,
    label = label,
    base_ls = list("sep"),
    lagrangian_ls = list("none"),
    par_base_ls = list(par_base_1, par_base_2),
    par_lagr_ls = list(NULL),
    lambda_ls = list(0),
    dists_ls = list(d),
    lag_ls = list(1, 1)
)

head(x_rs)
```

The first column contains the regime label and the remaining columns contain
the simulated spatial process.

# Returning covariance details

Set `return_all = TRUE` when you also want the covariance matrices and lag
arrays used internally:

```{r return-all}
set.seed(4)

out <- mcgf_sim(
    N = 50,
    base = "sep",
    lagrangian = "none",
    par_base = par_base,
    par_lagr = NULL,
    lambda = 0,
    dists = d,
    lag = 1,
    return_all = TRUE
)

names(out)
names(out$par)
```

This is particularly useful when checking a simulation design or comparing the
implied covariance with an empirical estimate.

# Common choices

- `lag` determines the Markov order.
- `horizon` controls how many steps are generated jointly at each update.
- `sd` controls marginal standard deviations by location.
- `scale_time` rescales temporal lags before evaluating the covariance.
- `mu_c` and `mu_p` allow nonzero conditional means.
- `init` can supply explicit starting values.

For the covariance functions themselves, see
`vignette("correlation-models", package = "mcgf")`.

# References

<div id="refs"></div>
