# Simulation from a spatial model Simulate from a variety of spatial models. ```r rsglmm( n, formula, family = "gaussian", data, weights, subset, offset, atsample, beta, linkp, phi, omg, kappa, ssq, corrfcn = "matern", longlat = FALSE, dispersion = 1, returnGRF = FALSE, warndisp = TRUE ) rstrga( n, formula, data, weights, subset, offset, atsample, beta, linkp, phi, omg, kappa, ssq, corrfcn = "matern", longlat = FALSE, dispersion = 1, returnGRF = FALSE ) rsgrf( n, formula, data, subset, offset, atsample, beta, phi, omg, kappa, ssq, corrfcn = "matern", longlat = FALSE ) ``` ## Arguments - `n`: The number of instances to simulate - `formula`: A representation of the model in the form `response ~ terms`. The LHS can be omitted. If the LHS exists, it can be of the form `y`, `y|z`, or sums of terms at either side of the `|` to specify the names of the variables to include in the data frame. - `family`: The distribution of the data to simulate from. - `data`: An optional data frame containing the variables in the model. - `weights`: An optional vector of weights. Number of replicated samples for Gaussian and gamma, number of trials for binomial, time length for Poisson. - `subset`: An optional set of indices. Simulations will be provided for those locations only. - `offset`: See `lm`. - `atsample`: A formula of the form `~ Xcoord + Ycoord` specifying the sampled locations. - `beta`: A vector of the regressor coefficents to use. - `linkp`: The link function parameter. - `phi`: The spatial range parameter. - `omg`: The relative nugget parameter. - `kappa`: The spatial smoothness parameter. - `ssq`: The partial sill parameter. - `corrfcn`: The correlation function to use. - `longlat`: How to compute the distance between locations. If `FALSE`, Euclidean distance, if `TRUE` Great Circle distance. See `spDists`. - `dispersion`: The fixed dispersion parameter. When this is not 1 and the sample is from a binomial or a Poisson distribution, no such distribution exists so an approximate sample is returned. Use with caution. - `returnGRF`: Whether to return the simulate Gaussian random field as well. - `warndisp`: Whether to warn when sampling from a quasi distribution. This is the case for binomial and Poisson when the dispersion is not one. ## Returns A data frame containing the predictors, sampling locations, optional weights, and samples. ## Details The spatial Gaussian random field is simulated using the Cholesky decomposition of the covariance matrix. The sample from a quasi distribution uses a hack which matches the mean and the variance of the distribution. See the source code for details. ## Examples ```r ## Not run: n <- 100 beta <- c(-2, 1) phi <- .2 omg <- .3 linkp <- 0 ssq <- 1 l <- rep(10, n) corrf <- "matern" kappa <- .5 family <- "poisson" Xcoord <- runif(n) Ycoord <- runif(n) f <- Xcoord + Ycoord formula <- y|z ~ f mydata <- rsglmm(1, formula, family, weights = l, atsample = ~ Xcoord + Ycoord, beta = beta, linkp = linkp, phi = phi, omg = omg, kappa = kappa, ssq = ssq, corrfcn = corrf, returnGRF = TRUE) ## End(Not run) ```

rsglmm function