SpatialFloor (version 1.1.0)

iso_simulation_p_rep: A simulation fucntion for isotrophic spatial data wrt/ p-rep data

Description

The function simulates spatial data by given parameters

Usage

iso_simulation_p_rep(n.row, n.col, lon.lat = FALSE, mile = FALSE,
  density.choice, density.layout = c("diagSet", "random", "equal_space"), h2,
  sigma_env, sigma_variety = (sigma_env * h2)/(1 - h2), mu_variety,
  cov_fun = "exp", ranges = sqrt(2), simulation = 3, nugget = 0,
  mu_floor = 0)

Arguments

n.row

(Mandatory) The row of the field grid

n.col

(Mandatory) The column of the fields grid

lon.lat

(Optional) The distance calculation is based on earth distance (if TRUE), default is FALSE

mile

(Optional) The distance calculation is based on milage (if TRUE), default is FALSE

density.choice

(Mandatory) The density of the replicated entries (range from 0 to 1, if 'diagonal'; from 0 to 0.34, if 'diagSet'), EVEN number ONLY

density.layout

(Optional) The layout pattern of density, default are: 'diagonal', 'diagSet', 'random', 'equal_space'. The diagSet is recommended over the diagonal at density from 0.01 to 0.35.

h2

(Mandatory) The heritability of the simulated data, h2 = sigma_variety / (sigma_variety + sigma_env)

sigma_env

(Mandatory) The variance of environment, (equilievent of sill)

sigma_variety

(Optional) The variance of entry, default sigma_variety = (sigma_env * h2) / (1 - h2)

mu_variety

(Mandatory) The mean of the variety

cov_fun

(Optional) The var-cov function ('exp' or 'gau'), default is 'exp', exponential

ranges

(Optional) The range parameter of spatial distribution, default sqrt(2)

simulation

(Optional) The number of simulated data returned, default 3

nugget

(Optional) The nugget parameter in spatial simulation, default 0

mu_floor

(Optional) The mean of the spatial floor, default 0

Examples

# NOT RUN {
temp.1 = iso_simulation_p_rep(10, 10, density.choice=c(0.04,0.2)
			, h2=0.5, sigma_env=100, mu_variety=300)
temp.2 = iso_simulation_p_rep(10, 10, density.choice=c(0.04,0.2)
			, h2=0.5, sigma_env=100, mu_variety=300)
# }