snapKrig (version 0.0.2)

sk_plot_semi: Plot a semi-variogram

Description

Plots a sample semi-variogram using the point pair difference data in vg. Binned summary statistics are drawn as circles with size scaled to the sample sizes. A covariance model (pars) is optionally drawn over the sample data as a ribbon plot.

Usage

sk_plot_semi(vg, pars = NULL, add = FALSE, fun = "classical", ...)

Value

nothing

Arguments

vg

data frame of sample absolute differences, with columns 'dabs', 'd' (and 'bin')

pars

list of the form returned by sk_pars with entries 'y', 'x', 'eps', 'psill'

add

logical, indicates to draw on an existing plot rather than create a new one

fun

character or function, the aggregation function (see details)

...

further plotting parameters (see below)

Plotting parameters

The following style parameters are optional:

alpha_bin, alpha_model, alpha_bin_b, alpha_model_b

numeric in [0,1]: respectively, the transparency of the fill color for circles and ribbons (default 0.3), and their borders (default 0.5 )

bty

character: plot frame border type, passed to base::plot (default 'n' for no border)

col_bin, col_model

character: respectively, the color to use for circles (default 'black') and ribbons (default 'blue')

cex_bin

numeric > 0: scaling factor for circles (default 1.5)

d_max

numeric > 0: x (distance) limit for plotting in input units

leg

logical: adds a sample bin legend

leg_main

character: title for the sample bin legend (default 'model')

lwd

numeric: line width for the model semi-variance

main

character: a title

n_bin, n_test

integer: respectively, the number of distance bins for the sample (optional if vg has a 'bin' column, and ignored if vg is a grid object), and the number of distances at which to evaluate the semi-variogram for model pars (default 5e3, ignored if pars not supplied)

reset

logical: indicates to reset graphical parameters to their original values when finished (default TRUE)

xlab, ylab

character: titles for the y and x axes. The default for y is 'semi-variance (gamma)', and for x 'distance'

Details

If vg is a data frame, it should contain absolute differences (numeric 'dabs'), inter-point distances (numeric 'd'), and, optionally, an assignment into distance bins (integer 'bin') for a sample of point pairs. If 'bin' is missing, the function calls sk_add_bins to assign them automatically.

Function fun is the statistic to use for estimating the variogram (ie twice the semi-variogram) from the distance-binned absolute differences in vg. If fun is a function, it must accept sub-vectors of the numeric vg$dabs as its only argument, returning a non-negative numeric scalar. fun can also be set to one of the names 'root_median', 'root_mean' (the default), or 'classical', as shorthand for the robust fourth-root-based methods in section 2.4 of Cressie (1993), or the classical mean of squares method of Matheron.

Optional list pars defines a theoretical semi-variogram to draw over the sample data. When add=TRUE, the function overlays it on an existing plot (without changing the legend, plot limits etc). Anisotropic models, which may assume a range of semi-variances for any given distance, are drawn as a ribbon plot.

add=TRUE can only be used in combination with an earlier call to sk_plot_semi where reset=FALSE (which allows the function to change R's graphical parameters)

vg can be a grid object (anything understood by sk) rather than a variogram data frame. When add=FALSE, the function uses it to set the distance limits for an initial empty plot (the model semi-variance is then drawn if pars is supplied).

Examples

# make example grid and reference covariance model
gdim = c(10, 15)
g_empty = sk(gdim)
pars = sk_pars(g_empty, 'mat')

# plot a semivariance model
sk_plot_semi(g_empty)
sk_plot_semi(g_empty, pars)

# change annotations, sharpen ribbon border
sk_plot_semi(g_empty, pars, main='title', xlab='x', ylab='y')
sk_plot_semi(g_empty, pars, alpha_model_b=1, main='example title', xlab='x', ylab='y')

# generate sample data and sample semivariogram
g_obs = sk_sim(g_empty, pars)
vg = sk_sample_vg(g_obs)
sk_plot_semi(vg)

# different aggregation methods produce variety of results
sk_plot_semi(vg, fun='root_median')
sk_plot_semi(vg, fun='root_mean')
sk_plot_semi(vg, fun='classical') # default
sk_plot_semi(vg, fun=function(x) mean(x^2)) # same as classical

# plot again with reference model and adjust distance limits, number of bins
sk_plot_semi(vg, pars)
sk_plot_semi(vg, pars, d_max=10)
sk_plot_semi(vg, pars, d_max=10, n_bin=1e2)

# add dashed line for half sample variance (this tends to underestimate the sill)
sk_plot_semi(vg, pars)
sample_var = var(g_obs[['gval']], na.rm=TRUE)
abline(h=sample_var/2, lty='dashed')

# initial call with reset=FALSE, then use add=TRUE to overlay the same model with a green fill
sk_plot_semi(vg, pars, lwd=2, reset=FALSE)
sk_plot_semi(vg, pars, add=TRUE, col_model='green', alpha_model_b=0)

# overlay several models with varying nugget effect
pars_vary = pars
for(i in seq(3))
{
  pars_vary$eps = 0.8 * pars_vary$eps
  sk_plot_semi(vg, pars_vary, add=TRUE, alpha_model_b=0)
}
dev.off()