csas: Estimate and print the spatial CSA plot

In imarkonis/csa: A Cross-Scale Analysis Tool for Model-Observation Visualization and Integration

Description

The function csa computes (and by default plots) the aggregation curve of a given statistic in two dimensions, e.g., space.

Usage

csas(
  x,
  stat = "var",
  std = TRUE,
  plot = TRUE,
  threshold = 30,
  chk = FALSE,
  ...
)

Arguments

x	A raster or brick object.
stat	The statistic which will be estimated across the cross-scale continuum. Suitable options are: "var" for variance, "sd" for standard deviation, "skew" for skewness, "kurt" for kurtosis, "l2" for L-scale, "t2" for coefficient of L-variation, "t3" for L-skewness, "t4" for L-kurtosis.
std	logical. If TRUE (the default) the CSA plot is standardized to unit, i.e., zero mean and unit variance in the original time scale.
plot	logical. If TRUE (the default) the CSA plot is printed
threshold	numeric. Sample size of the time series at the last aggregated scale.
chk	logical. If TRUE the number of cores is limited to 2.
...	log_x and log_y (default TRUE) for setting the axes of the CSA plot to logarithmic scale. The argument wn (default FALSE) is used to plot a line presenting the standardized variance of the white noise process. Therefore, it should be used only with stat = "var" and std = T.

Value

If plot = TRUE, the csa returns a list containing:

• values: Matrix of the timeseries values for the selected stat at each scale.

• plot: Plot of scale versus stat as a ggplot object.

If plot = FALSE, then it returns only the matrix of the timeseries values for the selected stat at each scale.

References

Markonis et al., A cross-scale analysis framework for model/data comparison and integration, Geoscientific Model Development, Submitted.

Examples

data(gpm_events)
event_dates <- format(gpm_events[, unique(time)], "%d-%m-%Y")
gpm_events_brick <- dt.to.brick(gpm_events, var_name = "prcp")
plot(gpm_events_brick, col = rev(colorspace::sequential_hcl(40)),
     main = event_dates)
csas(gpm_events_brick)

gpm_sp_scale <- csas(gpm_events_brick, plot = FALSE)
gpm_sp_scale[, variable := factor(variable, labels = event_dates)]
csa.multiplot(gpm_sp_scale, smooth = TRUE, log_x = FALSE, log_y = FALSE)

imarkonis/csa documentation built on May 18, 2020, 10:49 a.m.