quick.prevR: Quick prevR analysis and plot

In larmarange/prevR: Estimating Regional Trends of a Prevalence from a DHS and Similar Surveys

Quick prevR analysis and plot

Description

This function performs several analysis in one go: (i) apply rings(); (ii) compute prevalence surface with kde(); (iii) compute the surface of rings radii with krige(); (iv) plot prevalence surface using prevR.colors.red() and add rings radii as a contour plot.

Usage

quick.prevR(
  object,
  N = Noptim(object),
  nb.cells = 100,
  cell.size = NULL,
  weighted = NULL,
  plot.results = TRUE,
  return.results = FALSE,
  return.plot = FALSE,
  legend.title = "%",
  cex = 0.7,
  progression = TRUE
)

Arguments

object	object of class prevR.
N	integer or list of integers corresponding to the rings to use.
nb.cells	number of cells on the longest side of the studied area (unused if cell.size is defined).
cell.size	size of each cell (in the unit of the projection).
weighted	use weighted data (TRUE, FALSE or "2")?
plot.results	plot the results?
return.results	return the results?
return.plot	return the plot within the results?
legend.title	title of the legend
cex	to control the text size on the graph
progression	show a progress bar?

Details

N determine the rings to use for the estimation. By default, a suggested value of N will be computed with Noptim().

Value

A list of one or several elements, depending on the arguments: (i) prev is a SpatialPixelsDataFrame containing the prevalence surface; (ii) radius a SpatialPixelsDataFrame containing the kriged surface of the rings radii; (iii) plot a ggplot graph.

See Also

Noptim(), rings(), kde() and krige().

Examples

## Not run: 
quick.prevR(fdhs)

## End(Not run)

larmarange/prevR documentation built on May 18, 2023, 3:50 a.m.