EcoGenetics (version 1.2.1-5)

eco.rasterplot: Rasterplot graphs

Description

This function generates a multivariate plot for a data matrix (raster), with an option for filtering the data and to plot using groups. The resterplot graph is a flexible tool useful for different data sources.

Usage

eco.rasterplot(x, filter = NULL, condition = NULL, grp = NULL,
  limits = NULL, title = NULL, z.name = NULL, vertical = TRUE,
  interactivePlot = TRUE, ...)

Arguments

x

Data matrix (raster)

filter

Optional data matrix used as filter

condition

Condition used to filter data

grp

Factor with groups to use in the plot. Default NULL

limits

Balues limits used for computing the data gradient for the plot

title

Plot title

z.name

Name for the legend

vertical

Should the populations on the x axis be partitioned? Default TRUE.

interactivePlot

Show an interactive plot via plotly? (default: TRUE)

...

additional arguments

Examples

# NOT RUN {
 
# }
# NOT RUN {
data(eco.test)
require(ggplot2)

# using the ecogen object "eco" to perform a multiple-lsa
con <- eco.weight(eco[["XY"]], method = "knearest", k = 4, row.sd = TRUE)
test.lsa <- eco.lsa(eco[["P"]], con = con, method = "I", nsim = 99, multi = "matrix")

# the plot method for this object based in ggplot2, is a resterplot
eco.plotLocal(test.lsa, multi = "ggplot2")

# adding a factor
test.lsa <- eco.lsa(eco[["P"]], con = con, method = "I",
nsim = 99, multi = "matrix", pop = eco[["S"]][,1])
eco.plotLocal(test.lsa, multi = "ggplot2")

# The generic rasterplot method requires a data matrix, and, as option, a condition 
# and a filter matrix. The condition is an expression, containing the word "filter" and 
# logical elements, e.g., "filter < 50", "filter <50 || filter > 2", etc. ). 
# Filter is used as a logical matrix (TRUE-FALSE, in relation to the passed condition),
# for filtering the data. If a condition is passed but not a filter matrix, the condition
# is applied over the data matrix, also using the word "filter". 
# Internally, the multi.lsa plot uses three fundamental elements. 
# - a data matrix: in the example, ecoslot.OBS(test.lsa)
# - a filter matrix: in the example, ecoslt.PVAL(test.lsa); i.e., 
# the data matrix will be filtered by P-value using the third element, an expresion.
# - an expression: in the example: "filter < 0.05"
 
 # by combining the three elements, the multivariate plot can be manually constructed:
 my.plot <- eco.rasterplot(x= ecoslot.OBS(test.lsa), 
 filter = ecoslot.PVAL(test.lsa), condition = "filter < 0.05")
 my.plot
 
 
 # add population
 my.plot <- eco.rasterplot(x= ecoslot.OBS(test.lsa), 
 filter = ecoslot.PVAL(test.lsa), 
 condition = "filter < 0.05", grp = ecoslot.POP(test.lsa))
 my.plot
 
 
 # extra manipulation with ggplot2 graphs (ggplot2 commands allowed by rasterplot)
 my.plot <- eco.rasterplot(x= ecoslot.OBS(test.lsa), 
 filter = ecoslot.PVAL(test.lsa), condition = "filter < 0.05",
 interactivePlot = FALSE)
 my.plot
 
 
 ## rotate plot
 
 my.plot + coord_flip()
 
 ## change design
 my.plot + theme_grey()
 
 
 # using the data as filter
 eco.rasterplot(x= ecoslot.OBS(test.lsa), filter = ecoslot.OBS(test.lsa), 
 condition = "filter > 0 & filter < 3")
 
 
 # example of bad syntax (incorrect use of && over matrices)
 eco.rasterplot(x= ecoslot.OBS(test.lsa), filter = ecoslot.OBS(test.lsa), 
 condition = "filter > 0 && filter < 3")
 
 
# }
# NOT RUN {
 
# }