evalplot.stats: ENMevaluation statistics plot

In jamiemkass/ENMeval: Automated Tuning and Evaluations of Ecological Niche Models

ENMevaluation statistics plot

Description

Plot evaluation statistics over tuning parameter ranges to visualize differences in performance

Usage

evalplot.stats(
  e,
  stats,
  x.var,
  color.var,
  dodge = NULL,
  error.bars = TRUE,
  facet.labels = NULL,
  metric.levels = NULL,
  return.tbl = FALSE
)

Arguments

e	ENMevaluation object
stats	character vector: names of statistics from results table to be plotted; if more than one statistic is specified, the plot will be faceted
x.var	character: variable to be plotted on x-axis
color.var	character: variable used to assign symbology colors
dodge	numeric: dodge width for points and lines; this improves visibility when there is high overlap (optional)
error.bars	boolean: if TRUE, plot error bars
facet.labels	character vector: custom names for the metric facets
metric.levels	character vector: custom factor levels for metrics; this controls the order that metric statistics are plotted
return.tbl	boolean: if TRUE, return the data frames of results used to make the ggplot instead of the plot itself

Details

In this plot, the x-axis represents a tuning parameter range, while the y-axis represents the average of a statistic over all partitions. Different colors represent the categories or values of another tuning parameter. Error bars represent the standard deviation of a statistic around the mean. Currently, this function can only plot two tuning parameters at a time.

Value

A ggplot of evaluation statistics.

Examples

## Not run: 
# first, let's tune some models
occs <- read.csv(file.path(system.file(package="predicts"), 
"/ex/bradypus.csv"))[,2:3]
envs <- rast(list.files(path=paste(system.file(package="predicts"), 
"/ex", sep=""), pattern="tif$", full.names=TRUE))
bg <- as.data.frame(predicts::backgroundSample(envs, n = 10000))
names(bg) <- names(occs)
 
ps <- list(orientation = "lat_lat")
e <- ENMevaluate(occs, envs, bg, 
               tune.args = list(fc = c("L","LQ","LQH"), rm = 1:5), 
               partitions = "block", partition.settings = ps, 
               algorithm = "maxnet", categoricals = "biome", 
               parallel = TRUE)
               
evalplot.stats(e, c("cbi.val", "or.mtp"), x.var = "rm", color.var = "fc", 
             error.bars = FALSE)

## End(Not run)

jamiemkass/ENMeval documentation built on Jan. 19, 2025, 4:33 a.m.