View source: R/get_important_rasters.R
summarize_ENMeval | R Documentation |
Function to summarize ENMeval output
summarize_ENMeval(
eval.par,
minLat = 20,
maxLat = 50,
minLon = -110,
maxLon = -40,
examine.predictions = c("L", "LQ", "H", "LQH", "LQHP", "LQHPT"),
examine.stats = c("auc.val", "auc.diff", "cbi.val", "or.mtp", "or.10p"),
RMvalues = seq(0.5, 4, 0.5),
nullmodel.iter = 100,
plotDIR = "./plots",
showPLOTS = FALSE,
niche.overlap = FALSE,
plot.width = 7,
plot.height = 7,
imp.margins = c(10, 4.1, 4.1, 2.1)
)
eval.par |
Object returned from runENMeval |
minLat |
Minimum latitude to plot for predictions |
maxLat |
Maximum latitude to plot for predictions |
minLon |
Minimum longitude to plot for predictions |
maxLon |
Maximum longitude to plot for predictions |
examine.predictions |
Character vector of feature classes to examine how complexity affects predictions |
examine.stats |
Character vector of stats to evaluate |
RMvalues |
Vector of non-negative RM values to examine how complexity affects predictions |
nullmodel.iter |
Integer; Number of iterations to perform with null model. |
plotDIR |
Directory to save plots to |
showPLOTS |
Boolean; Whether to print plots to screen |
niche.overlap |
Boolean. If TRUE, calculates pairwise niche overlap matrix |
plot.width |
Integer. Specify plot widths |
plot.height |
Integer. Specify plot heights |
imp.margins |
Integer vector. Margins of variable importance barplot. c(bottom, left, top, right) |
summarize_ENMeval(eval.par = eval.par,
minLat = 20,
maxLat = 50,
minLon = -110,
maxLon = -40,
examine.predictions = c("L",
"LQ",
"H",
"LQH",
"LQHP",
"LQHPT"),
RMvalues = seq(0.5, 4, 0.5),
plotDIR = "./plots",
showPLOTS = TRUE,
niche.overlap = FALSE,
plot.width = 7,
plot.height = 7,
imp.margins = c(10.0, 4.1, 4.1, 2.1))
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