presence.absence.summary: Presence/Absence Summary Plots

In PresenceAbsence: Presence-Absence Model Evaluation

Presence/Absence Summary Plots

Description

Produces four types of Presence/Absence accuracy plots for a single set of model Predictions.

Usage

presence.absence.summary(DATA, threshold = 101, find.auc = TRUE, which.model = 1, 
na.rm = FALSE, main = NULL, model.names = NULL, alpha = 0.05, N.bins = 5, N.bars = 10, 
truncate.tallest = FALSE, opt.thresholds = NULL, opt.methods = NULL, req.sens, req.spec, 
obs.prev = NULL, smoothing = 1, vert.lines = FALSE, add.legend = TRUE, 
add.opt.legend=TRUE, legend.cex = 0.6, opt.legend.cex = 0.6, pch = NULL, 
FPC, FNC, cost.line = FALSE)

Arguments

DATA	a matrix or dataframe of observed and predicted values where each row represents one plot and where columns are: DATA[,1] plot ID text DATA[,2] observed values zero-one values DATA[,3] predicted probabilities from first model numeric (between 0 and 1) DATA[,4] predicted probabilities from second model, etc...
threshold	cutoff values between zero and one used for translating predicted probabilities into 0 /1 values, defaults to 0.5. It can be a single value between zero and one, a vector of values between zero and one, or a positive integer representing the number of evenly spaced thresholds to calculate.
find.auc	a logical indicating if area under the curve should be calculated
which.model	a number indicating which model from DATA should be used
na.rm	a logical indicating whether missing values should be removed
main	an overall title for the plot
model.names	a vector of the names of each model included in DATA to be used in the legend box
alpha	alpha value for confidence intervals for calibration.plot
N.bins	integer giving number of bins for predicted probabilities for calibration.plot
N.bars	number of bars in histogram
truncate.tallest	a logical indicating if the tallest bar should be truncated to fit for presence.absence.hist
opt.thresholds	a logical indicating whether the optimal thresholds should be calculated and plotted
opt.methods	what methods should be used to optimize thresholds. Argument can be given either as a vector of method names or method numbers. Possible values are: 1 Default threshold=0.5 2 Sens=Spec sensitivity=specificity 3 MaxSens+Spec maximizes (sensitivity+specificity)/2 4 MaxKappa maximizes Kappa 5 MaxPCC maximizes PCC (percent correctly classified) 6 PredPrev=Obs predicted prevalence=observed prevalence 7 ObsPrev threshold=observed prevalence 8 MeanProb mean predicted probability 9 MinROCdist minimizes distance between ROC plot and (0,1) 10 ReqSens user defined required sensitivity 11 ReqSpec user defined required specificity
req.sens	a value between zero and one giving the user defined required sensitivity. Only used if opt.thresholds = TRUE. Note that req.sens = (1-maximum allowable errors for points with positive observations).
req.spec	a value between zero and one giving the user defined required sspecificity. Only used if opt.thresholds = TRUE. Note that req.sens = (1- maximum allowable errors for points with negative observations).
obs.prev	observed prevalence for opt.method = "PredPrev=Obs" and "ObsPrev". Defaults to observed prevalence from DATA.
smoothing	smoothing factor for maximizing/minimizing. Only used if opt.thresholds = TRUE. Instead of find the threshold that gives the max/min value, function will average the thresholds of the given number of max/min values.
vert.lines	a logical where: TRUE means vertical lines added to plot at optimal thresholds; FALSE means no vertical lines, instead optimal thresholds marked along error statistics plots. Only used if opt.thresholds = TRUE.
add.legend	logical indicating if a legend should be included on the plot
add.opt.legend	logical indicating if optimization criteria legend should be included on the plot
legend.cex	cex for legends
opt.legend.cex	cex for optimization criteria legend
pch	plotting "character", i.e., symbol to use for the thresholds specified in MARK. pch can either be a single character or an integer code for one of a set of graphics symbols. See help(points) for details.
FPC	False Positive Costs, or for C/B ratio C = 'net costs of treating nondiseased individuals'.
FNC	False Negative Costs, or for C/B ratio B = 'net benefits of treating diseased individuals'.
cost.line	a logical indicating if the line representing the realtive cost ratio should be added to the plot.

Details

presence.absence.summary produces a set of summary plots for a single model, along with calculating AUC and optimal thresholds. presence.absence.summary is not quite as flexible as the individual plot functions, as some arguments are preset so that the plots will be comparable, but the remaining arguments have the same meaning. See the individual plot functions error.threshold.plot, auc.roc.plot, calibration.plot, and presence.absence.hist for further details.

Value

creates a graphical plot

Author(s)

Elizabeth Freeman [e-mail]

See Also

optimal.thresholds, error.threshold.plot, auc.roc.plot, calibration.plot, presence.absence.hist

Examples

data(SIM3DATA)

presence.absence.summary(SIM3DATA)

presence.absence.summary(	SIM3DATA,
					threshold=101,
					find.auc=TRUE,
					which.model=2,
					na.rm=FALSE,
					main=NULL,
					model.names=NULL,
					alpha=0.05,
					N.bins=5,
					N.bars=10,
					truncate.tallest=FALSE,
					opt.thresholds=TRUE,
					opt.methods=c(1,2,4),
					req.sens=0.85,
					req.spec=0.85,
					obs.prev=NULL,
					smoothing=1,
					vert.lines=FALSE,
					add.legend=TRUE,
					add.opt.legend=TRUE,
					legend.cex=0.6,
					opt.legend.cex=0.6,
					pch=NULL)

PresenceAbsence documentation built on Jan. 7, 2023, 9:09 a.m.