PresenceAbsence (version 1.1.9)

predicted.prevalence: Predicted Prevalence

Description

predicted.prevalence calculates the observed prevalence and predicted prevalence for one or more models at one or more thresholds.

Usage

predicted.prevalence(DATA, threshold = 0.5, which.model = (1:N.models), na.rm = 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)

threshold

a cutoff values between zero and one used for translating predicted probabilities into 0 /1 values, defaults to 0.5. threshold 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.

which.model

a number indicating which models from DATA should be used

na.rm

a logical indicating whether missing values should be removed

Value

returns a dataframe where:

[,1]	threshold	thresholds used for each row in the table
[,2]	Obs.Prevalence	this will be the same in each row
[,3]	Model 1	Predicted prevalence for first model

Details

Function will work for one model and multiple thresholds, or one threshold and multiple models, or multiple models each with their own threshold.

Examples

# NOT RUN {
data(SIM3DATA)

predicted.prevalence(SIM3DATA)
predicted.prevalence(SIM3DATA,threshold=11,which.model=1,na.rm=FALSE)
predicted.prevalence(SIM3DATA,threshold=c(.2,.5,.7),na.rm=FALSE)
# }

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