predicted.prevalence: Predicted Prevalence

In PresenceAbsence: Presence-Absence Model Evaluation

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) DATA[,4] predicted probabilities from second model, etc...
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

Details

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

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
[,4]	Model 2	Predicted prevalence for second model, etc...

Author(s)

Elizabeth Freeman [e-mail]

Examples

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)

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