SDMTools (version 1.1-11)

accuracy: Measures of Model Accuracy

Description

accuracy estimates six measures of accuracy for presence-absence or presence-psuedoabsence data. These include AUC, ommission rates, sensitivity, specificity, proportion correctly identified and Kappa. Note: this method will exclude any missing data.

Usage

accuracy(obs,pred,threshold=0.5)

Arguments

obs

a vector of observed values which must be 0 for absences and 1 for occurrences

pred

a vector of the same length as obs representing the predicted values. Values must be between 0 & 1 prepresenting a likelihood.

threshold

this can be: a) a single value representing a single threshold between 0 & 1; b) a vector of threshold values between 0 & 1; OR c) an integer value representing the number of equal interval threshold values between 0 & 1

Value

• a data.frame with seven columns:
• threshold
  the threshold values representing each row of data
• AUC
  the AUC given the defined threshold value
• ommission.rate
  the ommission rate as a proportion of true occurrences misidentified given the defined threshold value
• sensitivity
  the sensitivity given the defined threshold value
• specificity
  the specificity given the defined threshold value
• prop.correct
  the proportion of the presence and absence records correctly identified given the defined threshold value
• Kappa
  the Kappa statistic of the model given the defined threshold value

See Also

auc, Kappa, omission, sensitivity, specificity, prop.correct, confusion.matrix

Examples

#create some data
obs = c(sample(c(0,1),20,replace=TRUE),NA); obs = obs[order(obs)]
pred = runif(length(obs),0,1); pred = pred[order(pred)]

#calculate accuracy of the model with a single threshold value
accuracy(obs,pred,threshold=0.5)

#calculate accuracy given several defined thresholds
accuracy(obs,pred,threshold=c(0.33,0.5,0.66))

#calculate accuracy given a number of equal interval thresholds
accuracy(obs,pred,threshold=20)