sdm (version 1.0-89)

evaluates: evaluate for accuracy

Description

evaluates for accuracy

Usage

evaluates(x,p,...)
getEvaluation(x,w,wtest,stat,opt,...)

Arguments

x

a numeric vector including the observed values; or a sdmModels object

p

a numeric vector including the predicted values

w

a numeric vector indicates model IDs

wtest

which test, training, dep.test, or indep.test?

stat

statistics that should be extracted from the sdmEvaluate object

opt

a numeric value indicates which threshold optimisation criteria should be considered if a threshold-based statistic is selected in stat

...

additional arguments (see details)

Value

an object of class sdmEvaluate from evaluates function

a list or data.frame from getEvaluation function

Details

Evaluates the preformance (accuracy) given the obsetved values, and the predicted values. As additional argument, the distribution of data can be specified (through distribution), that can be either of 'binomial', 'gaussian', 'laplase', or 'poisson'. If not specified, it will be guessed by the function!

getEvaluation can be used to get the evaluation results from a fitted model (sdmModels object that is output of the sdm function). Each model in sdmModels has a modelID, that can be specified in w argument. If w is not specified or more than a modelID is specified, then a data.frame is generated that contains the statistics specified in stat. For a single model (if length w is 1), stat can be 1 (threhold_independent statistics), or 2 (threshold_based statistics) or NULL (both groups). If more than a model is specified (w is either NULL or has a length greater than 1), stat can be the name of statistics such as 'AUC', 'COR', 'Deviance', 'obs.prevalence', 'threshold', 'sensitivity', 'specificity', 'TSS', 'Kappa', 'NMI', 'phi', 'ppv', 'npv', 'ccr', 'prevalence'. If either of the thershold_based stats are selected, opt can be also specified to select one of the criteria for optimising the threshold. The possible value can be between 1 to 10 for "sp=se", "max(se+sp)", "min(cost)", "minROCdist", "max(kappa)", "max(ppv+npv)", "ppv=npv", "max(NMI)", "max(ccr)", "prevalence" criteria, respectively.

References

Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, DOI: 10.1111/ecog.01881

See Also

#

Examples

# NOT RUN {
file <- system.file("external/model.sdm", package="sdm")

m <- read.sdm(file) # a sdmModels Object (fitted using sdm function)

getModelInfo(m)

# there are 4 models in the sdmModels objects

# so let's take a look  at all the results for the model with modelID 1

# evaluation using training data (both threshod_independent and threshold_based groups):

getEvaluation(m,w=1,wtest='training') 

getEvaluation(m,w=1,wtest='training',stat=1) # stat=1 (threshold_independent)

getEvaluation(m,w=1,wtest='test.dep',stat=2) # stat=2 (threshold_based)

getEvaluation(m,w=1:3,wtest='test.dep',stat=c('AUC','TSS'),opt=2) 

getEvaluation(m,opt=1) # all models

getEvaluation(m,stat=c('TSS','Kappa','AUC'),opt=1) # all models


############

example for evaluation:

evaluates(x=c(1,1,0,1,0,0,0,1,1,1,0),
          p=c(0.69,0.04,0.05,0.95,0.04,0.65,0.09,0.61,0.75,0.84,0.15))

# }