pcc: Percent Correctly Classified

In PresenceAbsence: Presence-Absence Model Evaluation

Percent Correctly Classified

Description

pcc calculates the percent correctly classified and (optionally) the associated standard deviation from a confusion matrix.

Usage

pcc(CMX, st.dev = TRUE)

Arguments

CMX	a confusion matrix - output from cmx
st.dev	a logical indicating whether the associated standard deviation should be calculated

Details

Percent Correctly Classified is simply the proportion of test observations that are correctly classified.

Value

if st.dev = FALSE, returns: PCC percent correctly classified.

if st.dev = TRUE, returns a dataframe where:

[1,1]	PCC	percent correctly classified
[1,2]	PCC.sd	standard deviation of PCC

Author(s)

Elizabeth Freeman [e-mail]

See Also

cmx, sensitivity, specificity, Kappa, auc

Examples

data(SIM3DATA)

pcc(cmx(SIM3DATA))

pcc(cmx(SIM3DATA),st.dev=FALSE)

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