DAAGxtras (version 0.8-2)

confusion: Given actual and predicted group assignments, give the confusion matrix

Description

Given actual and predicted group assignments, give the confusion matrix

Usage

confusion(actual, predicted, gpnames = NULL, rowcol=c("actual", "predicted"), printit = c("overall","confusion"), prior = NULL, digits=3)

Arguments

actual

Actual (prior) group assigments

predicted

Predicted group assigments.

gpnames

Names for groups, if different from levels(actual)

rowcol

For predicted categories to appear as rows, specify rowcol="predicted"

printit

Character vector. Print "overall", or "confusion" matrix, or both.

prior

Prior probabilities for groups, if different from the relative group frequencies

digits

Number of decimal digits to display in printed output

Value

• A list with elements overall (overall accuracy), confusion (confusion matrix) and prior (prior used for calculation of overall accuracy)

Details

Predicted group assignments should be estimated from cross-validation or from bootstrap out-of-bag data. Better still, work with assignments for test data that are completely separate from the data used to dervive the model.

References

Maindonald and Braun: 'Data Analysis and Graphics Using R', 3rd edition 2010, Section 12.2.2

Examples

library(MASS)
library(DAAG)
cl <- lda(species ~ length+breadth, data=cuckoos, CV=TRUE)$class
confusion(cl, cuckoos$species)

## The function is currently defined as
function (actual, predicted, gpnames = NULL,
            rowcol = c("actual", "predicted"),
            printit = c("overall","confusion"),
            prior = NULL, digits = 3) 
{
  if (is.null(gpnames)) 
    gpnames <- levels(actual)
  if (is.logical(printit)){
    if(printit)printit <- c("overall","confusion")
    else printit <- ""
  }
  tab <- table(actual, predicted)
  acctab <- t(apply(tab, 1, function(x) x/sum(x)))
  dimnames(acctab) <- list(Actual = gpnames, `Predicted (cv)` = gpnames)
  if (is.null(prior)) {
    relnum <- table(actual)
    prior <- relnum/sum(relnum)
    acc <- sum(tab[row(tab) == col(tab)])/sum(tab)
  }
  else {
    acc <- sum(prior * diag(acctab))
  }
  names(prior) <- gpnames
  if ("overall"%in%printit) {
    cat("Overall accuracy =", round(acc, digits), "")
    if(is.null(prior)){
      cat("This assumes the following prior frequencies:", 
          "")
      print(round(prior, digits))
    }
  }
  if ("confusion"%in%printit) {
    cat("Confusion matrix", "")
    print(round(acctab, digits))
  }
  invisible(list(overall=acc, confusion=acctab, prior=prior))
}