confusion.matrix: Confusion matrix for logistic regression models
In profpetrie/regclass: Tools for an Introductory Class in Regression and Modeling

Description Usage Arguments Details Author(s) See Also Examples

This function takes the output of a logistic regression created with glm and returns the confusion matrix.

1	confusion.matrix(M,DATA=NA)

`M`	A logistic regression model created with `glm`
`DATA`	A data frame on which the confusion matrix will be made. If omitted, the confusion matrix is on the data used in `M`. If specified, the data frame must have the same column names as the data used to build the model in `M`.

This function makes classifications on the data used to build a logistic regression model by predicting the "level of interest" (last alphabetically) when the predicted probability exceeds 50%.

Adam Petrie

glm

  #On WINE data as a whole
  data(WINE)
  M <- glm(Quality~.,data=WINE,family=binomial)
  confusion.matrix(M)
  
  #Calculate generalization error using training/holdout
  set.seed(1010)
  train.rows <- sample(nrow(WINE),0.7*nrow(WINE),replace=TRUE)
  TRAIN <- WINE[train.rows,]
  HOLDOUT <- WINE[-train.rows,]
  M <- glm(Quality~.,data=TRAIN,family=binomial)
	confusion.matrix(M,HOLDOUT)
	
	
	#Predicting donation
	#Model predicting from recent average gift amount is significant, but its
	#classifications are the same as the naive model (majority rules)
	data(DONOR)
	M.naive <- glm(Donate~1,data=DONOR,family=binomial)
	confusion.matrix(M.naive)
	M <- glm(Donate~RECENT_AVG_GIFT_AMT,data=DONOR,family=binomial)
	confusion.matrix(M)