statpsych (version 2.0.0)

logitfit: The confusion matrix is computed using the observed 0 or 1 response variable scores, the predicted probabilities from the logistic model, and a specified cutpoint.

Description

The confusion matrix is computed using the observed 0 or 1 response variable scores, the predicted probabilities from the logistic model, and a specified cutpoint.

Usage

logitfit(y, p, cut)

Value

Returns a 1-row matrix. The columns are:

• C - percent correctly classified (aka accuracy)

• TP - percent true positives (aka sensitivity, recall)

• FP - percent false positives (1 - TN)

• TN - percent true negatives (aka specificity)

• FN - percent false negatives (1 - TP)

• PPV - percent positive predicted values (aka precision)

• NPV - percent negative predicted values

• F1 - F1 score (2 x PPV x TP)/(PPV + TP)

Arguments

y

vector of observed 0 or 1 response scores

p

vector of predicted probabilities from model

cut

cutpoint (defines the predicted 0 or 1 scores)

Examples

y <- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
x1 <- c(1,1,3,2,6,8,4,5,6,2,4,3,1,5,3,9,8,9,8,6,6,7,5,3,8,6,5,7,8,9,7,8)
x2 <- c(0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0)
out <- glm(y ~ x1 + x2, family = binomial(link = "logit"))
p <- predict(out, type = "response")
logitfit(y, p, .3)

# Should return:
#      C    TP    FP    TN   FN PPV     NPV      F1 
#  81.25 93.75 31.25 68.75 6.25  75 91.6667 83.3333 

logitfit(y, p, .4)
# Should return:
#     C    TP    FP    TN   FN     PPV     NPV      F1
#  87.5 93.75 18.75 81.25 6.25 83.3333 92.8571 88.2353