am.glm <- glm(am ~ hp + wt, data=mtcars, family=binomial)
newdata <- data.frame(hp=120, wt=2.8)
p1 <- predict(am.glm, newdata, type="response")
p2 <- 1/(1+exp(-(am.glm$coefficients[1] +
             am.glm$coefficients[2]*newdata[1,1] +
                 am.glm$coefficients[3]*newdata[1,2])))
p1 - p2
##            1
## 1.110223e-16

library(OneR) # for bin and eval_model function
mtcars_bin <- bin(mtcars)
m <- glm(am ~ hp + wt, data = mtcars_bin, family = binomial)
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
coefficients(m) #points for ranges
##   (Intercept)   hp(109,165]   hp(165,222]   hp(222,278]   hp(278,335]
##      21.30781      19.62427      19.91155      40.03050      62.28971
##  wt(2.3,3.08] wt(3.08,3.86] wt(3.86,4.64] wt(4.64,5.43]
##     -20.61467     -62.03146     -62.78544     -81.47841
prediction <- round(predict(m, type = 'response', mtcars_bin))
eval_model(prediction, mtcars_bin$am)
##
## Confusion matrix (absolute):
##           Actual
## Prediction  0  1 Sum
##        0   18  1  19
##        1    1 12  13
##        Sum 19 13  32
##
## Confusion matrix (relative):
##           Actual
## Prediction    0    1  Sum
##        0   0.56 0.03 0.59
##        1   0.03 0.38 0.41
##        Sum 0.59 0.41 1.00
##
## Accuracy:
## 0.9375 (30/32)
##
## Error rate:
## 0.0625 (2/32)
##
## Error rate reduction (vs. base rate):
## 0.8462 (p-value = 1.452e-05)
##
## different scaling
coefficients(m) * 20/log(2)
##   (Intercept)   hp(109,165]   hp(165,222]   hp(222,278]   hp(278,335]
##      614.8136      566.2367      574.5260     1155.0360     1797.3012
##  wt(2.3,3.08] wt(3.08,3.86] wt(3.86,4.64] wt(4.64,5.43]
##     -594.8136    -1789.8496    -1811.6048    -2350.9701