Using LDA methodology, I'm able to do the following.

# RUN LDA

r <- lda(y ~ x)

## FIND AND PLOT MODEL PROBABILITIES

xx <- seq(min(x), max(x), length=1000)

pred <- predict(r, data.frame(x=xx), type='response')

yy <- pred$posterior[,2]

t = data.frame(Our_Bid=c(xx), "Pr(win)"=c(yy))

head(t, 10)

> head(t, 10)

Our_Bid Pr.win.

1 0.000000 0.1525893

2 3.096096 0.1538502

3 6.192192 0.1551196

4 9.288288 0.1563976

5 12.384384 0.1576841

6 15.480480 0.1589792

7 18.576577 0.1602829

8 21.672673 0.1615952

9 24.768769 0.1629162

10 27.864865 0.1642459

I want to do something similar with glm but I'm not finding that I'm able to manipulate predict() such that I can get a similar output as ^^.

mod1 = glm(posted ~ amount, data=ndat, family=binomial(link="probit"))

summary(mod1)

Can anyone help?

Thanks!