I'm trying to run a logit model in R which has only one predictor. I then
want to construct a data frame with the predictor values on one column and
the probabilities in another column.

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!




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