predict() with arbitrary coefficients in r

Question

I\'ve got some coefficients for a logit model set by a non-r user. I\'d like to import those coefficients into r and generate some goodness of fit estimates on the same data

Answer 1

This is not an answer to your posted question - which BondedDust answered - but describes an alternate way in calculating the predicted probabilities yourself which might help in this case.

# Use the mtcars dataset for a minimum worked example
data(mtcars)

# Run a logistic regression and get predictions 
mod <- glm(vs ~ mpg + factor(gear) + factor(am), mtcars, family="binomial")
p1 <- predict(mod, type="response")

# Calculate predicted probabilities manually
m <- model.matrix(~ mpg + factor(gear) + factor(am), mtcars)[,]
p2 <- coef(mod) %*% t(m)
p2 <- plogis(p2)

all(p1 == p2)
#identical(as.numeric(p1), as.numeric(p2))

You can replace coef(mod) with the vector of coefficients given to you. model.matrix will generate the dummy variables required for the calculation - check that the ordering is the same as that of the coefficient vector.