CODEWITHSUNDEEP
rglmpredictpoisson
Daniel
Daniel

Reputation: 7832

Equivalent to plogis(logit) for Poisson-family in R

Note: This is a cross posting. I posted this question last week on Cross Validated, however, it either doesn't fit in there or it wasn't recognized - hence, I'm trying to get an answer here...

When I run a glm with binomial-family (logistic regression), R output gives me the logit-estimates, which can be transformed into probabilities using plogis(logit). So using something like plogis(predict(glm_fit, type = "terms")) would give me the adjusted probabilities of success for each predictor.

But what would be the equivalent for Poisson regression? How can I "predict" the adjusted incidents rates for each predictor?

Given this example:

set.seed(123)
dat <- data.frame(y = rpois(100, 1.5),
                  x1 = round(runif(n = 100, 30, 70)),
                  x2 = rbinom(100, size = 1, prob = .8),
                  x3 = round(abs(rnorm(n = 100, 10, 5))))

fit <- glm(y ~ x1 + x2 + x3, family = poisson(), data = dat)

and using predict.glm(fit, type = "terms")

I get:

         x1          x2          x3
1 -0.023487964  0.04701003  0.02563723
2  0.052058119 -0.20041119  0.02563723
3  0.003983339  0.04701003  0.01255701
4 -0.119637524  0.04701003 -0.03322376
5  0.010851165  0.04701003 -0.00706332
6 -0.105901873 -0.20041119 -0.00706332
...
attr(,"constant")
[1] 0.3786072

So, how many "incidents" (y-value) would I expect for each value of x1, holding x2 and x3 constant (what predict does, afaik)?

Upvotes: 3

Views: 848

Answers (1)

Daniel
Daniel

Reputation: 7832

Ok, I guess I now found what I was looking for: the inverse link-function. So, family(fit)$linkinv(eta = ...) gives me the correct predicted values / effects for different model families and link functions.

For instance, for binomial regression with logit-link, family(fit)$linkinv(eta = x) is equivalent to plogis(x).

Upvotes: 0

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