As your output says

Results are given on the logit (not the response) scale.

So to get them on response scale, you need to pass them through inverse of the logit link function.

You can recall, that generalized linear models are defined in terms of linear predictor, that is passed through link function, to predict mean of some distribution. In case of logistic regression, we use logit link function, i.e.

$$ \operatorname{logit}(p) = \log(\tfrac{p}{1-p}) $$

So the untransformed values returned by logistic regression are log odds. To transform them to probabilities, the linear predictor $\eta = X\beta$ is passed through inverse of the logit function, i.e. logistic function

$$ E(Y|X) = p = \operatorname{logit}^{-1}(\eta) = \operatorname{logistic}(\eta) = \frac{\exp(\eta)}{\exp(\eta)+1} $$

TL;DR so to transform the values returned by the function you mention (I am not familiar with this package), you need to use the logistic function, to get the probabilities.

As your output says

Results are given on the logit (not the response) scale.

So to get them on response scale, you need to pass them through inverse of the logit link function.

You can recall, that generalized linear models are defined in terms of linear predictor, that is passed through link function, to predict mean of some distribution. In case of logistic regression, we use logit link function, i.e.

$$ \operatorname{logit}(p) = \log(\tfrac{p}{1-p}) = \eta = X\beta $$

So the untransformed values returned by logistic regression are log odds. To transform them to probabilities, the linear predictor $\eta = X\beta$ is passed through inverse of the logit function, i.e. logistic function

$$ E(Y|X) = p = \operatorname{logit}^{-1}(\eta) = \frac{\exp(\eta)}{\exp(\eta)+1} $$

TL;DR so to transform the values returned by the function you mention (I am not familiar with this package), you need to use the logistic function, to get the probabilities.

As your output says

Results are given on the logit (not the response) scale.

So to get them on response scale, you need to pass them through inverse of the logit link function.

As your output says

Results are given on the logit (not the response) scale.

So to get them on response scale, you need to pass them through inverse of the logit link function.

You can recall, that generalized linear models are defined in terms of linear predictor, that is passed through link function, to predict mean of some distribution. In case of logistic regression, we use logit link function, i.e.

$$ \operatorname{logit}(p) = \log(\tfrac{p}{1-p}) $$

So the untransformed values returned by logistic regression are log odds. To transform them to probabilities, the linear predictor $\eta = X\beta$ is passed through inverse of the logit function, i.e. logistic function

$$ E(Y|X) = p = \operatorname{logit}^{-1}(\eta) = \operatorname{logistic}(\eta) = \frac{\exp(\eta)}{\exp(\eta)+1} $$

TL;DR so to transform the values returned by the function you mention (I am not familiar with this package), you need to use the logistic function, to get the probabilities.