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I'd like to predict a variable that is bound between 0 and 1, these are patients' responses on a visual analog scale. When I use a simple linear model, some predictions are out of the bounds of the allowed range of values; I'd like to avoid that. It occurred to me to fit a Gaussian glm with a logit link. however, I ran into trouble since logit(0) = -Inf and logit(1) = Inf. I can simply set 0 = 0.001 and 1 = 0.999, model runs fine.

My questions are:

1. Is the glm(..., family = gaussian(link = "logit")) appropriate for that kind of data?
2. Is there another, more appropriate way to circumvent the Inf/-Inf problems?
3. How could I calculate prediction intervals from that model?

Thanks!

I'd like to predict a variable that is bound between 0 and 1, these are patients' responses on a visual analog scale. When I use a simple linear model, some predictions are out of the bounds of the allowed range of values; I'd like to avoid that. It occurred to me to fit a Gaussian glm with a logit link. however, I ran into trouble since logit(0) = -Inf and logit(1) = Inf. I can simply set 0 = 0.001 and 1 = 0.999, model runs fine.

My questions are:

1. Is the glm(..., family = gaussian(link = "logit")) appropriate for that kind of data?
2. Is there another, more appropriate way to circumvent the Inf/-Inf problems?
3. How could I calculate prediction intervals from that model?

I'd like to predict a variable that is bound between 0 and 1, these are patients' responses on a visual analog scale. When I use a simple linear model, predictions can go over the allowed range of values; I'd like to avoid that. It occurred to me to fit a gaussian glm with a logit link. however, i run into trouble since logit(0) = -Inf and logit(1) = Inf. I can simply set 0 = 0.001 and 1 = 0.999, model runs fine.

My questions are:

1. Is the glm(..., family = gaussian(link = "logit")) appropriate for that kind of data?
2. Is there another, more appropriate way to circumvent the Inf/-Inf problems?
3. How could I calculate prediction intervals from that model?

Thanks!

I'd like to predict a variable that is bound between 0 and 1, these are patients' responses on a visual analog scale. When I use a simple linear model, some predictions are out of the bounds of the allowed range of values; I'd like to avoid that. It occurred to me to fit a Gaussian glm with a logit link. however, I ran into trouble since logit(0) = -Inf and logit(1) = Inf. I can simply set 0 = 0.001 and 1 = 0.999, model runs fine.

My questions are:

1. Is the glm(..., family = gaussian(link = "logit")) appropriate for that kind of data?
2. Is there another, more appropriate way to circumvent the Inf/-Inf problems?
3. How could I calculate prediction intervals from that model?

Thanks!

I'd like to predict a variable that is bound between 0 and 1, these are patients' responses on a visual analog scale. When I use a simple linear model, predictions can go over the allowed range of values; I'd like to avoid that. It occurred to me to fit a gaussian glm with a logit link. however, i run into trouble since logit(0) = -Inf and logit(1) = Inf. I can simply set 0 = 0.001 and 1 = 0.999, model runs fine.

My questions are:

1. Is the glm(..., family = gaussian(link = "logit")) appropriate for that kind of data?
2. Is there another, more appropriate way to circumvent the Inf/-Inf problems?

Thanks!

I'd like to predict a variable that is bound between 0 and 1, these are patients' responses on a visual analog scale. When I use a simple linear model, predictions can go over the allowed range of values; I'd like to avoid that. It occurred to me to fit a gaussian glm with a logit link. however, i run into trouble since logit(0) = -Inf and logit(1) = Inf. I can simply set 0 = 0.001 and 1 = 0.999, model runs fine.

My questions are:

1. Is the glm(..., family = gaussian(link = "logit")) appropriate for that kind of data?
2. Is there another, more appropriate way to circumvent the Inf/-Inf problems?
3. How could I calculate prediction intervals from that model?

Thanks!