How to save glm result without data or only with coeffients for prediction?

问题

When I use the following R code,

model_glm=glm(V1~. , data=xx,family="binomial");
save(file="modelfile",model_glm);

The size of modelfile will be as much as the data, which will be 1gig in my case. How can I remove the data part in the result of model_glm, so I can only save a small file.

回答1:

Setting model = FALSE in your call to glm should prevent the model.frame from being returned. Also setting y = FALSE will prevent the response vector from being returned. x = FALSE is the default setting and prevents the model.matrix from being returned.

This combination should shrink the size of your glm object.

Of course, you can also extract the coefficients with coef(model_glm) or, with standard errors,

summary(model_glm)$coef

回答2:

I had this issue where I was running the GLM as part of an R in production and the size of the GLM greatly slowed me down. I found I needed to kill off more than just the $data. Here is my post on it, with an example below.

> object.size(sg)
96499472 bytes
> sg$residuals <- NULL
> sg$weights <- NULL
> sg$fitted.values <- NULL
> sg$prior.weights <- NULL
> sg$na.action<- NULL
> sg$linear.predictors <- NULL
> sg$fitted.values <- NULL
> sg$effects <-NULL
> sg$data <- NULL
> object.size(sg)
3483976 bytes
> sg$qr$qr <- NULL
> object.size(sg)
79736 bytes

回答3:

object.size() is misleading because it ignores the environment attributes. If you want to assess the true size, use:

length(serialize(model_glm, NULL))

Apart from the data stored, if you want to significantly reduce the size of your glm do:

rm(list=ls(envir = attr(model_glm$terms, ".Environment")),
     envir = attr(model_glm$terms,
              ".Environment"))

This comes from a well detailed article

回答4:

You can NULL the data in the model object before saving it. I did a quick test and still generated predictions.

model_glm$data <- NULL

来源：https://stackoverflow.com/questions/13387603/how-to-save-glm-result-without-data-or-only-with-coeffients-for-prediction

标签

prediction

glm