尝试在另一台计算机上使用glm模型进行预测时出错

Question

I would like to save a glm object in one R machine and use it for prediction on another data set located on another machine that has a newer data.I try to use save and load but with no success.What am I doing wrong? Here is a toy example:

# on machine 1:
glm<-glm(y~x1+x2,data=dat1, family=binomial(link="logit")
save(glm,file="glm.Rdata") # the file is stored in a folder.

# on machine 2:
load(glm.RData) # got an error:"Error in load(glm.RData) : object 'glm.RData' not found"
#I tried :
 load(file='glm.RData') # no error was displayed
  print(glm) # got an error:"Error in load(glm.RData) : object 'glm.RData' not found"

Any help will be great.

Answer 1

As per @user3710546's advice, I would avoid saving your model using the name glm , as it'll mask (ie. block) the glm() function, making it difficult for you to use it in your session.

Using save() and load()

save() is generally used to save a list of objects to a file, rather than a single object. The first argument to save() is list , 'A character vector containing the names of objects to be saved.' (Emphasis mine.) So you'd want to use it like this:

# On machine 1:
save(list = 'glm', file = '/path/to/glm.RData')

# On machine 2:
load(file = '/path/to/glm.RData')

Note that the file extensions are often case-sensitive: you saved to a file with the extension .RData but loaded from one with the extension .Rdata , which is different. This may explain why the file isn't found.

Using saveRDS() and readRDS()

An alternative to using save() and load is to use saveRDS() and readRDS() , which are designed to be used with one object. They're used slightly differently:

# On machine 1
saveRDS(glm, file = '/path/to/glm.rds')

# On machine 2
glm = readRDS(file = '/path/to/glm.rds')

Note the .rds file extension and the fact that readRDS() isn't automatically put in the environment (it needs to be assigned to something).

Saving parts of a GLM

If you just want the formula saved—that is, the actual text string—you can find it in glm$formula , where glm is the name of your object. It comes back as a formula object, but you can convert it to a string with as.character(glm$formula) , to then be written to a text file or whatever.

If, however, you want the model itself without the dataset it was created from (to cut down on disk space), have a look at this article , which discusses which parts of a glm object can be safely deleted.