有没有办法“压缩”lm（）类以供以后预测？ [英] Is there a way to 'compress' an lm() class for later prediction?

问题描述

有没有办法'压缩'类lm的对象，以便我可以保存到磁盘，并加载它以后使用predict.lm？

Is there a way to 'compress' an object of class lm, so that I can save it to the disk and load it up later for use with predict.lm?

我有一个lm对象，在保存时结束〜142mb，我很难相信predict.lm需要所有的原始观察值/拟合值/残差等来进行线性预测。我可以删除信息，以便保存的模型更小吗？

I have an lm object that ends up being ~142mb upon saving, and I have a hard time believing that predict.lm needs all of the original observations / fitted values / residuals etc. to make a linear prediction. Can I remove information so that the saved model is smaller?

我已经尝试将一些变量（拟合值，残差等）设置为NA，

I have tried setting some of the variables (fitted.values, residuals, etc.) to NA, but it seems to have no effect on the saved file size.

推荐答案

您可以使用 biglm 以适合您的模型， biglm 模型对象小于lm模型对象。您可以使用 predict.biglm 创建一个函数，您可以将newdata设计矩阵传递给该函数，以返回预测值。

You can use biglm to fit your models, a biglm model object is smaller than a lm model object. You can use predict.biglm create a function that you can pass the newdata design matrix to, which returns the predicted values.

另一个选择是使用 saveRDS 来保存文件，它们看起来稍小，因为它们有更少的开销，作为单个对象，不像保存保存多个对象。

Another option is to use saveRDS to save the files, which appear to be slightly smaller, as they have less overhead, being a single object, not like save which can save multiple objects.

 library(biglm)
 m <- lm(log(Volume)~log(Girth)+log(Height), trees)
 mm <- lm(log(Volume)~log(Girth)+log(Height), trees, model = FALSE, x =FALSE, y = FALSE)
 bm <- biglm(log(Volume)~log(Girth)+log(Height), trees)
 pred <- predict(bm, make.function = TRUE)
save(m, file = 'm.rdata')
 save(mm, file = 'mm.rdata')
 save(bm, file = 'bm.rdata')
 save(pred, file = 'pred.rdata')
 saveRDS(m, file = 'm.rds')
 saveRDS(mm, file = 'mm.rds')
 saveRDS(bm, file = 'bm.rds')
 saveRDS(pred, file = 'pred.rds')

 file.info(paste(rep(c('m','mm','bm','pred'),each=2) ,c('.rdata','.rds'),sep=''))
#  size isdir mode               mtime               ctime               atime exe
#  m.rdata    2806 FALSE  666 2013-03-07 11:29:30 2013-03-07 11:24:23 2013-03-07 11:29:30  no
#  m.rds      2798 FALSE  666 2013-03-07 11:29:30 2013-03-07 11:29:30 2013-03-07 11:29:30  no
#  mm.rdata   2113 FALSE  666 2013-03-07 11:29:30 2013-03-07 11:24:28 2013-03-07 11:29:30  no
#  mm.rds     2102 FALSE  666 2013-03-07 11:29:30 2013-03-07 11:29:30 2013-03-07 11:29:30  no
#  bm.rdata    592 FALSE  666 2013-03-07 11:29:30 2013-03-07 11:24:34 2013-03-07 11:29:30  no
#  bm.rds      583 FALSE  666 2013-03-07 11:29:30 2013-03-07 11:29:30 2013-03-07 11:29:30  no
#  pred.rdata 1007 FALSE  666 2013-03-07 11:29:30 2013-03-07 11:24:40 2013-03-07 11:29:30  no
#  pred.rds    995 FALSE  666 2013-03-07 11:29:30 2013-03-07 11:27:30 2013-03-07 11:29:30  no

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