r - How to correctly `dput` a fitted linear model (by `lm`) to an ASCII file and recreate it later?

Question

I want to persist a lm object to a file and reload it into another program. I know I can do this by writing/reading a binary file via saveRDS/readRDS, but I'd like to have an ASCII file instead of a binary file. At a more general level, I'd like to know why my idioms for reading in dput output in general is not behaving as I'd expect.

Below are examples of making a simple fit, and successful and unsuccessful recreations of the model:

dat_train <- data.frame(x=1:4, z=c(1, 2.1, 2.9, 4))
fit <- lm(z ~ x, dat_train)
rm(dat_train) # Just to make sure fit is not dependent upon `dat_train existence`

dat_score <- data.frame(x=c(1.5, 3.5))

## This works (of course)
predict(fit, dat_score)
#    1    2 
# 1.52 3.48

Saving to binary file works:

## http://stackoverflow.com/questions/5118074/reusing-a-model-built-in-r
saveRDS(fit, "model.RDS")
fit2 <- readRDS("model.RDS")
predict(fit2, dat_score)
#    1    2 
# 1.52 3.48

So does this (dput it in the R session not to a file):

fit2 <- eval(dput(fit))
predict(fit2, dat_score)
#    1    2 
# 1.52 3.48

But if I persist file to disk, I cannot figure out how to get back into normal shape:

dput(fit, file = "model.R")
fit3 <- source("model.R")$value

# Error in is.data.frame(data): object 'dat_train' not found

predict(fit3, dat_score)
# Error in predict(fit3, dat_score): object 'fit3' not found

Trying to be explicit with the eval does not work either:

## http://stackoverflow.com/questions/9068397/import-text-file-as-single-character-string
dput(fit, file="model.R")
fit4 <- eval(parse(text=paste(readLines("model.R"), collapse=" ")))

# Error in is.data.frame(data): object 'dat_train' not found

predict(fit4, dat_score)
# Error in predict(fit4, dat_score): object 'fit4' not found

In both cases above, I expect fit3 and fit4 to both work, but they don't recompile into a lm object that I can use with predict().

Can anyone advise me on how I can persist a model to a file with a structure(...) ASCII-like structure, and then re-read it back in as a lm object I can use in predict()? And why my current methods are not working?

Answer 1

Step 1:

You need to control de-parsing options:

dput(fit, control = c("quoteExpressions", "showAttributes"), file = "model.R") 

You can read more on all possible options in ?.deparseOpts.

---

The "quoteExpressions" wraps all calls / expressions / languages with quote, so that they are not evaluated when you later re-parse it. Note:

• source is doing parsing;

• call field in your fitted "lm" object is a call:

  fit$call
  # lm(formula = z ~ x, data = dat_train)
  

So, without "quoteExpressions", R will try to evaluate lm call during parsing. And if we evaluate it, it is fitting a linear model, and R will aim to find dat_train, which will not exist in your new R session.

---

The "showAttributes" is another mandatory option, as "lm" object has class attributes. You certainly don't want to discard all class attributes and only export a plain "list" object, right? Moreover, many elements in a "lm" object, like model (the model frame), qr (the compact QR matrix) and terms (terms info), etc all have attributes. You want to keep them all.

---

If you don't set control, the default setting with:

control = c("keepNA", "keepInteger", "showAttributes")

will be used. As you can see, there is no "quoteExpressions", so you will get into trouble.

You can also specify "keepInteger" and "keepNA", but I don't see the need for "lm" object.

------

Step 2:

The above step will get source working correctly. You can recover your model:

fit1 <- source("model.R")$value

However, it is not yet ready for generic functions like summary and predict to work. Why?

The critical issue is the terms object in fit1 is not really a "terms" object, but only a formula (it is even not a formula, but only a "language" object without "formula" class!). Just compare fit$terms and fit1$terms, and you will see the difference. Don't be surprised; we've set "quoteExpressions" earlier. While that is definitely helpful to prevent evaluation of call, it has side-effect for terms. So we need to reconstruct terms as best as we can.

Fortunately, it is sufficient to do:

fit1$terms <- terms.formula(fit1$terms)

Though this still does not recover all information in fit$terms (like variable classes are missing), it is readily a valid "terms" object.

Why is a "terms" object critical? Because all generic functions rely on it. You may not need to know more on this, as it is really technical, so I will stop here.

Once this is done, we can successfully use predict (and summary, too):

predict(fit1)  ## no `newdata` given, using model frame `fit1$model`
#   1    2    3    4 
#1.03 2.01 2.99 3.97 

predict(fit1, dat_score)  ## with `newdata`
#   1    2 
#1.52 3.48 

-------

Conclusion remark:

Although I have shown you how to get things work, I don't really recommend you doing this in general. An "lm" object will be pretty large when you fit a model to a large dataset, for example, residuals, fitted.values are long vectors, and qr and model are huge matrices / data frames. So think about this.