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/

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.

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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.

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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.

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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.

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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 

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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.