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

This is an important update!

As mentioned in the previous answer, the most challenging bit is to recover $terms as best as we can. The suggested method using terms.formula works for OP's example, but not for the following with bs() and poly():

dat <- data.frame(x1 = runif(20), x2 = runif(20), x3 = runif(20), y = rnorm(20))
library(splines)
fit <- lm(y ~ bs(x1, df = 3) + poly(x2, degree = 3) + x3, data = dat)
rm(dat)

If we follow the previous answer:

dput(fit, control = c("quoteExpressions", "showAttributes"), file = "model.R") 
fit1 <- source("model.R")$value
fit1$terms <- terms.formula(fit1$terms)

We will see that summary.lm and anova.lm work correctly, but not predict.lm:

predict(fit1, newdata = data.frame(x1 = 0.5, x2 = 0.5, x3 = 0.5))

Error in bs(x1, df = 3) : could not find function "bs"

This is because ".Environment" attribute of $terms is missing. We need

environment(fit1$terms) <- .GlobalEnv

Now run above predict again we see a different error:

Error in poly(x2, degree = 3) :

'degree' must be less than number of unique points

This is because we are missing "predvars" attributes for safe / correct prediction of bs() and poly().

A remedy is that we need to dput such special attribute additionally:

dput(attr(fit$terms, "predvars"), control = "quoteExpressions", file = "predvars.R")

then read and add it

attr(fit1$terms, "predvars") <- source("predvars.R")$value

Now running predict works correctly.

Note that "dataClass" attribute of $terms is also missing, but this does not seem to cause any problem for any generic functions.

Answer 2

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.

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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.